Articles about Technology
Once you’ve decided to move to an API-first architecture model, it’s tempting to jump right in building APIs, selecting an API gateway service, and standing up an API marketplace. That all takes work and delivers progress — up to a point. But, as some companies find out too late, you can pour months into standing up a state-of-the-art composability framework without really getting to ROI. You could even be increasing your tech debt. To start solving real business problems, you’ll need more than fingers on the keyboard. You’ll need to start with an API ecosystem in mind. Envisioning an API ecosystem within your organization Your API gateway is middleware, not a complete solution. It’s designed to streamline microservices delivery, running in the background to make work faster, easier, and more secure for API developers. Adding an API marketplace can help, by providing more of a storefront approach to sourcing those connections. But you can’t just build APIs and hope the right people come find them. You’ll need to go beyond the technology layer and design an API ecosystem to derive strategic value from those tools. To ensure API gateway or API marketplace platforms reach their full potential, successful organizations put strategy first. As you think about designing a future state API ecosystem to work toward, consider: What business needs your internal and external customers (API and application developers) are trying to solve How your current business processes may be helping — or hindering — those objectives Where you might find opportunities to streamline, remove friction, and accelerate progress Shifting the technology mindset from product delivery to customer experience can dramatically elevate ROI from day one by changing the way you prioritize, design, and implement each component of your solution. Your API gateway and API marketplace are utilities; your API ecosystem is how your business culture uses those tools. Driving value to your API marketplace Moving from a customer experience mindset to effecting real change and boosting the ROI of your API marketplace comes down to processes. A full process audit can help you spot opportunities for growth. First, look for processes that currently rely on APIs or on systems ripe for modernization: Which processes currently rely on APIs? Which rely on aging or end-of-life legacy systems? How might the API marketplace support more rapid composability to streamline modernization of those systems? Next, consider your current development processes: How do our developers publish APIs today? Is the API gateway frictionless, or do developers have to think about it every time? How do others find and use published APIs? If others find the API, is it easy to understand how to use it? Is the API marketplace truly self-service, or do developers waste time hunting details to implement the component? How do we manage the API lifecycle to maximize the value of our API investments? Then, consider how to use your API ecosystem strategy to level up: Reduce friction for API providers and consumers Package API instructions and information in an easily accessible way Make it easy for users to search for and implement the right API or component for their needs Manage your API lifecycle from creation to retirement Whether your business is growing or you’re already a global enterprise, once you get your API gateway and marketplace tools implemented and automated in a way that makes sense for your users and the business problems they solve, you’ll start to see results. The turn-key ability to source, consume, and change APIs makes your API marketplace nearly infinitely scalable, and flexible to evolve along with your business. Finding the right partner For organizations with significant legacy architecture, change comes at a considerable cost. You can’t afford to miss the productivity and efficiency gains that an optimized API marketplace delivers. Whether you need help imagining an API ecosystem or bringing it to life, selecting the right API marketplace or building a custom API gateway, the right partner can bring experience and expertise to get you through the roadblocks. Fusion Alliance specializes in full-spectrum digital transformations — helping organizations around the world find the right technology to fit their business needs and evolve their processes to maximize the value of their technology investments. Learn more about our approach to building a composable enterprise >>
This panel was moderated by John Dages, Technology Solution Director at Fusion Alliance (left) and features Ryan Shondell, Executive Director of Data Services at OCLC (center) and Jeremy King Chief Enterprise Architect at NetJets (right). Good technology executives know that good directives must be followed up with action plans that create value. So how do you get the business on board with an API-first strategy? How do you enhance API adoption at the enterprise level? Solve an actual problem. Think of a legitimate felt need within a service or a function in your span of control and use microservices, headless, cloud-native, or composable functions to handle it. Buzzwords don’t drive API adoption. Follow-through does. Show the business how an API-first strategy makes their job easier. The average business user doesn’t care about fancy architecture. They don’t care if you use microservices, make something headless, or are running a composable enterprise unless it makes a difference in their day-to-day. They want the website to be faster or to have more capabilities so they can drive their own KPIs. So how do you drive API adoption when your business users are thoroughly tech agnostic? You build change management into your roadmap and win hearts and minds with results. When it comes to getting the business on board for API-first initiatives, the only way to build trust is demonstrable progress. About our panelists: Ryan Shondell is currently the Executive Director of Data Services at OCLC, responsible for developing and executing the company’s data strategy and aligned technology. This includes technical product management, data operations, data quality, and development of AI/ML capabilities, analytics, search, and all customer-facing data applications and APIs across a staff of 300. Prior to joining OCLC, Ryan held multiple senior engineering leadership positions at VMware going back to 2010, most recently as Senior Director of Engineering, where he helped to lead global development on products like Skyline and VMware Cloud. And now, he’s actually headed to Path Robotics to start his next adventure. Jeremy King has been working in Technology for over 20 years and is currently the Chief Enterprise Architect at NetJets. He started his career designing and developing embedded systems and has worked in many industries including banking, health care, travel and transportation, and integration tools. His background includes distributed cloud-native architecture, data structures and modeling, enterprise integration patterns, event-driven architectures, and API design. As a Software Architect, Jeremy has faced the challenge of making disparate systems exchange data in consistent, performant ways. His current passions include technical innovation, graph databases, and emerging API standards.
This panel was moderated by John Dages, Technology Solution Director at Fusion Alliance (left) and features Ryan Shondell, Executive Director of Data Services at OCLC (center) and Jeremy King Chief Enterprise Architect at NetJets (right). The decision to build or buy API gateway tools for your organization is rarely black and white. Building an API gateway pulls your team away from other opportunities, but buying could lead to vendor lock-in. Off-the-shelf solutions might not give you differentiating advantage, but inventing your own protocols could accelerate your tech debt. How do enterprise businesses weigh the trade-offs? When to consider building an API gateway tool The custom tool will deliver differentiated value directly to customers or enable the business to deliver that value The custom tool solves a consumer or business problem that delivers market value The custom tool gives you a competitive edge in functionality, cost, or speed to market When to consider buying an API gateway tool Your IT time and talent is needed on other revenue-driving projects Your team could use an out-of-the-box tool as a platform, and build your custom functionality on top of it as an accelerator The tool conveys significant ongoing maintenance and support savings Know which shark is closest to the boat It’s not always simple to project future savings or quantify possibility. If you’re faced with a build or buy tech decision, sometimes you have to solve for the biggest issue at hand. That could mean you buy an API gateway tool or another off-the-shelf solution. Yes, it’s a vendor dependency, but every other part of your business has them, too. If you make a forward choice, and keep your eyes open, you can avoid many of the pitfalls associated with vendor lock-in. For example, finding components that are portable, avoiding proprietary pieces, and limiting the specialized components you buy outright can help. And it’s always wise to create a backup plan. As they say, “don’t have such an open architecture that your business falls out.” About our panelists: Ryan Shondell is currently the Executive Director of Data Services at OCLC, responsible for developing and executing the company’s data strategy and aligned technology. This includes technical product management, data operations, data quality, and development of AI/ML capabilities, analytics, search, and all customer-facing data applications and APIs across a staff of 300. Prior to joining OCLC, Ryan held multiple senior engineering leadership positions at VMware going back to 2010, most recently as Senior Director of Engineering, where he helped to lead global development on products like Skyline and VMware Cloud. And now, he’s actually headed to Path Robotics to start his next adventure. Jeremy King has been working in Technology for over 20 years and is currently the Chief Enterprise Architect at NetJets. He started his career designing and developing embedded systems and has worked in many industries, including banking, health care, travel and transportation, and integration tools. His background includes distributed cloud-native architecture, data structures and modeling, enterprise integration patterns, event-driven architectures, and API design. As a Software Architect, Jeremy has faced the challenge of making disparate systems exchange data in consistent, performant ways. His current passions include technical innovation, graph databases, and emerging API standards.
This panel was moderated by John Dages, Technology Solution Director at Fusion Alliance (left) and features Ryan Shondell, Executive Director of Data Services at OCLC (center) and Jeremy King Chief Enterprise Architect at NetJets (right). How many meetings do you have with Amazon when you want to use S3 to move a workflow into the cloud? None, of course. Amazon makes its S3 service easy to find, understand, and consume. And how many meetings do you have to have when you add an API to your own organization’s technology ecosystem? The “how many meetings” test isn’t a trick question. It’s a good rule-of-thumb metric to judge whether your service is driving value or contributing to tech debt. Bottom line up front: APIs must be consumable to add value. If you’re building an API and you have to have a meeting before someone can use it, something has gone wrong and you may need to reconsider your composability approach. If you build an entire composable ecosystem that works the same way the world used to work 20 years ago, you’re cruising for tech debt rather than ROI. A functional composable enterprise requires components that are discoverable, with documented constraints and functionality, so that operators can find what they need and put it into place without a lot of hand holding. This discoverability could come from traditional documentation, or effective use of introspection endpoints to allow for programmatic, systemic discoverability. Either way, to reduce your tech debt and boost the ROI of your composability, be sure to ask yourself: how many meetings did we have before we could offer or consume this product? The closer that number is to zero, the better. About our panelists: Ryan Shondell is currently the Executive Director of Data Services at OCLC, responsible for developing and executing the company’s data strategy and aligned technology. This includes technical product management, data operations, data quality, and development of AI/ML capabilities, analytics, search, and all customer-facing data applications and APIs across a staff of 300. Prior to joining OCLC, Ryan held multiple senior engineering leadership positions at VMware going back to 2010, most recently as Senior Director of Engineering, where he helped to lead global development on products like Skyline and VMware Cloud. And now, he’s actually headed to Path Robotics to start his next adventure. Jeremy King has been working in Technology for over 20 years and is currently the Chief Enterprise Architect at NetJets. He started his career designing and developing embedded systems and has worked in many industries, including banking, health care, travel and transportation, and integration tools. His background includes distributed cloud-native architecture, data structures and modeling, enterprise integration patterns, event-driven architectures, and API design. As a Software Architect, Jeremy has faced the challenge of making disparate systems exchange data in consistent, performant ways. His current passions include technical innovation, graph databases, and emerging API standards.
When it comes to understanding how wearables are changing healthcare, consumer brands serve as a solid leading indicator. Popularized by brands like Apple Watch, FitBit, and Garmin, the global wearable healthcare market was estimated at $16.2 billion in 2021, and is projected to double in the next five years. Healthcare wearables in daily life Although most users rely on healthcare wearables to check texts during spin class or crush their friends’ daily step records, an increasing number of users rely on smartwatches and other medical wearables for life-saving medical information. As the technology evolves, healthcare wearables can now give minute-by-minute EKG readings, monitor blood sugar, check oxygenation levels, and help people use real-time data to manage their health while they go about their regular activities. Wearables also deliver oversight and peace of mind to caregivers, as when diabetic children wear devices that monitor insulin and food intake and link to mobile apps monitored by their parents. These breakthroughs allow patients of all ages more autonomy while providing reassurance to caregivers that the person is safe. Learn more about what wearable devices make possible >> Healthcare wearables in long-term care settings Long-term care presents a gap between that at-home monitoring scenario and the tech-saturated acute care space of a hospital or clinic. Historically understaffed, nursing homes and long-term care facilities struggle with high turnover, increasing rates of preventable errors, and unnecessary escalation of avoidable medical events. In addition to the impact on the patient and their loved ones, these realities impact the facility itself through lower reimbursement rates and increased cost of care. A 2021 American Healthcare Association and the National Center for Assisted Living survey on staffing in these facilities showed that 99% of nursing homes and 96% of assisted-living facilities face a staffing shortage. Harvard University professor David Grabowski says the pandemic only worsened that already critical situation. He notes, “We’ve overlooked and undervalued this workforce for a long time and now we’re at a full-blown crisis…We’re in a crisis on top of a crisis.” Ensuring the right level of care for high-risk and elderly patients amidst staffing constraints formed a critical use case for transformation. Healthcare wearables emerged as a leading option that would give staff the ability to monitor more patients, get notifications when care is needed, and escalate when necessary. Overcoming obstacles to adoption Implementing a program for wearable devices in nursing homes introduced more stringent requirements than consumer wearables, including: Privacy protection: Patient medical information, covered under HIPAA, requires more protection than off-the-shelf iOS and Android systems offer. Usability concerns: Patients under care in nursing homes and long-term care facilities often lack experience with technology, and/or the dexterity to manage new devices. Cost considerations: In addition to the cost of patient wearables and devices allowing nursing staff to monitor and communicate alerts, facilities must also invest in secure data infrastructure and information architecture beyond standard integrations in market-ready smartwatches. Creating a targeted solution Realizing that nursing home and long-term care facilities faced unique barriers to implementing wearable devices, BioLink Systems set out to create a solution. Initially, the company devised a device that could be attached to an adult brief to monitor urination levels and body position. However, early issues with the prototype limited production scalability. Fusion worked with BioLink to architect a cloud-based IoT solution that uses machine learning to exceed the company’s initial vision. Designed with a minimalist aesthetic and user experience to fit the target demographic, the BioLink bracelet and adult brief wearables: Meet HIPAA requirements Monitor patient fluids Track patient vital signs Alert nursing staff when patient vitals fall outside their customized range Escalate alerts if patients are not attended within an allotted timeframe Initial testing and rollouts in nursing homes delivered immediate results, including: Improved patient care Decreased response times Fewer avoidable events such as medication errors Decreased escalation of care level, including hospitalizations Improved oversight Increased compliance with state, federal, and agency regulations Better experiences for the patient and their loved ones Learn more about how BioLink’s wearables are changing healthcare >> What’s next for wearable healthcare devices As facilities gather more data from using these devices, the machine learning algorithm BioLink and Fusion designed will continue to refine unique vital sign ranges for each patient, resulting in more targeted care. Future iterations of the BioLink device will integrate that information with the patient’s electronic medical record, enabling further customization of care. While each device starts with a baseline for normal with each of these vital signs, the more data that is collected, the better the facility can care for the patient. For example, if a patient’s oxygen level is continuously high, the device eventually creates a new threshold for that patient’s vitals and only sends notifications accordingly. Especially with the elderly population, there are many people that can’t communicate what they need or when they feel a certain way. There are endless possibilities for being able to provide better care under these circumstances. With options like dehydration sensors, nursing care staff is better able to not only bring water to patients but ensure that they are actually consuming it. The more variables, the better Ultimately, the more variables, the better the information — resulting in better care and better outcomes. The correlation and combination of all the data from a patient can detect changes and allow for more timely, preventative care. And the more information included, the better the insights from the algorithm. With the right information, staff can prevent different medical events by predicting problems and eventually creating better remedies and treatments to avoid costly medical interventions or catastrophic incidents. As industry leaders and healthcare facilities see the impact of devices like BioLink’s bracelets, we expect to see greater adoption of healthcare wearables to elevate patient care, reduce facility costs, and find operational efficiencies even during times of staffing crises. With the right technology and innovation, we can change outcomes and save lives — with a wristband.
From online transactions to mobile payment apps like Venmo, today's consumers increasingly look for digital access to funds — and they expect a seamless experience. As customer expectations and the economy continue to evolve, digital transformation in finance and banking needs to keep pace. Banking culture hasn’t always kept up with what digital customers are actually looking for. The World Banking Report 2021 reported that, “Despite being vocal about improving the customer experience, the banking industry’s delivery of the key components of a strong customer experience, such as improving transparency and social responsibility, improving customer support, and reducing the cost of services, falls far short of customer expectations.” Three common barriers to digital transformation in banking There are several common challenges to digital transformation that keep banks from pivoting quickly to meet customer expectations. Roadblock #1: Technical debt As a highly regulated industry, traditional banking relies on complex and siloed legacy technologies that are often expensive to maintain. Over time, technical investments compound, making it increasingly difficult to find time or resources to shift to more modern or scalable platforms. When banks grow through mergers and acquisitions, attempting to integrate additional legacy systems adds to that technical debt. At the same time, banks face increasing competition from the fintech sector — online-first financial institutions that aren’t encumbered by aging platforms. Traditional banks saddled with technical debt may feel that they lack the time or resources to fully integrate, modernize, or replace their legacy technologies. But the longer this debt persists, the harder it is to compete with digital natives, leaving banks less agile in the marketplace. How platform modernization helped make an annuity organization more competitive >> Roadblock #2: Organization size Like many enterprise-level organizations, larger banks often create internal digital teams that combine business, IT, and marketing capabilities and develop expertise in their own technologies, systems, and processes. Faced with competing internal priorities and hampered by regulatory constraints, these internal teams may struggle to get alignment and prioritization for a banking digital transformation strategy and may lack the breadth of expertise necessary to implement a comprehensive modernization effort. Smaller banks, on the other hand, may be more nimble and successful at shifting internal priorities, but they may not have the resources to staff dedicated teams. While organization size is often called out as a hindrance to effective digital transformation in banking, the underlying problem may not actually be a headcount issue. Regardless of size or industry, most companies miss their digital transformation goals due to lack of clarity and strategy. “Digital transformation” in finance or any sector can be hard to define, implement, and measure. A more strategic approach starts with identifying concrete problems or issues, understanding customer needs, and developing solutions that bridge the gap with action steps that are clear, dynamic, and measurable. How technology strategy comes to life >> Roadblock #3: Relying on assumptions about customer needs and wants Understanding customers’ needs, pain points, and experiences can be difficult, and as users adapt to technology their preferences continue to change. This makes audience research even more critical when defining your bank’s digital transformation strategy. After a surge in remote work due to Covid, comfort levels with technology are at an all-time high. Research from McKinsey found that 75% of people using digital channels for the first time during the pandemic indicate that they will continue to use them when things return to “normal.” Not only are customers more comfortable with banking technology, but it has also become an important factor in choosing which bank to use. According to Mobiquity’s 2021 digital banking report, 40% of respondents agreed that they are likely to switch accounts to get better digital tools. Investing in both qualitative and quantitative data can dispel assumptions about your audience, while also revealing specific ways to improve the customer experience. As those opportunities are identified, banks can prioritize technology and services that will have the biggest impact. What goes into a successful customer experience strategy >> How to approach a digital transformation strategy in banking Given these challenges and the continuous evolution of customer expectations, several technologies offer significant potential gains and can help financial institutions stay competitive. Mobile app enhancements Mobile banking apps typically offer the ability to check balances, transfer funds, pay bills, and chat online with a bank representative. By building applications that go beyond these basic services, banks can increase their new customer base while improving customer retention and lifetime value. Leaders in the banking space now include peer-to-peer payments, lending inquiries, and chatbots as part of their applications. However, in addition to monitoring what competitors are doing, it’s important to implement a robust discovery process to see what the target audience wants from a banking app. This could include developing target personas and performing pain point analysis to find unique solutions and services that will better appeal to customers’ needs. From there, financial institutions are better poised to tackle the next layer of technology for the app space — personalization. Many banks are investing in personal financial management tools and customized product offerings in their apps, making banking more accessible and valuable than before. These user-friendly applications and their customization capabilities are an integral part of digital transformation in banking. Refine your mobile applications and provide a better customer experience >> Machine Learning Historically, machine learning engagements have required substantial data science and model training investments. But major ML platforms have evolved, lowering the barrier of entry for these projects. Now, midsize and even smaller banks can use machine learning models to better understand their customers and drive a more personalized experience. And machine learning isn’t just valuable for deepening current relationships; it can also help banks target and acquire new business by identifying trends and opportunities. This means higher quality leads, improved retention, and an increase in business with more potential for high lifetime value. [On-Demand] Reimagining customer insights, risks, & relationships through machine learning >> Data management strategy Traditional lending institutions underwrite loans by using a system of credit reporting. Banks that process loan applications evaluate the risk by looking at credit scores, homeownership status, and debt-to-income ratios. Today, three major credit bureaus provide this information. But these reports can sometimes contain erroneous information, and the information comes at a high cost since it can only be found in three places. And while banks often collect their own internal data, if that data is incomplete or disorganized it cannot offer useful insight. With structured data management strategies, financial institutions can mitigate losses by generating more data and using it to recognize trends and potential liabilities. See how one bank improved ROI by 1054% through strategic data management >> Robotic Process Automation (RPA) Some banking processes are still highly manual. Consider routine tasks like opening an account or reporting a stolen credit card — it takes time to get through the questions, and usually requires a phone call from the customer. With robotic process automation (RPA), in the case of a stolen credit card, the workflow process can automatically cancel the old card, issue a new card, and confirm the mailing address of the new card. RPA can also identify bots or theft with greater accuracy than a human analyst. RPA even has the potential to assist with workload transformation. In addition to streamlining and automating internal processes, RPA can be used to manage the cloud technologies that institutions rely on for their everyday tasks. This leads to more refined workload placement — and therefore a more productive workforce. The bottom line on digital transformation in banking From highly-personalized service offerings to easy-to-use applications, consumer expectations are high in the banking sphere. To keep up with these expectations, banks must position themselves to adapt quickly. Traditional banks are often at a disadvantage to digital-only competitors. Newcomers operate without the burden of legacy systems and outdated business models. But a digital-first attitude can help financial companies effectively implement technologies that enable digital transformation in banking. Find out how one financial services firm successfully handled digital transformation >> Ready to boost your productivity and customer engagement? Let us know your questions and find out how a strategic approach to digital transformation can help your bank thrive in a digital-first world.
A version of this article was originally published in Forbes. Theoretically, a case could be made for running your workloads, data, and applications entirely from on-prem servers. But realistically? That use case is vanishingly small. Nearly all companies can benefit from the cloud. And most organizations are well aware of the benefits cloud modernization confers. You’ve heard the touts: Cloud migration leads to faster run times, greater efficiency, and — here are the magic words — significant cost savings. As with any major business move, return on investment drives a substantial number of cloud decisions. But when you make those calls without a strategy specifically tailored to cloud environments, the risks add up. You wouldn’t relocate your corporate headquarters without significant strategic reasoning. Relocating your technical real estate ought to trigger the same level of analysis and corporate soul searching. If you aren’t seeing the ROI you expected from your cloud investment, you may need to consider a more strategic approach. Why you’re not seeing cloud ROI…and what you can do about it Companies have technology problems: aging servers, feral architectures, legacy applications, redundant workloads, wild west dev shops…the list goes on. Faced with a mess on the premises and a cloud mandate from leadership, it’s easy to understand why one of the most common strategies for cloud migration is lift-and-shift. All too often, though, a one-for-one move to the cloud is used as a shortcut to avoid having to create a full-blown cloud strategy and roadmap. Here’s how to avoid common ROI pitfalls. ROI pitfall: Unexpected costs Moving to the cloud can be a significant cost-saving strategy for your information technology budget, but it can also have the opposite effect. Without taking the time up-front to tune workloads and assess usage, you could wind up paying more for cloud storage/use than you used to spend for an on-prem solution. Understanding cloud pricing models, including getting clarity on all the variables that affect pricing in the cloud, couldn’t be more important — it isn’t an apples-to-apples comparison to on-prem. Cloud costs are directly related to resources consumed, so lifting and shifting without a strong strategic foundation can cannibalize your savings. To save more in the cloud environment, consider: Resizing: Do all of your workloads run at capacity all the time? Or are they often over-provisioned? Would a lower consumption rate with surge capacity work better for your needs? Retiring: Are any of your applications or workloads redundant? Lift-and-shift migrations can shed light on previously siloed tasks that could be streamlined to reduce cloud consumption. Replacing: Would a different solution make more sense? A cloud migration offers the opportunity to rethink your legacy applications and combine or replace some of your workloads and applications. ROI pitfall: Ungoverned actions Your teams may be used to a high degree of autonomy, with different business units responsible for their own tech and data usage. Once you move to the cloud, however, that shadow IT culture can cost you. If users deploy new resources and enable additional capabilities to the cloud at their usual rate, your spending can escalate quickly. To avoid painful consumption spending, here’s how a fresh look at policies and procedures can help: Rethinking: This is a good time to standardize processes around development, quality, testing and change management. Recommitting: A cloud environment makes governance more important for your organization than ever. Take the opportunity to recommit to strong governance standards across your organization—and be sure you have change management plans in place to support the shift. ROI pitfall: Unanticipated fluctuations Even the best cloud strategies can’t always anticipate the variability of cloud usage. Costs and efficiencies of different cloud platforms might vary month to month or even week to week. Some companies try to account for the volatility by building each of their workloads four or more different ways so they can quickly shift from one cloud provider to another. Others get fed up and go all in with one platform. Neither option is cost-effective. To make the most of cloud price fluctuations, consider new ways of working, like: Replatforming: Are your legacy systems still serving you well? Could the costs of a hardware or software upgrade be balanced out by greater efficiency or better cloud usage over time? Refactoring: One way to manage cloud environments is containerization. As I wrote in my previous Forbes article, “Once your workloads are containerized, adding automation can save you even more by establishing, testing, and refining models for workload placement without relying on time-consuming manual decisions.” Google Cloud Platform, AWS, and Azure all have forecasting tools you can use to further refine your automation models. Rehosting: Containerized workloads enable on-the-spot decisions about where to run a task. Realizing savings then becomes as easy as a drag and drop to the cheapest or most efficient cloud or on-prem location. Rearchitecting: Could developing cloud-native applications serve the same needs but in more efficient, modern, or cost-effective ways? Why you should start with the end in mind While each of these tactics has value, the best way to avoid surprises and see ROI for your cloud investment faster is to start with a strategy. A cloud mandate without a cloud strategy is a recipe for cloud disappointment Up-front strategic planning can save you significant rework and help you avoid costly mistakes with your cloud migration. To maximize your cloud ROI, key topics to include in your strategy are: Evaluating your current architecture, technology, or processes: What’s working and what could be improved? Identifying redundancy and opportunities for improvement: What could be streamlined in your code, workloads, or data? Articulating fluctuations in your use patterns: How are your workloads prioritized? Could your data and storage needs be tiered? What might optimal provisioning look like for your organization? When it comes to cloud ROI, grounding your operations and tactics in strategy is non-negotiable. The more intentional your organization is about its cloud deployment, the more quickly your ROI will outpace expectations.
In this case, we're not looking for plot twists. In the gripping mystery thriller film The Invisible Guest, a wealthy tech entrepreneur finds himself entangled in a murder case and hires a top lawyer to build his defense. As the entrepreneur’s story unwinds, the audience is kept guessing until the final identity is revealed, which, we must confess, we did not see coming. Hopefully the same could not be said for your organization’s technology platforms. As more and more data and applications move to the cloud, and employees increasingly expect seamless BYOD experiences, companies are looking for identity and access management solutions that balance flexibility with robust security. Whether you’re looking for a new tool, need to integrate or authenticate across new data streams, or create complex multi-tenancy and role-based access rules, we can help. Our technology and data teams can help you identify and implement the right solutions to keep identity records and access rules secure and seamless – so you can keep your business moving forward. Get smart. No shade intended for superheroes and middle-aged men flying jets, but if you’re looking for a gripping mystery thriller this weekend, we’d recommend The Invisible Guest. It’s currently streaming on Netflix with English subtitles. That’s right, you get culture points and suspense. And if you’ve been wondering about simulation theory and hoping to find a sci-fi outlet to help you think it through, Sea of Tranquility turned out to be better than When We Cease to Understand the World, in our opinion. If, after getting all turned around by cinematic and literary plot twists, you want to explore tech solutions, let us know. We can’t deliver time travel, fix the lighting on your moon colony, or help you prep for a Spanish deposition, but real-world identity and access management solutions are right up our alley.
As the pace of technological change continues to increase, digital transformation in healthcare often struggles to keep up. Challenges like integrating aging legacy systems, maintaining patient privacy, and leveraging disparate data sources into actionable insights loom large in healthcare, where time and resources are often at a premium. But the same circumstances that make digital transformation in healthcare more difficult are the very things that underline its importance. When patient lives are on the line, digital transformation isn’t just a “nice to have.” Healthcare systems that achieve their digital transformation goals see immediate improvements in patient experience, quality of care, and patient outcomes. From that standpoint, digital transformation in healthcare isn’t just about adding technology, it’s about revolutionizing the processes and systems that drive the health and well-being of the population as a whole. Case study: Life-saving technology in diabetes long-term care >> Putting patients first While individual healthcare providers commonly put their patients’ needs front and center, the system as a whole did not evolve with that mentality. Due to a variety of factors, including payer systems, consolidation, and the regulatory environment, healthcare systems got a reputation for siloed information, duplicate workflows, lack of clarity, and confusion. As healthcare organizations seek to modernize, smart health systems are taking a consumer-centric approach — redesigning patient experiences and pathways while improving care delivery and outcomes using digital technology. Article: Transforming customer engagement in the digital age >> Planning the future of digital transformation in healthcare During the pandemic, industries accelerated digital transformation efforts across the board, and healthcare was no exception. Out of necessity, more medical touchpoints and interactions moved online, from virtual office visits to automated triage to digital paperwork. Now, two years into the new normal, healthcare organizations are taking stock of their progress, appreciating the speed and scale of their efforts, and mapping opportunities for the future. A recent Deloitte study found that 60% of health systems say they are about halfway through their digital transformation journey. In our experience, working with technology innovators and leaders across industries is where things can get messy. Digital transformation is a long game, and organizations often get bogged down at the halfway mark. To keep moving forward and avoid costly wrong turns, healthcare leaders need a fresh vision and renewed roadmap. Evolving digital transformation in healthcare to meet the changing expectations of patients and providers requires a commitment to a digital-first, people-centric approach, but offers great opportunities for continued growth in connection, innovation, and successful outcomes. Based on our experience, we see five key areas where focused efforts can deliver outsized returns for healthcare systems that are mid-way through their digital transformations: 1. Modernize legacy systems to give providers and patients more options While the vast majority of individual healthcare providers and healthcare organizations use an electronic health records (EHR) system, relatively few seamlessly integrate with patient portals. A recent PEW Health Information Technology (HIT) survey found that almost 80% of respondents wanted to access and view their electronic health records through a website, an online portal, a mobile app, or electronically in some other way. Moreover, the same survey highlights a strong desire for their doctors to share information about the patient’s health status. For most healthcare organizations, integrating patient records across practices and within portals is a headache at best. Adding in the other digital interactions that today’s consumers expect — such as automated appointment and prescription workflows, chatbots, pre-filled forms, and instant answers — might seem impossible. Delivering a better patient experience and giving providers greater flexibility with their tools often takes a more strategic view. Rather than layering in more and more technology solutions, smart healthcare organizations take a holistic approach to modernization, creating flexible, modular solutions that give patients and providers more options in the near term while also making future enhancements easier. Case Study: How an AI healthcare company optimized its digital experience >> Article: Modernization challenges and the path forward >> 2. Mitigate risk to build patient trust In addition to technology lag, healthcare systems also struggle to connect patient health information due to regulatory constraints. To maintain HIPAA compliance in the US and GDPR compliance for EU patients, healthcare organizations sometimes limit the very information sharing that would result in higher quality care. To meet patient expectations of data privacy and personal health data security while also delivering on modern expectations for functionality and connectivity, health organizations need to build in best practices for security and governance throughout their technology architecture. While there are myriad ways to approach this issue, a couple of key options deserve consideration: BYOD Policies A 2019 study found that 63% of healthcare organizations sustained a security incident related to unmanaged and IoT devices. Given the rapid acceleration of digital transformation in healthcare since 2020, we suspect that number is much higher today. As healthcare organizations modernize systems and integrate more virtual and IoT solutions into their technology spaces, having a robust and updated BYOD policy becomes more important. Developing a compliant, enforceable strategy is a critical step in your modernization efforts. Case study: Navigating BYOD in a highly regulated industry >> Containerization One way to mitigate risk is to containerize data, workflows, and applications in the cloud. Although the cloud can sometimes get a bad rap for security, a carefully designed strategy puts security first and can prevent any breach from spilling over too far into other parts of your architecture. Article: Maintaining a composable enterprise >> Blockchain Best known in the context of cryptocurrency, blockchain uses a computerized database of transactions to allow secure information exchange without the need of a third party. Applying blockchain technology to the healthcare industry could improve information security management; healthcare data can be communicated and analyzed while preserving privacy and security. Countries like Australia and the UK have started experimenting with blockchain technology to manage medical records and transactions among patients, healthcare providers, and insurance companies. In both examples, decentralized networks of computers handle the blockchain and simultaneously register every transaction to detect conflicting information, keeping records accurate and making them more difficult to hack. Article: Building trust in your data privacy compliance >> 3. Use voice and wearables to enhance patient experience and outcomes Wearable devices and IoT-based health sensors can track a patient’s conditions and activities remotely, from their vital signs and hydration to the onset of a medical crisis event. The data collected can be helpful to healthcare providers and enable them to better guide patient care. Healthcare providers use IoT and wearable data for remote monitoring and preventative care, providing more specific, personalized connections even with lower staff coverage. Machine learning also drives AI-based natural language processing technology in the healthcare space. As more patients become familiar with voice models like Alexa, Siri, and Google Home, healthcare organizations see potential to deploy the technology for tasks like triage and treatment reminders. For example, the UK’s NHS uses voice technology to field common questions, deliver health information, and remind patients to take medication. Case study: Using wearables to improve patient care >> 4. Put data to work for predictive and preventative care Healthcare organizations collect volumes of data but traditionally haven’t used advanced analytics to translate the information into actionable insights. Today’s leading provider systems are exploring how real-time business analytics, predictive analytics, and AI can transform patient experience and how care is delivered. In much the same way that businesses use data analysis to spot trends, forecast consumer behavior, and drive purchasing decisions, healthcare organizations can use the information they collect to understand patient expectations, discover areas of dissatisfaction or waste, and identify opportunities to enhance the overall experience of patients with their facilities. Likewise, providers can use patient data to understand how a unique individual responds to treatment, spot key diagnostic markers, and even predict potential outcomes so that doctors and patients can work together to minimize risk. Article: Data analytics in healthcare settings >> 5. Automate administrative tasks to focus on patient care The growing number of administrative tasks imposed on physicians, their practices, and, by extension, their patients adds unnecessary costs to the health care system. Excessive administrative tasks also divert time and focus away from providing actual care to patients. Tools like Robotic Process Automation (RPA) can help healthcare systems save time and resources in areas such as administration, billing, and human resources — freeing up more time for face-to-face interaction with patients. When it comes to finding the right applications for automation in healthcare, it’s important to keep patient experience at the center of your strategy. Developing a customer-first automation strategy can help create the perfect blend of automated interactions and human interactions that will meet today’s expectations and delight patients rather than frustrate them. Article: Finding the right use cases for automation >> Evolving patient care through digital transformation in healthcare As the digital tools, apps, and resources pioneered during the pandemic continue to evolve, healthcare leaders must continue to push ahead with digital-first, patient-centric investments in technology, integrations, and solutions. Finding the right balance between patient and provider expectations, maintaining compliance, and enhancing patient care requires a mindset that values the patient’s perspective. Ready to take the next step? Get a machine learning jumpstart >> Get a better view of your data analytics maturity >> Refresh your digital transformation roadmap >> Wherever you are on your digital transformation journey, our team of digital, data, and technology experts can help. Ask us your questions about digital transformation in healthcare >>
The pace of change and unpredictable circumstances of the past couple of years have led many companies to rethink their just-in-time approaches to resourcing tangible goods and materials. But why stop there? To scale and adapt fast, companies also need a new approach to how they resource skillsets. One of our clients, PRECISIONxtract, did just that. By taking a just-in-time approach to their shifting skillset needs, the company was able to scale up fast — and minimize risk — in a changing business environment. A right-fit-first approach PRECISIONxtract’s transformative healthcare market access solutions offer patients and providers unprecedented connection to the right medication and resources in clinical settings. To bring that vision to life, PRECISION could have found a series of single-skill vendors or taken the time to recruit and onboard new employees. Instead, they looked for a cross-functional partner that would be a seamless fit with their company culture and that had the right mix of scalable skills. They found that fit with Fusion Alliance. Fusion quickly became an integral part of PRECISION’s team, assembling a group of more than 20 strategy, data, and technology experts to deliver responsive support for a growing set of initiatives. Boosting surge capacity across disciplines Knowing that their flagship product, Access Genius, needed design and functionality upgrades, PRECISION called on Fusion to assess and modernize the application without disrupting the existing business. To avoid downtime and increase speed to market, our team used an Agile process and a model-driven design, in which models from the source code informed modernization efforts. Streamlining the overall architecture not only saved development time, but also made Access Genius easier to deploy to PRECISION’s clients. And, to make the product easier to maintain and cheaper to run, we applied containerization through a microservices model and moved Access Genius to a distributed cloud hosting framework. Our solution provided real-time customer insights that were delivered across a variety of digital channels, in lieu of a people-driven process. This helped take Access Genius: From a complex, cumbersome, legacy monolith into a lightning-fast, distributed, cost-effective, cloud-native solution From a user-driven, database-centric format to a distributed API-based framework, enabling immediate data updates for important cost and coverage changes From a time-intensive customer engagement portal to an intuitive, streamlined, automated process Equipped with a modern, stable, extensible platform, PRECISION was free to explore opportunities for more radical innovation. Disrupting the market with frictionless access to timely data Although Access Genius successfully broke down barriers with data, the solution’s interface required users to navigate a complex dashboard with manual clicks and drop-downs. For pharma teams with limited time to connect doctors to information, seconds count. Working with PRECISION’s product team, Fusion technology experts analyzed the friction point of manual navigation and explored ways to make Access Genius more seamless for the user. Drawing on deep expertise deploying cutting-edge technologies into highly regulated spaces, Fusion suggested exploring a shift away from a traditional web-based interface to an AI-enabled voice functionality that would connect users to the most relevant data and messaging right in the flow of conversation. Changing the way pharma enablement tools go to market At the same time, other Fusion consultants were hard at work rethinking the way PRECISION’s products reached, empowered, and retained customers. We brought in a range of specialists to bring new strategies to life, including: Instructional designers and training developers created an interactive training platform to equip pharma sales reps with greater confidence in provider interactions by deepening their understanding of the Access Genius tool. RESULT: Access Genius IQ, a new training tool that helps PRECISION customers see faster ROI for their Access Genius investment Brand experts, visual designers, content strategists, and web developers elevated visual brand elements and created websites, editorial content, and outreach campaigns. RESULT: New website architecture, design, and content; long-form lead generation content; prospect cultivation email marketing Digital marketing strategists, creative designers, and ad teams implemented innovative ad campaigns in rapid succession as PRECISION had more time to develop and roll out new products. RESULT: LinkedIn ad campaigns generating 3X leads, including 100 qualified leads in the first 90 days Read more about the success of Fusion’s marketing partnership with PRECISION >> Reimagining the skillset supply chain Partnering with Fusion gives PRECISION access to a huge team of experienced consultants with a wide range of skillsets — allowing the company to surge and scale as their business needs and market realities shift. With Fusion bringing in the right people at just the right time, PRECISION saves valuable time and resources, enabling them to be more innovative, more agile, and more impactful for their customers, healthcare providers, and patients. Ready to explore how Fusion skillsets can help your team succeed? Our ongoing work with PRECISIONxtract is just one example of how we help companies build momentum for a digital-first world. We bring big-picture thinkers, technology-minded creatives, data scientists, and technical experts to work alongside our clients, providing a force-multiplying effect that leads to scalable, future-focused solutions for the most complex challenges. Ready to get started? Let’s talk.
Every few weeks, we share insights with our Fuse subscribers along with news and trends we’re following across the web, including book recommendations. Here’s a compilation of some of our key insights from last six weeks. If you want content like this delivered directly to your inbox, we’ve got you covered. Subscribe to the Fuse here. Data is the Holy Grail In the classic film Monty Python and the Holy Grail, viewers hear King Arthur and his trusty servant Patsy approaching with a trademark “clip-clop, clip-clop” sound. When the duo emerges from the primordial mist, you see (spoiler alert) that the source of all this noise is not, as might be supposed, a horse. Rather, Patsy is banging two coconut shells together as the king trots about on his own two legs. The duo is getting from point A to point B in their quest, but not in the most efficient or effective way possible. Many companies follow that script. Equipped with buzzword mandates like process optimization and data-driven decision making, it’s all too easy to make small adjustments that sound like you’re headed in the right direction but aren’t necessarily getting you there any faster. How do you drop the coconuts and get on the horse (metaphorically speaking)? What does it look like to use data to drive optimization in real terms? We’ve got our eye on digital twins. Before you run away (how’s that for a deep cut Monty Python reference?) from yet another data buzzword, it’s worth another look at this practical application of machine learning and data analytics. Digital twins are most often used to optimize physical assets and processes like manufacturing, warehousing, and logistics. Using sensors to collect data on a product, machine, or physical process, the digital twin feeds real-time data to a machine learning algorithm to test variables and scenarios faster — ultimately leading to actionable process improvement insights. These days, we’re starting to see more businesses use digital twin frameworks to optimize and innovate non-physical business processes like accounting, HR, and marketing as well. A digital twin simulation can help you surface interdependencies and inefficiencies that might otherwise be blind spots, especially if they’re baked into your business culture as “the way we’ve always done it.” In the quest for digital transformation, don’t settle for coconuts. Instead, let’s talk about the ways your data can carry more of the weight for you. Get smart: If all this talk of Monty Python and the Holy Grail puts you in the mood for an old-school movie night, good news: it’s available on Netflix. And if you’re looking for a more literary scratch for your Middle Ages (ish) itch, we’re reading Cathedral by Ben Hopkins. It’s a fascinating look at the complex processes involved in constructing architectural marvels in the days before edge computing. We may handle optimization differently now, but human nature stays the same. Read the full Fuse: Data for April here. A horse is a horse, and other martech myths Martech is a crowded field, and a lot of the voices weighing in on your options have a horse in the race.* No one is out to skew the odds on purpose, but your organization is unique. Just because one solution is a front-runner doesn’t necessarily mean it’s a great fit for your business. So how do you sort the facts from the hype and decide where to place your bets? We rounded up a few martech myths as a starting point. Myth: One CDP is as good as another. Fact Check: Finding the right CDP (or CRM, or DMP, or any other solution you can think of) isn’t a simple box to check. And, once you make your decision, integrating and customizing your platform will also take time and attention. Myth: Everyone needs a CDP. Fact Check: Depending on your use cases, you might be able to do everything you need to do within your current tech stack. Myth: You should pick a platform and go all in. Fact Check: It probably goes without saying, but when it comes to technology there are no one-size-fits-all solutions. One product might be a great fit for your needs, but that doesn’t mean that vendor should supply your entire tech stack. Myth: Data and tech silos are just the way business works. Fact Check: Regardless of size, scope, or industry, today’s businesses can’t afford to be siloed. When you’re evaluating a tech solution or rethinking your entire customer data strategy, prioritizing integration is always a safe bet. On your mark. Ready to put your martech through the paces? Read on to find resources to help you optimize your stack and get your customer data strategy across the finish line. *Full disclosure: one of our team members won $100 when Rich Strike won the Kentucky Derby, but for the most part we are platform- and livestock-agnostic. Get Smart: We try not to be too on the nose with our book recommendations but couldn’t help ourselves this time. In Data Strategy, Bernard Marr collects a solid primer on the data landscape and how your organization can use it (legally and ethically) to advance your goals. Spoiler alert, though probably not surprising given the title, strategy turns out to be the foundational driver for effective data use. Whether you read the book, our Ultimate Guide to Customer Data Strategy, or just want to get a sense of potential next steps, we’d love to chat about customer data. Grab a time that works for you. Read the full Fuse: Marketing for May here. Three technology strategies walk into a bar If you’ve got a monolithic legacy system on your hands, sticking with the status quo isn’t a fun choice. But going nuclear and building back from scratch probably isn’t realistic. Wouldn’t it be great to find a middle ground? Meet the composable enterprise. It’s an iterative path toward digital transformation, with applications repackaged into components that can be used to build new solutions across the business. Piece by modular piece, you rebuild your technology ecosystem — becoming more efficient, effective, and scalable as you go. As the glue that holds those components together, APIs are key to building a composable business. And developing secure API solutions that accommodate shifting capacity demands and amplify your technology takes a hefty dose of strategy and expertise. That’s what we love about it! If APIs are your jam, too, or if you’re wondering if a composable system makes sense for your business, let’s talk. We’re talking APIs over IPAs in Cincinnati on June 16 and you’re invited. It’ll be fun! Get smart: You’ve probably spent the day wondering how speculative/sci-fi/literary fiction relates to API strategy and microservices (or maybe that’s just us). But, we’d guess, the same type of mind that enjoys transforming legacy monoliths into composable enterprises would also really track with a book like How High We Go in the Dark by Sequoia Nagamatsu. Modular pieces linked together by strong bonds leading to an intricate and ever-expanding whole? We’re here for it (the book and the technology strategy). Read the full Fuse: Technology for May here.
A version of this article was originally published in Forbes. Remember when your business first got into the cloud, and the whole idea seemed edgy and exciting? If you’re like most organizations, some years have passed since those early days, and you may be feeling a little bogged down with workload migration. You might not be seeing the efficiencies you imagined. Your cloud ROI might even be slipping. You can’t roll back to square one, but you can get back on track. Every journey is different, but as we’ve helped companies optimize their technology, we’ve identified five key ways to elevate your workload migration into true workload transformation. Realign for impact. Workload transformation can be a long game, but it doesn’t have to be a slog. Evaluating processes and mapping out potential efficiencies can help ease transition pain points and accelerate your time to proving results. If you haven’t taken a fresh look at your cloud roadmap lately, it may be time to regroup and make sure your workload optimization strategy is still aligned with business priorities and resources. Start with your current state. You might ask questions like: What does your current workload array include? How are different environments performing? Which technical skillsets do you have in-house? Which short- and long-term business processes are impacted by technical workloads? Next, align your workload transformation vision with broader business goals. A short stakeholder workshop might allow you to uncover ideas such as: Ways your cloud strategy could help to advance your broader business goals How to prioritize your workload migration to support desired business outcomes like cost, speed, innovation, or streamlined functionality Learn more about cloud strategy workshops >> Zoom in before you zoom out. Workload migration optimizes consumption to help you run leaner. But you don’t have to stop there. True workload transformation comes from maximizing efficiencies at every step. As you consider ways to transform your workloads, it’s worth taking the time to zoom in on what those entail. If your goal is a modern architecture that allows rapid pivots and scalability, but your applications and processes need some work, you may need to start with foundational improvements before jumping into more sophisticated tools and tactics. Make sure you’re keeping an eye on the details throughout your cloud migration journey. Some factors to keep in mind include: Adopting best practices like unit testing and documentation for application development, especially as you move into microservices and connecting cloud-native apps to legacy systems. Stress-testing each application or feature prior to migration to determine if it will perform as expected in a cloud environment. Checking performance, reporting, and data egress needs before making placement decisions. Choosing the right instance type for your migration based on the workload’s required memory, connectivity, and storage to avoid performance impact. Aligning migration strategies with your security standards, including backup and business continuity requirements, access, and API requirements. Learn more about infrastructure assessments >> Ditch the hype. Contrary to what you might have heard, there’s no one-size-fits-all solution to workload transformation. With the digital landscape shifting faster and faster, getting sidetracked and stuck in the mud are all too easy. Don’t buy into the hype. Joining Team AWS or Team Azure or Team Google won’t do your business any favors in the long run. That’s why many businesses move into a multicloud or hybrid cloud model, which adopts a platform-agnostic approach to workload optimization. In this framework, you might have some workloads running on-prem, while others operate in some combination of cloud options. Learn more about cloud technology strategy >> Be open-minded. However, even with variable workloads, the costs and efficiencies of different cloud platforms might vary month to month or even week to week. Some organizations look at that and figure they can’t afford to build each workload four (or more) different ways, so they go all-in with one platform. That can be short-sighted. Instead, many companies opt for some degree of containerization. Using Kubernetes and DevOps, containerizing workloads lets businesses make on-the-spot decisions about where to run a workload. Using this method, your team can essentially drag and drop a workload to the cheapest or most efficient cloud or on-prem location. But why stop there? Once your workloads are containerized, adding automation can save you even more by establishing, testing, and refining models for workload placement without relying on time-consuming manual decisions. Learn more about modernization roadmaps >> Get there faster. Done right, workload transformation eliminates overrun. When you find the right ways to combine, host, and run your workloads, your whole business benefits from the efficiency. And you don’t just save in terms of performance and scalability — you also save time and money. Wouldn’t it be great to transform faster? Fusion helps you innovate and modernize your architecture while supporting ongoing feature development and production. True workload transformation supports your ongoing business needs while also positioning your company to win in the future. Let us know how we can help. Assess your workload strategy >>
While artificial intelligence (AI) continues to linger in popular imagination in the form of humanoid robots, in real life AI more often exists as a process enabler. Over the past several years, as costs democratized the technology, AI and related emerging technologies like machine learning (ML) and deep learning (DL) became more accessible to mid-market companies. Today, most businesses use AI in one capacity or another — streamlining work, minimizing risk, and gaining competitive insights. These innovations are more than buzzwords. They have powerful potential to revolutionize the way your business collects, processes, and acts on data to solve the real problems facing your business. AI, ML, and DL in the business context To find the right AI applications for your business, it helps to understand your options. Artificial Intelligence Machine Learning Deep Learning Definition Machines programmed to be “smart” Machines that learn from experience provided by data and algorithms ML applied to larger data sets and using multi-layered artificial neural networks Common Examples Smartphones, chatbots, virtual assistants Spam filters, online purchasing recommendations Alexa, Google translate, facial recognition, self-driving cars Example Use Case Configuring a CMS to deliver personalized website experiences using available data points Discovering patterns in data such as “customers who buy X also buy Y,” purchasing cart analysis Processing a large volume of unstructured data, such as images or voice recordings, to generate insights Limitations Machine can only act on specific rules provided Humans must input data parameters as a starting point Requires very powerful – and expensive – computational resources How machine learning differs from AI “ML is the science of getting computers to act without being explicitly programmed.” Stanford University Machine learning takes a different approach to developing artificial intelligence. Instead of hand-coding a specific set of rules to accomplish a particular task, ML trains the machine using large amounts of data and algorithms that give it the ability to learn how to perform a task. Over the years, algorithmic approaches within ML evolved from decision tree learning, inductive logic programming, linear or logistic regressions, clustering, reinforcement learning, and Bayesian networks. Currently, machine learning uses three general models: Supervised learning: Humans supply factors until the machine can accurately apply the distinctions (for example, defining what counts as spam to a filter). Unsupervised learning: The system trains itself on provided data, which is used to surface unknown patterns, as in clustering and association. Clustering looks for patterns of demographics in data and how they predict one another, as in targeting groups of customers with products they will likely need. Association uncovers rules that describe data, as in online book or movie recommendations based on previous purchases and purchasing-cart predictions. Reinforcement learning: Using complex algorithms, the system learns through trial and error toward a defined “reward” of success. Cycling quickly through mistakes or near mistakes, the machine adjusts the weight of the previous results against the desired outcome. How deep learning works As another method of statistical learning that extracts features or attributes from raw data sets, deep learning builds on ML frameworks. While ML requires humans to provide desired features manually, DL uses even more complex algorithms and achieves more sophisticated results without human input. Deep learning algorithms automatically extract features for classification. This ability requires a huge amount of data to train the algorithms and ensure accurate results. To process this volume of data, DL requires specially designed, usually cloud-based computers with high-performance CPUs or GPUs. Using multi-layered artificial neural networks inspired by the biology of the human brain — specifically the organic interconnections between neurons — deep learning trains artificial neurons to identify patterns in information to produce the desired output. Unlike the human brain, artificial neural networks operate via discrete layers, connections, and directions of data propagation. Three common types of artificial neural networks and DL processing applications are: Convolutional neural networks (CNN) are deep artificial neural networks that are used to classify images, cluster them by similarity, and perform object recognition. These algorithms navigate self-driving cars and enable facial recognition, but are also used in leading-edge medical applications such as identifying tumor types. Generative adversarial networks (GAN) are composed of two neural networks: a generative network and a discriminative network. While GANs can be used negatively as in the creation of “deep fake” photos and video, organizations can also use GANs to create privacy-safe data pools for ML. Natural language processing (NLP) is the ability to analyze, understand, and generate human language, whether text or speech. Alexa, Siri, Cortana, and Google Assistant all use NLP engines, and many businesses are exploring ways to incorporate voice into their proprietary applications and digital solutions. Make smart decisions about AI New Era Technology provides cloud infrastructure and emerging technology solutions that accelerate your digital transformation. Our teams help businesses across a wide variety of industries uncover the best use cases for AI, and the right emerging technology solutions to meet your goals. We can help you source, clean, and integrate your data, build and train machine learning models, and iteratively test and improve your solution to maximize results. Not sure how this might work for your business? Check out these real-world examples: Find out how machine learning helps a national pizza chain retain customers >> Discover how AI transforms business processes >> Explore the future of wearables and mobile ML technology >> Learn how ML can help businesses predict sales pipelines >>
In today’s rapidly evolving markets, with technologies and customer expectations changing more and more rapidly, companies recognize the need for digital transformation. And yet, studies show that 70% of digital transformations fail. Despite their best intentions, many organizations get caught up in long-term digital transformation plans that don’t deliver value for months or even years. It’s no wonder that the results are mixed at best. Whether you’re just getting started or facing yet another costly, time-consuming roadblock on your digital transformation journey, we recommend taking an entirely different approach: committing to an Agile digital transformation. Defining an Agile approach to digital transformation Although it’s most often used as a framework for software development, the principles of Agile methodology are exceptionally well-suited for complex projects of any kind, including the wholesale change required for a successful digital transformation. The key benefits of an Agile approach include: A customer-centric mindset Speed of change with a focus on delivering value right away Flexibility and rapid response to changing circumstances A holistic view of the solution For companies that need to keep pace with changing circumstances and shifting customer expectations, it’s easy to see why an Agile approach to digital transformation makes sense. Implementing an Agile digital transformation To pivot from a long-range digital transformation effort to a more Agile approach, apply the key benefits listed above to the digital transformation context. Agile digital transformation starts with a customer-centric mindset Successfully responding to customer expectations may require a significant shift in your business culture. An Agile digital transformation begins with building this new mindset from day one. The Agile methodology frames every task in light of how it impacts the end-user. In the digital transformation context, this translates to viewing work through the lens of customer experience. To that end, an Agile digital transformation begins with defining customer personas. In other words, we discover who the customers are and what they want. From that starting point, we evaluate every potential phase or step we might take in terms of the value it would deliver for the customer and the acceptance criteria that would define success. This makes it easy to prioritize work and build a path forward, and it reinforces a business culture that puts people first and solves real problems for real customers. Agile digital transformation focuses on delivering value right away Agile methodology breaks work into short sprints, with each sprint focused on delivering value within a few weeks. While the speed to results quickly builds equity with customers and stakeholders, the true value of this approach is the ability to test assumptions and manage risk as you go. By managing change in small increments, any mistake or failure can be mitigated quickly — reducing the organization’s exposure to risk and minimizing costly wrong turns — and your digital transformation can get back on track fast. Most companies find an Agile approach to digital transformation also helps to refine customer models, especially as expectations change. Instead of assuming you know what your customers want, you can test and refine products, processes, and solutions in real time, with real people. Agile digital transformation increases flexibility and enables iterative innovation As you collect feedback from incremental rollouts and real-world testing, Agile methodology enables your organization to grow in flexibility. At the end of each sprint, your team will conduct a Sprint Review & Retrospective, looking at data and outcomes to decide whether or not to continue, pivot, or change your approach. Organizations that report successful digital transformations adopt a permanent posture of growth. In this mindset, there is no “one and done.” There is always room for improvement, for increasing effectiveness, and for building efficiency. This method also reduces the tension of staring down a large and complex problem. All tasks not associated with the sprint at hand are placed in a backlog, and that list is re-prioritized at the beginning of each new sprint. Companies that take an Agile approach to digital transformation find that the ability to reorder the backlog as learning occurs leads to better results and more lasting change. Thanks to Agile project management, your teams are always working on the most valuable tasks at any given time, constantly delivering results and helping to shape a new company culture. Agile digital transformation takes a holistic view Unlike drawn-out projects that can easily become siloed from the day-to-day work of an organization, an Agile approach gives your company a holistic view, seeing how digital transformation impacts every process and business area. The drive to continually improve shifts the entire organization, and as your team gains experience and produces results with each sprint, other departments will flex to match. For example, your team may be improving the user interface (UI) of a product, adding features with each sprint to better guide customers through the purchasing journey. Although the product team may primarily be involved, other business units might respond by: Retraining customer support to respond to new customer questions about the changes Rewriting core marketing messages to match the new customer journey Developing new sales collateral to emphasize the improved UI Recalibrating data collection and analytics to add new data events and key performance indicators to measure the effectiveness of the changes Keeping everyone in sync will make your company’s internal communications and leadership collaboration more efficient and effective as well. With regular practice, your organization will be ideally positioned to roll future changes out smoothly. Starting down the path to Agile digital transformation If you’re new to Agile methodology, or if you’ve reserved the idea for IT, implementing it more broadly for your digital transformation can be daunting. An experienced partner can help. Our team implements Agile structures across organizations, helping companies transform the way they do business and connect with their customers. We can help you get started, get unstuck, and get on track for an Agile digital transformation. Let us know how we can help. Learn more about our approach to innovation >>
Every few weeks, we share insights with our Fuse subscribers along with news and trends we’re following across the web. Here’s a compilation of some of our key insights from last quarter. If you want content like this delivered directly to your inbox, we’ve got you covered. Subscribe to the Fuse here. Data literacy: Food for thought How do you get from buzzwords like “data literacy” and “data culture” to confidence that data is driving better decisions across the business? If Peter Drucker was right — and he’s Peter Drucker, so he probably was — culture eats strategy for breakfast. It’s not enough to build a business case for data. You need a business culture to support it. In our experience, success starts with aligning people, processes, and business goals with purpose-built data and technology solutions. When people understand what data makes possible and how it impacts their job — where to find it, and how to read and interpret the data they need — convincing them to use it to drive better decision making is a much easier lift. Easier said than done? You bet. We love a complicated algorithm or elegant data architecture, and we’re basically ninjas at selling business cases (if we do say so ourselves). But there’s a reason Fusion stakes a claim on being people-focused. Because we don’t just love data. We love when it works. Get smart: If you’re looking for an overview of data culture and a baseline for building data literacy across your organization, we recommend Be Data Literate by Jordan Morrow. Although written as a primer for individuals, the book’s framework could easily be used as a springboard for helping your whole company level up its data acumen. Read the full Fuse: Data for March. A one-brain approach to B2B marketing In AppleTV+’s bizarrely compelling drama Severance, employees’ brains are modified to separate their work memories from their off-work thoughts. Of course, what makes the show sci-fi is the fact that no one really has a “work self” and a “life self.” So, why does B2B marketing often seem to assume that consumers and business purchasers are different people? Compare your IG feed to the LinkedIn ads you’re served. One platform shows you talking Australian lizards. The other shows you text about processing speeds. When you need insurance, you remember where to go. When it’s time to make a CMS platform decision you…probably should have made a note. We want to believe that our B2B customers make purely rational decisions, but experience and data suggest otherwise. Whether it’s B2C or B2B, people predominantly buy from emotion, not stats and features. Creative marketers who are willing to push the envelope can capitalize on this idea to stand out in the sleepy B2B marketing landscape. It’s hard to argue with results. One of our clients, a pharma sales enablement company, saw 3x lead growth when they pivoted from standard B2B ads to a brighter, more engaging campaign direction. Your B2B targets don’t come to work as a separate persona. Creative marketing captures attention with a whole-brain approach. Ready to ditch the sinister work-life lobotomy assumptions? We’re always ready to talk about how to set your brand apart, whether it’s new creative or a streamlined martech stack. Let us know how we can help. Get smart: Wondering how to sell creative marketing internally? We’ve been reading The Human Element: Overcoming the Resistance that Awaits New Ideas and thinking through the authors’ framework for overcoming our natural resistance to change — especially as it applies to organizations. If you’re struggling through a shift, this book could be worth your time. Read the full Fuse: Marketing for March. Put your technology on a balanced diet Tech creep is kind of like strolling the cereal aisle with a four-year-old (or a 34-year-old, no judgment) who begs for the choco-sugar-neon-behavior-bombs instead of the sensible-fiber-nut-loops you had planned. When it comes to building your tech stack or stocking your pantry, “it looked cool” isn’t really a strategy. And yet, for many companies, an enterprise architecture hodge-podged out of whatever looked good at the time often gets the job done. Until it doesn’t. A move to the cloud, a new data privacy mandate, or even the increasing demand for speed and agility to stay competitive might expose the imbalance in your tech stack. How do you get back to a more wholesome view? Realigning your solutions with your organizational goals and objectives is a great start. Regardless of how long you’ve been using it, does every piece of your technology still fit your plan? You might need to let go of sunk costs and admit that a tool has gotten a little soggy for your current needs. You might need to put your appetite for shiny new solutions on a diet. At the risk of straining our balanced breakfast metaphor past the breaking point (too late?), we recommend putting a healthy strategy on the menu. As guidelines change and organizations shift to keep up, this is a great time to reassess your tools and processes. In its simplest form, a refreshed technology strategy includes a current state audit, an ideal state articulation, and a plan to bridge the gap. Whether your internal culture skews Team Sugar-Bombs or Team Fiber-Loops, we can help you take a strategic view and bring your technology stack back into balance. Get smart: We get that it’s a little bit ironic for a bunch of tech consultants to recommend a book like Cal Newport’s Digital Minimalism. But hear us out. Newport’s approach to consumer technology – that tech and platforms should have to earn their place in your life by proving that they help you meet your goals and values – has some merit for the business world as well. We’ve all seen what Newport terms “maximalism” at play in sprawling, bolted together legacy architectures. Maybe the time has come for a more minimalist, goal-driven tech stack. Whether you’re ready to start over or looking for ways to modernize what you have, we’re always happy to talk technology strategy. Read the full Fuse: Technology for April.
Why quality is key for legacy system modernization According to Boston Consulting Group, 70% of digital transformations fail to meet expectations, deliver on time, or stay on budget. Why? For some businesses, the roadblock is executive buy-in. For others, it’s resources. But many organizations at the mid-market to Fortune 500 level agree on the need for cloud adoption and system modernization and have no qualms about paying for it. At those companies, the problem is a more foundational issue. The foundation of modern, scalable architecture is quality. The most sophisticated code and trendiest solutions can only take you so far. Without a solid process that prioritizes continuous delivery, a clean commit history, and unit testing, your legacy system modernization efforts will stall out. Legacy system modernization requires a culture shift. At a certain point, traditional enterprise architecture hits a scalability ceiling, and performance begins to suffer. Most organizations accept that strategic cloud adoption is the logical next step, but fewer understand the culture shift that a modern, scalable architecture often requires. Putting a problem into a box and calling it Kubernetes does not equal modernization. In an asynchronous cloud environment, you can’t afford to indulge a maverick developer mentality. It may sound counterintuitive, but the best way to achieve true scalability, and see cloud ROI faster, is by creating and enforcing a culture of accountability to old-school best practices. Building a quality-first culture starts with clear communication. Learning a new way of operating is hard. That’s why change management plays such a critical role in successful digital transformations. When it comes to retro-engineering a quality mindset into your IT culture, you’re requiring process changes and new skill acquisition — and those don’t come easy. Successful change at the team level takes transparency, and a commitment to clear communication. To build organizational buy-in and help dev teams take ownership of a shift to a quality-first development culture. Quality starts with testable code. You can’t inspect quality into a system. When you’re building a modern, scalable architecture you can’t afford the risk of last-second changes with huge downstream implications. Instead, commit to continuous delivery so that code reviews move beyond a formality to deliver meaningful impact. Quality builds momentum. A reliable workflow of testable code, where potential problems are spotted, assessed, and addressed right away, enables faster development. The small up-front time investment leads to significant efficiencies down the line, laying the groundwork for increasingly streamlined and sophisticated solutions. Quality delivers massive time savings. Entrenched developer cultures sometimes resist inexpensive and fast quality solutions like unit testing. It may not be flashy, but adding one extra function to do a unit test is a much simpler fix than the downtime you incur from uncontrolled code. Quality unleashes your team. Organizations sometimes go to extraordinary lengths to avoid simple quality fixes like unit testing. But when there are problems in production, they always come back to code. Rather than taking on an expensive, fragile, time-intensive work-around, getting developers up to speed on unit testing helps you get more utility from your team now and prepares your architecture for the future, when today’s tribal knowledge may not be as readily available. The most important journeys start with one step — and digital transformations are no exception. Change is hard. But overcoming resistance to achieve legacy system modernization is worth it. When your goal is a modern scalable architecture, the first step is always quality. If your modernization efforts have gotten off-track and you’re feeling stuck, we can help. Fusion helps enterprise organizations align their goals, refine their processes, and identify the right technologies to step into the future with confidence. Learn more about cloud strategy >> Schedule a consultation >>
Business processes are the backbone of any organization. But with all the time spent on manual processing, human errors, and system inefficiencies, companies are losing thousands of dollars each year just trying to function. That’s where business process automation (BPA) comes in. The BPA market is ballooning at a rate of 10% year-over-year. And for a good reason — it gets your employees away from manual, administrative tasks and back to doing the job you hired them to do. What is business process automation? Business process automation is the use of technology, including software and systems, to automate simple to complex repeatable, everyday tasks. In doing so, BPA accelerates how work gets done through automated processes defined by the user’s rules and actions. Information is routed to the right place at the right time without significant manual effort. Ultimately, companies can improve their overall efficiency as most frequently repeated tasks are automated to increase speed, accuracy, and consistency. Some common uses for BPA include supply chain management, employee onboarding, system provisioning, work intake management, document approval, and social media posting. What problems can BPA solve? BPA can solve numerous processes inefficiencies and alleviate significant pain points that organizations across industries are facing. Some of the common scenarios in process automation include: Governance: How do we avoid system, site and content sprawl in a way that is standardized and controlled, but still agile and intuitive? Streamlining: How can we improve and track our work intake and management processes? Content Management: How can we help our users produce, organize, and find content across our systems? Regulatory Compliance: How do we ensure we’re aligned with data and retention terms from regulatory agencies and in our contracts? Integration: How can we avoid the need to enter the same data into multiple systems? Cost reduction: How can we reduce repetitive, manual, and error-prone tasks? With the power of business automation, you can answer these questions and save your organization time and money by reducing the time spent completing these tasks. Using Office 365 for process automation Microsoft has an extensive cloud-based ecosystem that can make automation seamless and fit your unique business needs, including Office 365, Power Platform, and Azure. Power Automate Flow is a key component of the Power Platform and plays a significant part in process automation. Flow’s powerful design surface, coupled with hundreds of triggers and connectors, allows automation to be achieved by a variety of user roles with varying technical skills – from power users to administrators and developers. With Power Automate Flow, you can: Streamline business processes with automated workflows Leverage other Office 365 systems and applications Integrate with a wide variety of third-party business systems Expand automation capabilities across desktop, web, and mobile Reduce operating and support cost Real-world process automation One of our clients, a healthcare consulting firm, had more than 20 terabytes of digital content — all stored on internal file servers and an on-premises SharePoint farm — with hundreds of active projects and a multitude of consultants trying to access documents and data. They were also dealing with strict compliance agreements and industry regulations. In addition to migrating their digital content to Teams, SharePoint Online, and Azure Files, they wanted to reduce error-prone manual tasks and reduce their overall support costs by automating and streamlining their work management processes with Teams, SharePoint Online, Power Automate Flow, and Power Apps. Here is a high-level architecture diagram of a solution we developed to achieve the automation they were looking for: Power Apps and Teams are the two systems that end users and content owners interact with. SharePoint tracks the data in lists, and then those are backed by the Azure SQL database tables. In this case, SharePoint Online is being used for the data storage, but in other use cases, you could replace SharePoint Online with Azure SQL or a different storage technology. Power Automate is Microsoft’s power-intelligent, cloud-based solution that replaces old on-premises workflows and automation systems. It uses triggers and actions to eliminate repetitive tasks without manual processes or coding. At the beginning of the process, a trigger kicks off the flow. In this scenario, we used canvas flows that contain the business logic and the actions to perform the steps necessary to automate the entire process. Here is an example of one of the sequences that were used in this solution: In this sequence, there are two primary APIs used in the flows: The Graph API and the SPO REST API. The Graph API is an abstracted API for most of Microsoft’s cloud-based systems that allows you to interface and interact with these systems. The SPO REST API is used to directly interact with SharePoint Online. Additionally, we created a series of templates in Teams to standardize their Information Architecture and governance. Learn more about the success of automation for this healthcare company here. What are the benefits to Microsoft’s automation tools? Overall, installing the right software and implementing the right flows can be a cost-effective way to improve your business processes and reduce the manual input required with repetitive tasks. This can result in a number of key benefits, including: Reduced workload Reduced operational costs Increased reliability Optimized performance Improved compliance Reduced human error Increased reliability With the tools provided in Power Automate, you can get the most out of your existing resources and make your processes more efficient. In a Forrester Total Economic Impact Study, the impact of Power Automate was clear; they found: A 199% ROI over three years $1.41 million in worker time savings over three years 27.4% reduced errors due to increased automation Conclusion Ultimately, BPA aims to make your business processes more efficient, cost-effective, streamlined, and error-proof. The reality is, automating your processes as an organization is not an option — it is not an if, but a when. Companies that get ahead of their automation processes have a significant advantage over their competitors. And, using tools like Office 365 make automation a simple and seamless process. These triggers and actions come together in one integrated ecosystem that offers thousands of prebuilt templates and hundreds of connectors, so you can customize automations and fully automate your work. With no-code apps, rapid development, and an easily configurable solution, Office 365 gives you complete control over your business processes and to get the right information when and where you need it.
In a few short years, hyperautomation, or intelligent automation, has gone from a relatively unknown term to a word used across the technology spectrum. Gartner’s Strategic Technology Trends for 2020 named hyperautomation the #1 strategic technology trend for the year. Gartner also forecasted that the hyperautomation software market will reach nearly $600 billion by 2022. What’s fueling the investment? Organizations are trying to remain competitive by decreasing costs and increasing productivity. A focus on hyperautomation can address business challenges and improve operational efficiency, not to mention elevating the customer experience. “Hyperautomation has shifted from an option to a condition of survival,” said Fabrizio Biscotti, research vice president at Gartner in a recent press release. “Organizations will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world.” The foundation of hyperautomation With Robotic Process Automation (RPA) at its core, hyperautomation incorporates advanced technologies — including artificial intelligence (AI), machine learning (ML), natural language processing, optical character recognition (OCR), process mining, and others — to not only automate tasks typically completed by humans but also to build intelligence into the processes, as well as the information derived from those processes. By building on RPA, hyperautomation elevates workflow automation to make decisions previously made by people. It augments the power and value of what RPA provides with a proven path to applying AI to improve business operations. Hyperautomation and digital transformation Because of the level of automation that can be achieved, hyperautomation is commonly referred to as the next major phase of digital transformation. And, it’s an intricate process. Organizations must implement automation simultaneously on multiple fronts to reach the end goal of hyperautomation. They often need to partner with digital innovation advisors and technology consultants to create a hyperautomation strategy from top to bottom and take all of the organization’s nuances into account. To achieve scalability, disparate automation technologies must work together. Careful planning, implementation, and improvement of processes are accomplished through intelligent business process management (BPM). BPM is a core component of hyperautomation and supports long-term sustainability and operational excellence. The combination of BPM solutions with low-code, RPA, AI, and ML has become a driving force for digital transformations, integrating essential data, connecting your workforce, and developing applications. It is up to technology leaders to create a clear strategy, set objectives, and prioritize actions across all business operations. Doing so ensures that the application of automation is efficient. Employees on the front lines are also in an excellent position to identify which processes would provide the most benefit from automation. This can be supported by implementing a demand management solution. Then, it can then be synchronized with organizations’ change management to ensure employees understand the changes and are prepared for more advanced processes, thus elevating the workforce. Organizations may be wary of the costs of change on such a large scale, but the process of integrating technologies does not always require creating a new infrastructure to replace manual operations. Many RPA, AI, and ML solutions can be integrated into automation and technologies that already exist. The future of hyperautomation The next generation of hyperautomation includes support for more complex processes and long-running workflows. Software robots will be able to interact with business users across core business functions, directly impacting the customer experience. Hyperautomation represents the next step in intelligent automation and will transform how we will work in the future. It allows businesses to protect their investments through a holistic approach to digital transformation. As hyperautomation becomes more prevalent, we will realize a seamless and equal blend of robotics, human employees, and existing systems, which will all work collaboratively in a way never seen before. No matter your industry, hyperautomation is worth consideration for its potential cost savings, intelligent processing, intelligence mining, employee efficiencies, and customer service improvements. Learn more about how hyperautomation technologies like ML and AI can benefit you.
Are you spending increasing amounts of time reacting to incidents where an end-user clicked on something, downloaded an unknown file, or entered credentials for a document they thought a coworker sent to them? It’s not just you. A recent survey confirmed that cybersecurity threats are on the rise. 53% of IT professionals surveyed indicated an increase in phishing activity since the start of the COVID-19 pandemic. With remote work continuing for many employees, IT departments find themselves playing defense against these cyberthreats. Sophisticated phishing techniques can catch even the most well-meaning employees off guard. Regardless of how your network is monitored, secured, and maintained, the “human firewall” can be the weakest link in the chain. To combat this, practical security awareness training has become vital. The need for security awareness training Security awareness training is necessary to teach employees how to identify potential threats. All employees, regardless of job title and function, are susceptible to attacks. A 2020 MediaPRO and Osterman Research study found that only 17% of employees are very confident that they can identify a social engineering attack, while more than one-quarter of employees (28%) admitted a lack of confidence in identifying a phishing email. Because company information is readily available through mobile devices, tablets, and laptops, there is always a risk of accidental exposure. Offhand clicks, done without hovering over a link, can spell disaster. Even two-factor authentication isn’t safe from social engineering schemes to obtain passwords and logins. Importance of security-minded culture Establishing a culture of security-minded employees goes beyond learning modules and quizzes. Security is the responsibility of all employees that have access to corporate systems. Awareness and training are ongoing activities, not a checkbox to complete once a year. By recognizing good behavior (i.e., thanking employees for forwarding suspicious emails along to the Help Desk), you can continuously instill the importance of each employee’s part to protect the company. You should use all incidents as teachable moments. But there are some other, less obvious benefits of the security-minded culture. A security-minded culture protects assets The average cost of a data breach in 2020 was a staggering $3.86M. Companies need to defend themselves by helping to increase the effectiveness of the “human firewall.” A security-minded culture empowers employees Security awareness training can reduce human error and empower your staff to know when an incident is happening. By preparing employees and enabling them to take action (i.e., feeling comfortable saying no when a caller posing as an executive requests sensitive passwords), you will improve employee reaction time and empower your organization’s employees to make decisions to help the organization. A security-minded culture prevents downtime Time is money, and downtime can create a significant loss of revenue. When an incident occurs, systems can be taken offline to properly investigate and recover from an incident. If your employees are more security-minded, there will be fewer incidents that cause downtime. A security-minded culture ensures compliance Some industries have enhanced scrutiny for employee security awareness. Conducting training ensures that you meet regulations and show that you are doing your due diligence as an employer and vendor. How to build a successful security awareness training program Creating, or even improving your security awareness training program, doesn’t have to be a massive undertaking. Because this subject is so top-of-mind, you might find that now is the perfect opportunity to engage your organization and use the momentum to your advantage. Here are some steps to get you started: Step 1: Gain stakeholder backing Unfortunately, security can be viewed as a low-value cost center. It is crucial to make sure your program has senior leadership support. Providing research data and your current metrics on the current number of phishing emails your organization receives can help you explain the need for investment. Step 2: Define security awareness education goals Not all organizations will have the same plans for the subject matter, employee participation, and education methods. Identify security training that meets the needs of your business. Step 3: Assess your audience Because security is an organizational issue, your audience probably consists of a wide variety of backgrounds and skillsets. Not everyone going through training is well-versed in cybersecurity, and not everyone learns the same way. Get to know your audience and ensure you are aiming to meet their needs. Step 4: Develop a program The education you provide could be administered in many ways, including learning management modules, presentations, and onsite Q&A sessions. Your company should also be performing regular phishing tests to simulate outside threats. Step 5: Perform ongoing training Awareness training is not something that should just be done annually, but rather something that takes place on a regular cadence that makes sense for your organization. Making security guidance and education routine ensures that your employees keep up to date. Emerging threats are continuously discovered. Your company culture and meeting cadence can best determine the frequency and methods that work for you. Step 6: Track results Metrics provide insight into the effectiveness of the training, as well as provide measurable reports to leadership. Successful training will lead to more reported incidents as employees become more aware. The percentage of employees who have completed training, number of phishing exercises, and total real phishing threats detected are significant numbers to measure. Gone phishing Security awareness training is an essential part of any IT strategy, and one that you can’t afford to put off. Remote work paired with an increase in phishing threats creates a dangerous liability for your organization. All employees, regardless of position, need training to prevent a security incident. Find out more about how Fusion Alliance works with clients to improve security awareness: We partnered with a large, Ohio-based utility company to reduce the risk created when employees use their personal devices at work.
Advances in mobile health technology have transformed the entire landscape of healthcare, including the ability of physician groups, employers, nursing home facilities, and pharmaceutical companies to capture data in the healthcare space. Gone are the days of paper tracking for your glucose levels and blood pressure. Instead, wearable devices like watches and trackers can seamlessly provide real-time data streams to applications and third parties. The data collected from wearables can be used for clinical research, patient monitoring, and wellness tracking, among other uses. Each data point collected can add complexity to your broader data set. Because of the amount and complexity of data, turning to machine learning (ML) can help organizations leverage their data to identify patterns and make data-driven decisions. By applying machine learning techniques to wearable device data, we can now surface patterns in big data and make predictions about behavior. Machine learning enables healthcare-related industries to leverage wearable device data and identify trends, improve recommendations, and define research outcomes. Popularity of wearable devices Wearables are popular, and their adoption continues to grow. Globally, the wearable technology market is expected to grow from $69 billion in 2020 to $81.5 billion in 2021, an 18.1% increase, according to the latest forecast from Gartner. What’s fueling the growth? Demand for smart devices in the healthcare sector is rising, as is demand for Internet of Things (IoT) devices. Many devices are not fitness-specific, featuring notification of text messages, push notifications for mobile apps, and the ability to pay for items by scanning a QR code with Google Pay or Apple Pay. As such, they have broad appeal. “As a result of the pandemic, we have seen wearable devices become much more than just activity trackers for sports enthusiasts. These devices are now capable of providing accurate measurements of your health vitals in real time. Improved measurement accuracy coupled with the latest advancements in ML make it possible to detect abnormalities before they lead to a major health event.” – Alex Matsukevich, Fusion Alliance Director of Mobile Solutions Types of data collected by wearable devices There are a variety of brands and categories of wearable devices, from mass-market consumer versions to highly specialized types created for niche uses. Apple, Fitbit, Google, Samsung, Garmin, LG, Sony, and Microsoft dominate the market. Though the concept of “wearables” includes a focus on wristwatches, while exercise equipment, glasses, and textile sensors are also becoming more common. Wearable devices can measure: Sleeping patterns Heart rate Irregular heart rhythms Location/route during exercise Pace, stride, and distance while moving Blood oxygen levels Falls Limitations of wearable device accuracy Wearables do have limitations, and accuracy is a concern. Healthcare decisions made using erroneous data could have outcomes detrimental to a patient’s overall health. A study from the University of Michigan reviewed 158 publications examining nine different commercial device brands. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate, and Fitbit tending toward underestimation. But for energy expenditure (calories burned), no brand was deemed accurate. This does not mean that the results are invalid, but that there is a significant difference between results from wearables and clinical results in a lab setting. Wearable devices are constantly upgraded and redesigned as technology improves. And data collected by wearables does not provide a clinical diagnosis. As such, this data is just part of the larger picture of health and can be used only in conjunction with other factors to evaluate your overall wellbeing. Overcoming the biggest challenge of wearable device data analysis Healthcare professionals are already using ML to analyze data for patients. Research published in the International Journal of Research and Analytical Reviews confirms that ML techniques are successful in predicting health conditions such as heart disease, diabetes, breast cancer, and thyroid cancer. The biggest hurdle to incorporating device data into broader data sets is the addition of new inputs, such as hours of sleep or total steps walked per day. Traditional data points such as total cholesterol or blood pressure readings are less frequent, so there is a smaller amount of data overall. The challenge is finding how to best incorporate it into other data sets to create a more comprehensive picture of health. The future of wearable device data and machine learning We can glimpse into the future of wearable device data and machine learning with Microsoft’s recent patent filing. Their potential product aims to provide wellness recommendations based on biometric data, such as blood pressure and heart rate, pertaining to work events. To do this, Microsoft requests access to applications used by employees. Microsoft then tracks data points such as: Duration of time spent writing emails Number of times a user refreshes their inbox Time spent reading emails Number of corrections made when writing emails Recipient list for emails Number of meetings in a day Tone of language in emails By combining this information with biometric data (from a secondary device such as a Fitbit or Apple Watch) and machine learning, Microsoft could begin to understand what work events trigger a response. For example, suppose an employee received an email from their manager. Microsoft might observe that the employee spent a higher-than-average amount of time reading the email and that the employee’s heart rate was also elevated during this time. Based on these insights, Microsoft could propose recommendations for helping employees manage stress levels, highlighting events that trigger anxiety. Patent filing sample image from Microsoft outlining tips and recommendations to improve employee wellness With a broad user base using both Office and Teams already, Microsoft has a deep understanding of work-related events. As Facebook built their business making sense of our social lives, Microsoft has the potential to optimize our work lives. “Wearables combined machine learning will become the new standard in personalized consumer electronics, rapidly increasing in popularity and scale every year until then. An integrated device of the future will be able to get a baseline of your health and will alert you to any abnormalities present. We already see this happening with the new Apple Watch, and it will be very soon that this technology becomes commonplace.” – Michael Vieck, Fusion Alliance Software Developer Wearable devices will transform healthcare experiences Data is the key to predicting, understanding, and improving health outcomes. IBM Research anticipates that the average person will generate more than 1 million gigabytes of health-related data in their lifetime, equivalent to 300 million books. The sheer volume of data means that machine learning will be vital in making sense of it. Paired together, wearable devices and machine learning have the potential to transform healthcare experiences. Today’s applications and uses are only the beginning. Read more: Top 3 reasons to invest in machine learning for mobile Machine learning and wearable devices of the future Wearing Your Intelligence: How to Apply Artificial Intelligence in Wearables and IoT
More organizations have shifted to the cloud, completely transforming the way business is done. For many, the days of solely relying on big on-premise data centers are gone, now replaced with a combination of on-premise and cloud-based applications. As the way we store and access data changes, we are forced to come up with new ways to improve infrastructure and keep it secure. That’s where Zero Trust comes in. No matter where you are on your Zero Trust journey — maybe you’ve never heard of it, maybe you want to try it but don’t know where to start, or maybe you’re in the thick of it — we’re here to walk you through five steps that will help you understand Zero Trust and how it can elevate your data security. So what is Zero Trust? Zero Trust is a security concept centered on the belief that organizations should not automatically trust anything inside or outside their perimeters and instead must verify anything and everything trying to connect to their systems before granting access. This vendor-neutral design philosophy allows maximum flexibility in designing infrastructure architecture. Every access request is fully authenticated, authorized, and encrypted before granting access. Lateral movement is prevented through security policies and least privilege (minimum permissions to do your job). Rich intelligence and analytics are utilized to detect and respond to anomalies in real time.\ The Zero Trust Maturity Model Traditional This level is where most organizations are at today. Companies who are at this stage have not started their Zero Trust journey, and generally have: On-premises identity with static rules and some single sign-on (SSO). Limited visibility available into device compliance, cloud environments, and logins. Advanced At this level, an organization has begun its Zero Trust journey and has started to make some progress. The areas of adoption at this stage are usually: Hybrid identity and finely-tuned policies that gate access to data, apps, and networks. Devices registered and compliant to IT security policies. Networks being segmented and cloud threat protection in place. Analytics that are starting to be used to assess user behavior and proactively identify threats. Optimal Although the Zero Trust journey is never complete, at this stage an organization has made great strides and improvements in security through the adoption of: Cloud identity with real-time analytics and dynamically-gated access to applications, workloads, networks, and data. Data access decisions governed by cloud security policy engines and secured sharing with encryption and tracking. Complete Zero Trust in the network – micro-cloud perimeters, micro-segmentation, and encryption are in place. Implemented automatic threat detection and response. Steps to achieve Zero Trust 1. Define your protect surface Define your protect surface based on the most crucial data, applications, assets, and services elements for your business. 2. Map the information within your surface There are many ways to map transaction flows, and some techniques for defining your protect surface also apply to mapping its transaction flows. 3. Architect a Zero Trust environment As you develop the architecture, keep in mind ease of operation and maintenance, and flexibility to accommodate protect surface and business changes. 4. Create Zero Trust policy Zero Trust policy is based on the Kipling Method. This shows you how to decide whether to allow or block traffic and how to create a security policy that safeguards each protect surface. Who should access a resource? What application is used to access the resource? When do users access the resource? Where is the resource located? Why is the data accessed — what is the data’s value if lost (toxicity)? How should you allow access to the resource? 5. Monitor and maintain Security is a continuous process as logging and monitoring will reveal needed improvements to make to your policies are your business and infrastructure change. Follow the operational processes you developed when architecting the network to maintain and continually update prevention controls. Running the Zero Trust marathon Zero Trust is a marathon, not a sprint. Since it is not a vendor-specific model, you have the ability to adopt this model utilizing a number of different vendors. If you are ready to start your Zero Trust journey or want to talk about where you’re at, reach out to us today.
The pandemic made it clear that traditional banking is a thing of the past. Online banking had already been on the rise, but a 200% jump in new mobile banking registrations in April 2020 established that customers are able and willing to change. As more Americans bank virtually, banks are fighting to meet customer demand? And beyond the challenges set forth by the pandemic, “digital natives” like Rocket Mortgage, Venmo, Stripe, and Robinhood are all vying for business. These technology-forward organizations position themselves differently from traditional financial institutions and are attracting a younger user base for their services. But a traditional bank has advantages over these challengers: Familiarity and history: Your personal relationships and history with customers mean that your bank is often more aware of their history. And customer questions can be answered in person instead of being routed through a call center. Deep and rich data: Historical data can prove invaluable for ML efforts. Customer deposit amounts, payments, and balance information can be used to predict future behavior. Preference for personal banking: Customers, especially those with a high net-worth, may have discomfort with digital channel dependency for wealth management. A new brand might be a risk, and they could feel uncomfortable not having a specific person to call if something goes awry. As we get back to our “new normal,” traditional banks can use the rich data and relationships they have with customers to their advantage. Forward-thinking leaders are reimagining what it looks like to do business, and they’re using machine learning to elevate the customer experience. Discover how you can use machine learning to create engaging and profitable relationships with your customers. Every bank can find value from machine learning Machine learning might sound like a type of data analysis useful to only the largest of organizations, but its concepts can scale to meet the needs of small and mid-sized banks too. When we use the word machine learning (ML), we are referring to machines and systems that can learn from “experience” supplied by data and algorithms. In banking specifically, ML algorithms can be used to identify patterns in data beyond what humans are capable of observing, and these learnings can be applied to new data sets. It is now possible to improve the customer experience using ML. By parsing customer transaction data, ML can identify clues and patterns ahead of time, even before the customer considers taking action. For example, the process of buying a home and obtaining a mortgage might begin with small savings accumulation or an increase in deposit amounts from wages. ML models can assess banking-specific data like credit patterns, risk tolerance, and price sensitivity, and can be coupled with demographic data like age, median income, and distance to branch. The goal of using ML data in this use case is to target prospective customers with offers most relevant to their situation and stay ahead of customer demands. Knowing where to begin — and where to focus efforts Machine learning has such a wide variety of applications, it can be difficult to know where to start. Identifying a use case for customer-focused ML expenditures is a good first step. In general, we have found that you can benefit from starting with a use case with low or medium relative complexity. Examples focused on improving the customer experience include: Predicting service line interest (HELOC, mortgage refinance, etc.) Streamlining loan approval processes Increasing lines of credit Improving fraud alert notifications With so many use cases to choose from, it can be easy to get lost in the planning for each example. Instead, try focusing on one area at a time. Using your strengths, combined with ML concepts, you can deliver an optimal customer experience that digital challengers just can’t match. Need help getting started? Check out our Machine Learning Jumpstart program. Cross-selling across the relationship with machine learning You can leverage machine learning to determine not only which customers would be a good fit for a mortgage loan, but also the other products that customers might need. Thinking about a mortgage loan, a Home Equity Line of Credit (HELOC) might be a good match for a new homeowner. In any case, the message and product can be tailored to meet the customer’s specific needs. Another part of cross-selling is to personalize the offer based on the customer’s history and propensity to buy. Perhaps an interest rate would be meaningful for one type of customer, while a waived application processing fee would entice another. For individuals who identified as being interested in high revenue products, the marketing effort can be even more personalized, like a phone call or an in-person event invitation. Applying machine learning in real life The following illustration is an example of how an internal dashboard might appear to a banker or service representative. For any specific product, each person has a percentage likelihood that they will take action. Individual model scores are shown, along with next steps, such as outreach about an investment account, or mailing a promotion about mortgage rate refinancing. In this example, marketing inputs, like website data, are combined with transaction and deposit information. When a banker or service representative encounters a customer, either in person in on the phone, they can suggest specific next steps, or ask if the customer has questions. Having a dashboard with this information enables banking employees to be empowered to guide the conversation with data in real time. Related Case Study: Machine learning predicts outcomes at Primary Financial How does a financial services firm improve sales targeting to predict its clients’ desires to invest? Machine learning was the answer for PFC. Learn more. FAQs about machine learning and banking Does the machine learning process work fast enough to enable real-time benefits? For all but the most complex scenarios, yes! Normally, ML is fast enough to be integrated into real-time transactions. Does machine learning get in the way of compliance requirements? In general, no. By using existing data that you obtained or using your data in coordination with third-party data, you are not running amiss of privacy and compliance concerns. How do we ensure the use case we pick machine learning is right given that there are so many to choose from? We recommend focusing initially on those low-cost, high-ROI use cases with a low-medium relative complexity. Given additional experience, context, pipelines, and an understanding of how advanced analytics programs operate, then more complex initiatives can be undertaken. Data reliability can be a concern. Using low-quality data is not advised, but it is possible to start projects with small data sets. Engaging with a third party to evaluate your situation is advised in situations like this. Reimagining customer insights & relationships Banks that employ machine learning will have a portfolio of more customers than ever who are positioned for a variety of banking products delivered in a digital, personalized, and meaningful way. Now is the time to act and implement machine learning to meet customers where they are, using the contact methods that they desire and delivering the products and services to best meet their needs. Need help building your use case and plan? Access our Machine Learning Use Case Guide for Banks. Want to dig deeper? Check out our webinar on this topic.
Many organizations struggle to adopt Agile as part of their digital transformation. And one of the most common struggles in adopting Agile is how to move toward incremental and iterative refinement. Iterating on a goal allows more information to be gained quickly, but it also means timelines are likely to overlap. While stakeholders tend to focus on their piece of the puzzle or the next goal, product owners have to balance complex timelines and goals for multiple stakeholders. How does an Agile product owner prioritize all the overlapping timelines and goals within the wider context of their organization? There are no solid rules, and everyone has to adapt to their own organization, but many successful product owners factor in the following key principles to make their decisions and plan their sprints: Prioritize based on a mixture of urgency, importance, and size Use tactical stories to achieve strategic goals Find ways to regularly invest in maintenance These principles can increase product ownership success and even lead to greater agile adoption across your organization. As in most things, the trick is balance. Prioritize stories based on Urgency, Importance, and Size Urgency There is always pressure to prioritize urgency. You can’t turn back time, and if you miss an urgent issue then you are certain to hear about it. However, the biggest mistake product owners make is prioritizing urgency too high. There will always be urgent requests, and if urgency is your only factor, you will only end up fulfilling urgent requests. Importance Importance is not always reflected in urgency, but it must always be reflected in priority. Waiting for important work to also become urgent almost certainly spells disaster. The proactive stance of prioritizing important work against urgent work also tends to have political implications. We’ve all heard the adage “the squeaky wheel gets the grease.” The best customers are typically the ones who complain the least, so don’t punish them for that. This also applies to stakeholders. The more you incentivize good stakeholder behavior, the more you are in control of your backlog. Size Of course, urgency and importance are not the only two factors to balance. Part of the difficulty of building a sprint for a product owner is the need to also balance size. Ideally, you want constant progress throughout a sprint. It is common for a feature to get started and then get blocked. That is why you need to include user stories of all sizes. Only having large stories tends to mean stories will get handed off mid-sprint. Knowledge sharing provides value, but mid-sprint handoff only slows progress. If most stories in your backlog tend to be large, small stories will have increased priority in order to fill in a sprint even though they may be of lower importance and only medium urgency. Prioritizing through a mix of urgency, importance, and size doesn’t just create a balanced sprint, it also creates a balanced time orientation. Urgent stories make a team reactive. You can’t look forward when you’re focusing on past mistakes or present pressure. Directing your team’s time orientation beyond the present moment is a key strategy for creating a productive team. Use tactical stories to achieve strategic goals Of course, not every sprint can be a perfect balance of importance, urgency, and size. Like the strategy for directing a team’s time orientation, some choices might be preferred to create or enforce a team culture. While a product owner is the leader of their team, they also answer to stakeholders. And organization objectives, usually data-driven ones, are created with these stakeholders as part of an organizational strategy. The decisions a product owner makes that have a direct impact on reaching these objectives are called tactics. But the product owner is still leading a team! There are many strategic decisions that a product owner makes which may not have a direct benefit to organizational objectives, however, they might lead to a more productive team. Good product owners don’t just focus on tactical decisions of meeting objectives, they refine strategies that benefit the individual team and drive toward objectives in an indirect manner. For instance, retrospectives tend to generate more strategic shifts than tactical shifts. Common examples of tactics used by product owners: Sprints containing stories of varying size Proof of Concept (POC) stories which discover information to do further planning or design Prioritizing the most complex use cases first Prioritizing the most common use cases first Stories that require liaising with other teams, when collaboration is an organizational goal Maintenance which offers innovation affordances Maintenance which increases product stability Common examples of product owner strategies: Cross-training by assigning stories based on areas of weakness Siloing specialties so individuals achieve high efficiency in strategic skills Requiring recorded demos as acceptance criteria for certain stakeholders Requiring regular alpha or beta releases for highly engaged stakeholders to provide feedback “Dead sprints” following releases which allow for feedback to build before addressing it Documentation stories which seek to identify outdated/deprecated documentation and update it in order to lower future bug counts Support buckets, allocated time for bugfixes that can be released ad-hoc instead of on a sprint schedule To clarify, I am not recommending that product owners devote large amounts of resources to tasks which in no way benefit organization objectives. It’s simply a recognition that indirect benefits to objectives lead to success in certain types of organizations. Strategic stories are an intentional investment in greater future value (such as cross-training), although it’s likely that the story could provide a more immediate value to the organization as well. Find ways to regularly invest in maintenance The last key principle faced by new product owners is learning how to please stakeholders while still investing in maintenance. This can sometimes be a challenge because many product owners will get a slap on the wrist if they spend too much time or money on maintenance, and not all maintenance plays are equal. However, I always recommend a portion of each sprint is dedicated to maintenance. There are different types of maintenance, so be sure to find the maintenance tactics that fit your organization’s needs. I’ll highlight a few valuable maintenance story types that have proven effective time after time. Tests Tests almost always are a valuable play. Even for a product that truly requires a higher caliber of quality — where testing is likely a requirement of the product delivery — it is common to discover there are things that lack regular testing. Tests can mean anything from writing automated unit tests for code to walking through the onboarding process to find points of friction. Unit tests make sure untouched features aren’t broken by unrelated changes. End-to-end tests take more time to perform but ensure that the most valuable processes always work. Manual tests, popular for proofs of concept, save the time required to automate tests but take longer to perform, matching a lesser initial investment to the likelihood that something will not be tested often. Manual tests can also be more valuable than unit tests for things that constantly change since overly-specific tests tend to discover more problems with the tests than they do the thing being tested. Find testing methods that work for your product, and don’t forget that processes need tested regularly as well. Support bucket The concept of a support bucket is one of my favorite strategies. It communicates to a team that things worth building are worth maintaining. Refining products regularly shows team members their work is valuable. In cases where there is a strong bug reporting system, it is likely that the team struggles to resolve bugs as fast as they come in. A support bucket is a way to maintain a position of strength. When your team is on top of their bugs, creating a support bucket addresses incoming bugs faster and keeps them from dominating a future sprint. If bugs don’t come in as much as expected, it creates a vacuum that allows team members to define their own work. Many developers relish the opportunity to take two days to refactor code that works but is confusing. However, this is something that can only be done when there are already good tests to ensure it doesn’t cause more problems than it solves. Pre-emptive customer engagement One of the least-used but highest-value maintenance tasks is pre-emptive customer engagement. If bug reports aren’t coming in, that can mean you’ve created a high-quality product! It can also mean you aren’t hearing about difficulties encountered by customers, or customers aren’t adopting product updates for some reason. When user stories are created, it is probably easy to identify real customers to whom this user story will apply. Talk to those customers and ask them how they are using the new features. How to prioritize user stories in Agile All the talk about priority, strategy, tactics, and maintenance is great, but how do I balance ALL of that when assembling a sprint? Much of this depends on the leadership and the number of stakeholders you’re dealing with. Organizational politics can send blue-sky planning into a tailspin. In the real world, you have to not only do good work, but make sure that work gets seen by the right people, and that they are impressed. If your organization’s leadership prioritizes innovation and “shiny things” This is a sign that part of your work is about the ideas and the culture shift. This is a completely valid position for a leader. There are many political advantages to trying new things and building a culture of innovation. It is a great way to gather information for refining organizational strategy. In cases like these, the following breakdown of average sprints works well: 50% strategies of short-term investment that can get thrown out or create a culture of innovation 25% tactics supporting a long-term organizational strategy 25% maintenance Applicable tactics/strategies: POCs, specialized efficiency, regular alpha/beta releases, prioritizing common use cases first, maintenance offering innovation affordances If your organization’s leadership prioritizes long-term strategy, stability or predictable growth This is a sign that part of your job is about giving customers confidence in your products. This is not an anti-innovation position, rather it is a recognition that you are in a position of strength in your market. In cases like these, the following breakdown of average sprints works well: 50% tactics supporting a long-term organizational strategy 25% strategies to shape the team 25% maintenance Applicable tactics/strategies: cross-training, prioritizing most complex use cases first, maintenance improving product stability, improving documentation, support buckets, external liaisons Closing If you’re a product owner or your organization is beginning to adopt Agile, take a look at your organization to find out what your stakeholders truly care about. While every organization is unique, these factors are universal. Tailor your strategies to your team and your tactics to your objectives. Most of all, create balanced sprints which deliver constant progress toward your goals. Analyzing urgency, importance, and size can lead to solid prioritization that drives team success.
In today’s age of digital transformation, companies have had to change the way they test software. Teams performing 100% manual test scripts find it very difficult for the testers to keep up with development. We often see developers either having to slow down development so the test team can keep up – or developers and others have to jump in to help get the testing completed on time. In either case, there is a negative business impact and reduction in test coverage. Testing trends As the reality of software testing has evolved, here are the trends we are tracking for 2021 and beyond: Artificial intelligence and machine learning test tools Modern web applications are highly dynamic. Developers are continually modifying and introducing new features to increase quality and meet the needs of stakeholders. But rapid change requires maintainable and reliable tests. Enter artificial intelligence (AI) and machine learning (ML). These technologies enable tests to detect changes in the application and adjust accordingly. Less time fixing tests leads to more time finding critical defects, which ultimately results in higher-quality software. Shift left toward testing at the integration layer In the waterfall model of the software development life cycle, much of the testing happens near the end (on the right end of the spectrum). Agile development, on the other hand, incorporates testing throughout the entire process. As a result, many testers are shifting left (i.e., performing tests earlier). In particular, testing at the integration layer with automated API tests will continue to pay dividends as release cycles shorten and microservice architecture is implemented. Performance & load testing While testing various aspects of performance has always been an essential part of software development, there is a renewed interest in load testing in particular. Due to an increasing number of open-source tools (i.e. Locust, Gatling) and a move toward cloud-hosted solutions such as LoadNinja and BlazeMeter, load testing is becoming more accessible to non-experts. Additionally, the ability to integrate performance tests into the CI/CD pipeline provides continuous feedback on how releases will perform under expected user loads. Look for load testing to grow this year and beyond. “Low Code/No Code” tools Building coded test automation frameworks, though worthwhile, requires a large investment of time, skill, and money. This can lead smaller companies to think of test automation as beyond their resources. Yet, the recent advent of powerful new testing tools requiring minimal or no coding has shifted the balance. With easy-to-use open-source platforms like the Selenium IDE and TestProject (along with paid products like Mabl), automation of web and mobile apps, as well as APIs, is no longer out of reach. While these tools are no replacement for fundamental testing knowledge and skill, they can certainly lower some of the barriers to implementing test automation. To Selenium & beyond For years, Selenium WebDriver and its derivatives have dominated the market in web and mobile user interface testing. With the advent of Selenium 4, this powerful framework isn’t going anywhere soon. However, a number of newer players are making themselves known in this space, including TestCafe, Microsoft Playwright, and Cypress. Each of these frameworks have their own advantages and disadvantages, and it is becoming increasingly important for organizations to be able to evaluate which option best fits their unique needs. Looking forward Testing is an integral part of the business process and any digital transformation. Skillfully implemented, these testing trends are contributing to increased efficiency, reduced costs, and better software. Increased test automation, AI/ML tools, greater technical proficiency, and shifting left all hold the promise of delivering greater value to the organization. Companies overwhelmed with testing should work with experts who understand best practices and what type of testing is right for what scenario. If you’re looking for assistance or would like to learn more about our software testing capabilities, contact us today.
In our previous article, Testing in the DevOps Pipeline, we discussed release pipelines and the automated test cases which help make them a reality. We started with these topics because they touch on some of the more exciting and new ways that adopting DevOps methodologies impacts the entire software development lifecycle. Being able to automatically build, test, and deploy code changes at the touch of a button has transformed many historically-more-manual processes, like testing. In this brave new world of automated tests and deployment is there still room for more “traditional” testing practices? And, does having more automated testing lead to more frequent and higher quality releases? Not a Magic Bullet While the ability to rapidly repeat tests for each new build — or on a schedule — provides a high level of confidence in system stability, automated testing should not be seen as a magic bullet. Automated tests provide their greatest return on investment when they are reserved primarily for regression and smoke test suites while leaving most new feature testing manual. When determining which test cases should be automated and which should be left manual, the upfront cost of automation can’t be ignored. In the time it takes to go through the entire process of creating a new automated test for a single new feature, a manual tester could have verified multiple new features. With the upfront cost of time in mind, we recommend automating test cases that are either mission-critical or that are going to need multiple repetitions and keeping one-off feature testing manual. A hybrid approach While many within the industry believe more automation leads to faster releases, we find that a more “hybrid approach” often leads to faster and higher-quality releases. In fact, some of our clients that have managed to make multiple daily deploys have done so by committing the resources needed to fully adopt the hybrid testing approach. These clients have established a separate Scrum team solely dedicated to the development of testing frameworks and automated regression and smoke test suites. Having a team dedicated to this work increases confidence in the overall system stability with each new build, freeing the feature teams to concentrate on new features. As a result, the testers on the feature teams have the overall system stability they need to confidently push out new builds and not worry about attempting to automate incremental releases. In doing so, the teams are able to get new builds out much more quickly, deploying multiple production changes during work hours each day. Additionally, it can lower the cost and technical background needed for testers on the feature teams; instead of an SDET on each team, we can have a few SDETs on the framework team and primarily manual testers on the feature teams. The test automation pyramid As part of evaluating the cost of automation, another model to keep in mind is the test automation pyramid. This model is a way of visualizing which aspects of the system under test should be automated the most. Source: MartinFowler.com Unit tests constitute the base of the pyramid and receive the most automation, while User Interface (UI) tests receive the least. There are three main metrics that collectively determine how much automation should be used for a given area of the system: How long it takes to write the test How long it takes to run the test How much effort is needed to maintain the test Unit tests are relatively quick to write, extremely fast to run, and are reliable tests that should be updated as part of the development process. On the other end of the spectrum, UI automaton can take quite a while to write. Running tests that require WebDriver instances to open browsers and navigate web pages is a slow process, and the nature of UI automation is particularly brittle and requires a lot of maintenance. For these reasons, we encourage an abundance of automation at the unit and service layers while UI automation should be reserved for rarer occasions that justify the upfront and upkeep costs. However, while we discourage being overly zealous with automation at the UI level, that doesn’t mean it shouldn’t be tested; this is where our traditional testers once again have a crucial role to play. Where do testers test? Answer: everywhere In our previous article, we talked about the benefits of release pipelines and the automated testing that gives us the confidence needed to use them. While these practices have been incredibly helpful, automating and streamlining the deployment process, there’s still often a need for manual verification. As a result, most of these pipelines do not push directly to a production environment. A common pattern illustrating the typical flow of a build from its creation to final deployment in production might look something like this: Dev -> Testing -> Staging -> Prod. Each of these stops in new environments serve as manual gates where someone (usually a tester) must sign off on the build before allowing it to continue to flow through to the higher environments. Let’s take a look at how a new build might flow through this process. A build is created by a developer and tested locally in a dev environment. At this stage, the developer is expected to execute unit tests and perform additional cursory testing before pushing to a testing environment. This deployment process should be automated via the help of a build server (e.g. Jenkins) and include automated smoke tests that ensure basic stability of each new build. Once deployed to the test environment, the tester can now run any automation that is not part of the pipeline and begin the process of manual verification. These intermediate environments between the developer’s local environment and production are crucial to the pragmatic approach of blending manual and automated testing. The automated smoke tests in the pipeline during deployment to the intermediate environments gives confidence of general build stability, while the feature-specific manual testing gives confidence to the new feature itself. Should any of the automated tests fail during deployment, or if the tester finds a bug in an intermediate environment, an issue is raised with the developer and the process starts again from the beginning. Once the feature team tester has signed off on the release, the build can continue to a staging environment. Staging environments mirror production and, as the name implies, are used to stage changes before they finally go live. In blue/green deployment environments, the staging environment will be the currently inactive server cluster. The release build is deployed to the inactive cluster, and we are provided with a final opportunity for manual testing before going live. If everything continues to go smoothly in staging, the clusters are flipped, the inactive servers go live, and the previously-active production servers become inactive and ready for use as the next release’s staging environment. Even after the build is live in production, the job of the tester is not yet finished. Final manual testing of the build and monitoring for any errors or outages should be standard post-release procedure. Isn’t this risky? Monitoring your system in production for outages and increased error rates is a critical component to successfully deploying more frequently. While most of this article has explored the benefits of manual testing new features, the potential risks of not automating these tests should be acknowledged. Any features not included in the automated regression tests are features that could possibly break with future builds. Monitoring for outages and increased error rates is a way to mitigate the risk of any issues going unnoticed. This idea of increased risk might make some uncomfortable, but the unfeasibility of full test coverage gets at the heart of what makes a good tester. Testers have long understood that it’s impossible to exhaustively test a non-trivial system, and therefore the role of the tester is to analyze risk and come up with the most pragmatic test plan. There is inevitably a tradeoff between the risks of missing a bug and the time-to-market needs of the organization. Expecting to automate every test will cause unnecessary delays in the process and may even decrease the overall quality of the system. Now more than ever The skills testers have cultivated for decades regarding risk analysis and test coverage are needed now more than ever and should not be ignored in an attempt to automate everything. By extending those skills to the decisions about which tests to automate, it’s possible to create a test plan that pragmatically blends the best of both automated and manual testing and that mitigates potential risk with post-release monitoring.
What will the future hold when it comes to digital transformation? We don’t have a Magic-8 ball or special spidey sense, but our team does anticipate sizeable change. We asked a few of our team members what they thought based on their work and personal experiences. Here’s what they’re envisioning for 2021 and beyond. The cloud gains more ground I expect cloud-based business application platforms such as Dynamics 365, Salesforce, ServiceNow, and Workday to drive significant digital transformation within modern workplaces in the next year. Following on the heels of many core infrastructure services moving to the cloud — such as email, servers, files, and data — the next major lift for many organizations will be to modernize and automate their core business processes. I anticipate areas like finance, HR, production, and other critical business operations and workflows will be the next major shift to using cloud-based business application platforms. Moving away from legacy, on-premise solutions is not always a simple task, but in doing so, employees can then work remotely without being tethered to an office environment. Greg Deckler, Vice President, Cloud Services Connect with Greg on LinkedIn Remote workers collaborate differently The work-from-anywhere model has been proven to work and it will continue. However, right now, a Zoom meeting is about the extent of what most people see as remote teamwork — and we all know those can be exhausting. I predict greater adoption of tools like Miro and Mural. These online workspaces allow for active collaborating and co-creating in real time. The need to move quickly and keep pace with digital transformation will require these types of tools, and those who know how to leverage them, to make the most of a remote team’s time together. Doug Scamahorn, Solution Director, UX Design & Innovation Connect with Doug on LinkedIn Cookie compensation I think 2021 will be the year when businesses and marketers confront the pending deprecation of the third-party cookie. Google is driving the industry towards new solutions for retargeting and attribution following the announcement that Chrome will cease to support third-party cookies in 2022. While industry players debate over a long-term replacement, expect to see a scramble to shore up first-party data in the meantime. At a tactical level, this will look like increased pushes for “registered” online experiences where users must explicitly identify themselves, as well as the integrations that power these points of data collection. In the background, businesses will be pushing to connect the dots between online and offline touchpoints using a variety of identifiers, from email to devices to data from “walled gardens” like Amazon, Facebook, and even Walmart and Target. Companies may opt for a CDP (consumer data platform) solution on top of their existing data stack to manage data points specifically for targeted marketing campaigns. When reporting on campaign success and attribution, analysts may need to adopt new tools and strategies for managing “fuzzier” readouts on customer behavior and journey identification. Amy Brown, Solutions Director Connect with Amy on LinkedIn Augmented reality becomes actual reality As mobile processing and bandwidth progresses and matures, we can expect more augmented reality (AR) apps to provide visual assistance in a huge range of applications. I fully expect we will see vehicles with heads-up displays, smart glasses (remember Google Glass?), and other clear displays to be adopted by more companies and thus, individuals. Visual processing in itself is gaining in popularity. Retailers like IKEA are already using AR with their IKEA Place app to enable customers to “see” furniture in their spaces. Microsoft’s recent HoloLens release is a good example of where we’re headed. Jeremy Keiper, Competency Lead Connect with Jeremy on LinkedIn B2B marketers will get more creative There’s always been an understanding that marketing is both an art and a science. Over the last decade, marketers have leaned into the science. Data provided marketers with information about customer behavior that was never available before. Even before the pandemic, B2B marketers were relying heavily on digital channels to engage customers. But pandemic office closures caused marketers to rely on channels like email, webinars, social media, and search engine marketing (SEM), in an attempt to reach prospective buyers who were now working from home. And they had to get creative. Marketers had to be willing to test new ideas and try things that haven’t been “proven,” and to think creatively about how we connect with and engage prospects and customers. I anticipate this to continue, and marketers will use customer data to make sure they understand consumer goals and motivations, then get creative about how to reach out and connect. Kristin Raikes, Sr. Director of Digital Strategy Connect with Kristin on LinkedIn Looking ahead Thinking about the year ahead, we do know that even after offices reopen and things get back to “normal,” the new “normal” will look different than it did before. If people continue to work from home or prefer to engage with brands virtually versus physically, then technology will have to adapt. Are there any major trends not listed above that you think will be a key to digital transformation for this year? If you have questions about specific trends, you can also connect with our team via their LinkedIn profiles above. Our consultants and team members work with clients to improve, streamline, and create actionable change. We create exceptional customer experiences by leveraging data insights, experience design, and technology to transform the way you connect with your customers. Interested in learning more? Let us know, or sign up for our newsletter to get to know us.
Are you currently using a DevOps pipeline, or moving that direction? Are you looking to integrate testing into the release pipeline? In this article, we’ll briefly discuss DevOps pipelines and give some hints on how you can test in such an environment. Joining the DevOps moment As you participate in the never-ending dance of software development and releases, you’ve probably come across the term “DevOps.” Though it sounds like an elite team of special operatives, the portmanteau is more accurately described as a movement to blur the lines between the traditionally-siloed worlds of development and operations. In an on-premise software-deployment environment, isolating development and operations has made sense. Operations can focus on releasing the software to the appropriate customer through a medium that users can consume. The developers are 100% dedicated to adding new features and making sure they don’t break the build, and they don’t need to worry about the details and demands of delivering to individual customers. Yet software is increasingly moving “to the cloud.” With cloud apps and services being deployed to locations hosted by software companies (rather than users), the load of operations is eased. Simultaneously, this movement is eroding the argument that development and operations should continue to be siloed. “DevOps pipelines” are a powerful tool when uniting the two functions. With the adoption of continuous integration/continuous deployment (CI/CD) with tools such as Jenkins, Travis CI, Gradle, and Bamboo, you can put together a sturdy, reliable pipeline to handle everything from initial build to release. Developers can now push to a code repository and then watch an entirely automated process release that code into your desired environment. Common Pipeline Components and Corresponding Tools Is quality going down the drain with a pipeline?? If the software-release process is handled by an automated pipeline, is there still the opportunity to test the code before it’s released into the wild? Do I still need testers? Absolutely! Adopting a pipeline does not mean software testing should be sacrificed to streamline the release process. The very basis of DevOps pipelines is to make the release process reliable and easily repeatable while reducing the chances of errors, delays, and miscommunication. All of that contributes to higher quality products, right? New or existing automated tests can be easily and smoothly integrated into the pipeline. Unit tests, smoke tests, and regression suites can all be run as part of a release pipeline. The testing is not limited to API tests, either. CI tools such as Jenkins or third-party testing platforms like Mabl can run your UI tests in “headless mode” to avoid interrupting team members’ work whenever the pipeline runs. “Continuous testing” is a significant opportunity with CI/CD. Let’s walk through a sample scenario. Say several developers have reviewed and approved a code merge to the develop-branch of a code repository. Once the code is merged, the CI/CD tool picks up the changes in the code repository and tells the build server to build the code. If the build is successful, the code is ready to be deployed by another tool. Unit tests (provided by the developers) should be run by the build server as a sanity check. Then, a suite of automated software tests can be kicked off against the successful build as a further check that the code changes have not broken existing features. If any of these steps fail, the pipeline will notify the responsible parties that it’s a breaking build, and deployment is terminated. A successful run will result in deployment to the dev environment. The pipeline is flexible The scenario above describes only one potential setup. The multitude and variety of available tools allow for incredible flexibility! If manual tests are required, the pipeline can be configured to build the code and then wait until the appropriate user approves the deployment. Does this defeat the point of using a pipeline? Not at all! The pipeline keeps all of the steps in order, and any part that should be automated in your specific system can be automated. Steps won’t be forgotten or completed out of order. Logs and results are kept in the pipeline for future reference and easy visibility into the status of releases. So where do I start? You could certainly begin by compiling a pipeline from multiple tools. Many of the individual tools have excellent documentation with notes on how to integrate with other components. If you are new to creating a pipeline, however, we’d recommend checking out GitLab. It is a single tool that can handle source code management, continuous-integration/continuous-delivery, builds, and releases. With GitLab, your pipeline can be built & managed in a single place. Your software can be tested on the pipeline using your automated test suites stored in GitLab, or by running a command to kick off tests living somewhere else. The source code management is based in Git and is very similar to GitHub, with one prominent difference being the use of “merge requests” rather than “pull requests.” Same basic concept, just different terminology. Example of GitLab’s pipeline diagram, displayed while running Another popular CI/CD tool is Jenkins, an established and well-supported automation server. Like GitLab, Jenkins provides a UI for pipeline dashboards and interaction. Jenkins, however, is not an all-in-one CI/CD solution. While GitLab hosts the resources it uses in the pipeline, Jenkins is more of a coordinator that calls different pieces of your pipeline from wherever they are hosted. It is an exceedingly useful and time-tested tool for scenarios where you want to utilize several different platforms in one pipeline. Jenkins can be configured to build, test, and deploy code once it is pushed to a repository. Jenkins dashboards can show multiple projects in one view If the pipeline fits… As support for DevOps continues to grow, so does the number of available tools for adding automated tests into your pipeline. A well-built pipeline is incredibly flexible and should never require teams to reduce software testing, even if some of that testing is manual. Tools such as GitLab and Jenkins provide an excellent starting place for building a sturdy software-release pipeline as you begin your DevOps journey. So, go ahead. Put on those coveralls and grab your wrench. It’s time to build your software release pipeline and join the DevOps movement. And if you’re struggling with where to start or need assistance, don’t hesitate to get in touch. We’re always here to help!
The financial industry has faced waves of changes over the last two centuries. Emerging nations, the American gold rush, the power of the stock market, and even the Great Depression have all shaped how banking works and what consumers expect from their banks. Notably, from 2015 onward, bankers began to list technology risk among their top five concerns1. While these changes have increased banking access and options for the average consumer, they also brought in more tech-savvy competition and greater regulatory scrutiny as heaps of data have become digitally accessible. Ironically, the very technological disruption that has so upended the financial industry will also be what brings new opportunities for growth and increased wallet share. This is especially true with advanced data tools such as artificial intelligence (AI) and machine learning (ML); according to one source2, 83% of early AI adopters have already achieved substantial (30%) or moderate (53%) economic benefits. In light of the benefits machine learning can bring, we’ve compiled four major areas where we’ve seen ML used to reduce costs, increase revenue, and mitigate risk for banks. 1. Acquire new customers Gone are the days where marketing was limited to just a few channels; now banks must maintain an omnichannel presence in order to reach younger consumers who may not listen to the radio or watch TV. Acquiring new customers means reaching them where they are with messaging that’s highly targeted and relevant. Yet as margins get slimmer and budgets are squeezed, reaching these consumers with targeted messaging without breaking your budget can be a challenge if you’re not careful. How machine learning can help Making the most of your marketing involves making the most of your data. Machine learning can help you identify trends in consumer behavior and interests, which can help you deliver the right marketing messages in the right channels at the right time. ML opportunities Identify which existing bank customers will buy another bank product Score your commercial leads based on risk, profitability, and probability to close ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue 2. Deepen relationship with customers Digital transformation has affected every business in profound ways, especially in the area of reaching customers and managing the customer relationship. Today’s users want a more seamless experience, more targeted messaging, and on-demand access to information, and they’ll move to the bank that can meet their digital demands. How machine learning can help AI and ML allow you to combine your leadership’s decades of experience with customer engagement data. So not only will you have a gut check of what customers want, you’ll have quantifiable data to back it up. Which means your sales and customer relationship initiatives will ultimately be more effective at targeting customers ready to upsell and at cross-selling more of your products to hungry buyers. ML opportunities Identify high-value customers early and engage with them differently Predict the likelihood of a customer taking their deposits elsewhere Identify which disputed purchases are legitimate Project a customer’s lifetime value for those with a limited history with the bank 3. Reduce Financial Risk Consumers are becoming both more credit averse and less credit worthy, which extra pressure on banks and credit unions of all sizes. On top of that, banks face increased risk caused by data breaches, fraudulent activity, and increased costs brought on by regulatory compliance3 . These challenges make maintaining adequate cash reserves more difficult than ever before at a time of increasing market volatility. How machine learning can help Machine learning can give you the insights needed to reduce your overall financial risk by helping you identify fraud and financial liabilities early – so you make and keep more of your profits. ML opportunities Clarify the liabilities on your balance sheet and determine which are the greatest risks Detect fraud and misuse of the company’s finances Project cash reserves to reduce excess bank cash GET THE USE-CASE WORKBOOK: The ultimate guide to machine learning use cases for banks 4. Optimize investment offerings The investment management arm of today’s banks continues to change rapidly as industry challenges increase. Today’s investment managers deal with increased market volatility, capped organic growth, and increasing fees. Because of these challenges, they struggle to keep up with shifting expectations of clients who demand a better investment turnover. How machine learning can help Machine learning can be used to detect patterns hidden in a bank’s historical investment data combined with external financial data. These patterns produce actionable insights that can increase the accuracy of key investment decisions. ML opportunities Match securities to investors based on trade history and market conditions Dynamically price securities based on competitive offerings, market saturation, and risk profile See how one institution used ML to predict their deposit customers' likely deposits on a daily basis, freeing $40,000,000 in excess cash reserves The up-and-coming (and existing) opportunities for financial institutions to win with ML are staggering. One source estimates that advanced data initiatives like AI and ML are predicted to boost overall business profitability by 38% and generate $14 trillion of additional revenue by 20354. And while it’s true that digitally savvy industry newcomers may take advantage of these trends faster than their legacy peers, legacy banks and credit unions hold something the younger competition doesn’t: mountains of historic data that, when mined for insights using AI and ML, can give them a leg up in retaining customers, increasing their wallet share, and reducing their overall financial risk. To adequately leverage these four opportunities, banks will need to embrace the very technology disrupting the industry. Banks that view their data as one of their most important assets and embrace AI and ML to create new insights will likely see growth, whereas those who don’t will struggle to keep up. 1) 2015 Banking Banana Skins Report 2) 2017 Deloitte 3) 2017 Financial News 4) n.d., Accenture
Curious about automated user-interface-level (UI) testing? That’s good, curiosity is where it all begins, and you’ve come to the right place. The next step can be the most daunting. The purpose of this post is to provide some high-level strategies and encouragement to get you started on your journey. Let’s get a couple of things on the table to avoid potential confusion. First, our central focus will be on automated UI-level tests. Some of the concepts and ideas will naturally bleed over into code-level unit tests and service-level integration tests, and we’ll discuss these aspects of testing with UI features in mind. Second, these high-level ideas come from our own experience and do not always translate to your unique business processes, operations, and technology needs. As Kaner, Bach, and Pettichord reiterate in Lessons learned in Software Testing, “. . . the concept of best practices doesn’t really exist devoid of context.” Now that you know what you’re in for, we hope that you’ll find conversation starters, thought provokers, or otherwise-useful nuggets to kickstart your transformation into automated UI testing. Taking the plunge: Start with expectations and create a baseline What do you hope to get out of an automated UI effort? Who is going to be writing the tests? How frequently do you envision them running? Who is going to consume the output and reports generated by them? The goal here isn’t to have an exhaustive plan or answer all of these important questions right away. When you’re in the process of undertaking new business practices or adopting transformative technology, it is important to have some sort of starting point or baseline to compare subsequent changes. Committing thoughts and ideas along with notations of your business’ current testing environment to paper (physical or digital) can serve as that baseline. Which technology/tool am I supposed to use? It’s best to approach this question with an openness to all the different ‘tech flavors’ and be unafraid to make significant changes. This will directly impact initial expectations, particularly with regard to the skills necessary to author the tests and any supporting code. It’s also important to think about your own SDLC as a whole in this stage: are you considering transitioning to BDD? Is there already a solid process for deploying into which you need to mesh? How frequently are changes being pushed? Categorizing your options for testing technology will help answer some of these initial questions. Categorizing technology options SerenityBDD and Cucumber unlock the gherkin syntax for describing behaviors, but require coded hooks in order to become executable. Selenium WebDriver and Appium open the door to controlling browsers, mobile devices, and desktop apps with the most modern programming languages, but requires a unit testing framework in your language of choice to write the tests. Record and playback tools, such as Katalon and TestComplete, boast “codeless solutions,” although you may end up in a situation where you are constantly re-recording scenarios depending on the app under test and the release cycle. This is by no means an extensive list of everything out there. As you stumble across others in your research, categorize additional options with those mentioned here. I’ve picked a technology and someone to work with. Now what? One of the most common mistakes we see is a lack of support to generate an enormous number of tests to convert a manual regression completely over to an automated one. Beyond the strong reminder that automated tests are not a complete substitute for manual tests, this can lead to a casserole of difficult-to-maintain artifacts that are constantly breaking the build. Take it slowly. Work through those questions in the sections above within the scope of just a few tests. You will thank yourself in the long run if you’ve dealt with some of the pain points with a limited scope before trying to ramp up the volume. For example, if working with a web app, start with simple navigation tests, i.e., confirm that you can navigate to three different pages, including the homepage by checking for page titles. Keep these tests as current as possible while changes to the application are in progress. Focus on how and when you run these tests. Consider how you might add more tests. If the thought of more tests seems too painful, consider the alternative of breaking down the process of conversion to even smaller steps. Useful example of taking the plunge into automated UI tests Let’s say that you’re a test manager at a company that builds technology solutions for healthcare providers. You’ve decided that you want to start experimenting with automated UI testing for one of the six different web apps currently under your purview. After considering the makeup of the whole team responsible for that app (BAs, scrum masters, application developers, testers, etc.), you’ve settled on the enterprising individual who will give this a shot. You discuss the current development and deployment processes, and, given the background of the project in question, decide that a Java project built on the command line best suits your technology and business process needs. After working through Selenium WebDriver tutorials, your test writer comes back to us with a project containing two tests: one that confirms that upon navigating to the homepage URL, the page title is accurate, and another that confirms the page title of the login page. Over the next few weeks, you focus on running these two tests frequently, ironing out your own build process, and working with the application development processes to determine when and how our tests execute. We also work through a couple of different reporting methods while figuring out how to present, discuss, and store that data. When ready, we expand our two tests to ten and (once again) iterate on our processes and goals. We continue this cycle until we’ve got solid coverage with reliable tests and processes. Armed with our experience from the first app, we turn our attention to the next one. Follow-up post: Automated UI Tests: Taming the Tangle
Product design (UX/UI design) is becoming one of the most important roles in the tech industry. Designers are under pressure to accelerate product development and reduce the time, effort, and cost spent. We’ve been there and understand what it’s like. This eight-step process can help you speed up development and achieve all of the above. Use it to understand your product goals and customers, and also to collaborate with the entire team to discover problems, ideate, test, and validate potential solutions. 1: Understand the product and set up a strategy Familiarize yourself with product vision and strategy Your first step is to understand the product’s “big picture” and the vision behind it. You’ll need to answer the following questions: What problem the product is solving? What value is it delivering? Who are the users? Who are the competitors? Who are the partners? Conduct stakeholder interviews In order to answer the questions above, you’ll need to meet with project stakeholders. In your interviews with them, ask for the following: Mission statements, strategy documents, organizational or team structure charts, etc. KPIs (key performance indicators) − to help you understand the most important features in the product by understanding what success looks like. Previous research they’ve conducted − including user research, market research, competitor analysis, etc. Create a roadmap Roadmaps facilitate team collaboration and clarity around priorities. Create a roadmap to help your team better understand: What is the ideal state of the product? What is the current state of the product? What steps need to be taken to meet the end goal and how should you prioritize them 2: Conduct user research User research is one of the most important steps in the product-design process. All of your team’s hard work, time, and money will be worthless if you end up making a product that no one wants to use or that can’t compete in the market. Here are some research methods to help you better understand your users and competitors: Establish user personas: A persona is a hypothetical character created to represent a major user group that might use your product in a similar way. Create user personas to better understand your product’s users and their needs, goals, and pain points. To create user personas, use the data you gathered in stakeholder interviews, conduct surveys, interviews, ethnographic research, etc. Create a user journey map: A user journey is the path a user takes through your product to achieve a certain goal. User journey maps show users’ thoughts and feelings while using the product or going through that journey. This makes it easier for you to identify areas for improvement since you see when your users are annoyed, confused, or happy. Develop your user journey maps using the feedback received through user testing, observations, data received from the support team, etc. Conduct a competitive analysis: Conduct market research or a competitive analysis to learn what other similar products or companies are doing and analyze how their problem/solution could map to your own problems. 3: Define your information architecture Information architecture helps you organize and structure the content of your product in a way that your users can find what they are looking for easily without having to go in circles. Create this structure for your product through any of these methods: Site maps Flow charts Card sorts 4: Discover problems Discovery is an important phase that allows designers to work with the entire team to define and research problems identified in steps 1-3, as well as gather enough information and initial direction on what to do next. Discovery will help you frame problems with all the evidence you need before moving to the ideation phase. 5: Ideate The ideation phase moves you from learning about your users and the problem to coming up with potential solutions. In this phase, gather together and come up with as many ideas as possible. The focus is on quantity, not quality. Some ideas may surface as the potential solutions to your problem. Others will end up in the reject pile. If carried out properly, an ideation session can lead you to find that groundbreaking solution that you and your users are looking for. 6: Perform user testing User testing gives you the opportunity to evaluate and validate your ideas with the users. At this stage, you’ll be able to gain deep information about your users’ behavioral patterns, preferences, and suggestions. Testing early during the design process allows you to prevent future re-design costs and to launch a user-friendly product. 7: Finalize the design With the usability testing complete, you can start updating the design according to the feedback you received. You will now design what the screens will actually look like and create the final UI through high fidelity wireframing and prototyping. 8: Communicate and collaborate Your last step is to share the design with developers and walk them through the entire user flow to give them the opportunity to review what needs to be implanted and raise any questions or concerns. Once the development starts, you might need to do any of the following tasks: Support developers : Provide guidance and answer questions about how things should look or work. Update: If there are technical limitations with implanting the design or new issues arise, get more user feedback and update the designs again. Review and desk check : When the development is completed and pushed to the test environment, review the work to make sure everything matches with your design. The benefits If you follow this process, you’ll be able to develop products with: More efficiency: Time, effort, and cost will be reduced by discovering and testing different ideas early in the process and moving forward with the solution that works best for both customers and the business. Higher customer satisfaction: Continuous research helps you understand and respond to users’ needs so that you are more competitive in the market, which helps you increase customer satisfaction remarkably. Accelerated development: Providing high-fidelity design and working closely with developers throughout the process prevents them from making changes and fixes that are avoidable (such as when mocks are not detailed enough or when they make the wrong assumptions), resulting in faster development. This high-level overview is a great starting point, but every organization and product has different needs. If you’d like to talk about how to improve your current product development process or how to establish a new one, contact us today.
There’s been a lot of hype about Robotic Process Automation (RPA). Headlines tell us we can transform our business process in as little as 12 weeks using RPA bots. Benefits are touted, velocity is promised, trends of growth are noted, and new jargon was coined: “Automation arbitrage, a term Gartner uses to describe the recalibration of human labor to drive business outcomes is one of the biggest enablers in this coming decade.” – Gartner, The CIO’s Guide to RPA and Introduction to Hyperautomation. Hype can be fun, but it doesn’t answer the very practical question, “Can RPA help transform my business?” This article will answer it for you and help you make an informed decision about whether RPA is right for your organization. Read on to learn the best-fit processes, work through a decision flowchart to determine whether your process is suitable for automation, and gather helpful considerations to keep in mind as you’re getting started. There are also links to demos and further resources throughout. First, what is RPA? Robotic process automation uses computer software (bots) to emulate a human worker interacting with digital systems. RPA bots automate repetitive tasks by interacting with software applications, just as humans do while working. In short, companies use RPA software to perform repetitive tasks that would usually be done by workers sitting at their computers. Bots can be programmed to work just like us − logging into and switching between applications, interpreting information, making calculations, and copying and pasting data. Bots can also process data, trigger responses, and communicate with other systems to perform tasks at a high speed without error, which enables organizations to effectively automate tasks, streamline processes, and increase productivity. Often conflated with artificial intelligence (AI), RPA is non-intrusive and does not require system integration. It sits on top of your existing system to perform business processes, using the same interfaces that humans use. And, unlike scripts or macros, RPA will not break every time there is a minor software update. Essentially, bots can work in two modes, attended or unattended. This provides flexibility to better meet specific business needs. Attended bots are typically targeted toward front-office activities and are useful when the entire end-to-end process can’t be automated. These bots are programmed to work alongside humans to complete processes that can pass data between bots, applications, and human workers, or complete specific functions within a process. Unattended RPA bots execute tasks and interact with applications independent of human involvement. Unattended bots can be triggered by events or scheduled. They will run until a condition is met. Read more: Jumpstart your business processes: Using hyperautomation to achieve speed and scale >> Which business processes are the best fit for RPA? An important thing to understand about RPA is that it doesn’t add value to every area of the enterprise. Forrester Research, Inc. counsels caution when considering this “shiny new kid on the block.” Therefore, careful consideration, selection, planning, and governance are crucial to the success of an RPA implementation. First, consider whether your process falls within or is similar to this sample list of business functions that benefit most from RPA: Finance and accounting − orders, claims, vendor management, accounts payable, and collections. IT services − software deployment, server and app monitoring, routine maintenance and distribution, batch processing, password reset/unlock, backup and restoration. HR services − data entry, payroll, time and attendance management, benefits administration, compliance, and reporting. Supply chain − inventory management, demand and supply planning, work order management, and returns processing. Next, ask yourself the following questions about the process you hope to automate (see the decision flowchart below for the entire process): Is it rules-based, standardized, with clear processing instructions or templates? Is it highly manual, repetitive, and prone to human error? Do transactions flow at a high volume and/or frequency? Is it well-documented, stable, and mature? Are there standard, readable electronic input types? The above sections represent the critical first step to determine whether automation is right for your process. Completing the exercise of the decision flowchart will make it clear whether you should pursue business transformation via process improvement initiatives, or RPA implementation. Benefits realized from RPA There is a reason automation is here to stay, and the sooner you implement RPA, the sooner you create a competitive edge for your business. RPA benefits include: Reduced costs − RPA can reduce processing costs by up to 80%. Improved economics, efficiency, and effectiveness through reduction of human error and the costs of duplicate effort, rework, and mistakes. Transformed and streamlined organization workflows. Increased compliance and consistency. Positive impact on operational metrics − reduced focus on non-value-add activities provides time for important strategic tasks and customer relationships. Improved customer service through agent access to readily available information and reduced manual efforts. Non-intrusive, seamless integration with existing enterprise systems, resulting in reduced implementation costs. Extremely scalable across business units and geographies; multiply bots and deploy more as you go. Improved processes − bots constantly report on their progress, so you can strategically improve processes by using operational and business predictability. How to ensure RPA implementation success Clear vision, comprehensive planning, and structured governance are critical factors in the success of any RPA implementation. Proposed changes must be well-defined by leadership, shared by IT and business, and communicated with the affected employees. Below is a list of success factors to keep in mind as your organization takes its first steps toward automation. Plan well − a common automation pitfall is lack of governance. RPA programs need centralized control and governance, including formalized methods and standards to ensure maximum benefits. Avoid working in silos − implementation efforts must be driven by collaboration between IT and the business and based on a clear vision from leadership. Start managing change early − your people strategy can’t be put off until deployment. The successful realization of benefits from RPA projects requires end-to-end organizational change management (OCM) that is adaptable to the size and complexity of the RPA endeavor. Communicate widely and frequently − throughout the implementation process, communication is key because bots will change how people do their jobs. In addition, some workers may fear job loss, so communication, transparency, and training can help them embrace this new frontier in business processes. Manage for, or eliminate, potential surprises − don’t forget to factor in the effects of third-party partnerships and applications. These are an uncontrollable factor of your business environment, so use care when including them in your automated process. Put process over tools − RPA is not only about rapidly developing bots. A robust governance structure, well-defined opportunity identification process, quality development, and reliable operations are more important than any particular tool. Stay objective − avoid implementing automation solely for the wow factor” Be sure you understand what you hope to achieve through automation and that you’ve considered the long-term costs involved. Manage expectations − bots are not the whole solution, and RPA is not a silver bullet; it should be viewed as only part of the automation strategy for the enterprise. You may need a broader strategy, such as system modernization, process transformation, and use of machine learning, to underpin a larger transformation effort. Keep in mind that RPA is not a set-and-forge” process. New bots will need consistent oversight until they are fully trained. They’ll also require ongoing management, especially when there are changes in the system or environment. Summary Now that you have the facts, you can decide whether RPA is right for your organization. And if you need help, our team is experienced in leading Robotic Process Automation programs in both advisory and implementation capacities. Our solid partnerships with Microsoft and UiPath, a top RPA vendor according to the 2020 Gartner RPA Magic Quadrant, help us offer the most appropriate technologies available for your organization’s needs. RPA services that we offer include: Advisory/assessment Set up Center of Excellence (prioritization of applications) Construct team Evaluate tools Evaluate Book of Work (project work having funding associated with it) Process mapping and analysis Implementation Develop and upgrade bots Create run books for bots (procedures for handling tasks, contingencies, and troubleshooting) Perform monitoring and management (steady state) Contact Fusion Alliance to discover if RPA is right for you.
Executive summary The credit card industry is becoming more complex. Advanced loyalty, targeted offerings, unclear rate conditions, and many other factors can often make it difficult for banks to identify the right customer. Ultimately, the financial services firms that will succeed in this environment will engage the right customers with the right message at the right time. Market leaders will be those who can accurately forecast the revenue and risk for each prospective and existing customer. While the credit card environment has changed, the analytics and modeling techniques have largely remained the same. These models are highly valuable, but do not offer flexibility to evaluate granular and complex customer behaviors incumbent in a financial services firm’s data and other public and private data sets. Machine learning and deep learning (collectively, machine learning) change the paradigm for predictive analytics. In lieu of complex, expensive, and difficult to maintain traditional models, machine learning relies on statistical and artificial intelligence approaches to infer patterns in data, spanning potentially billions of available patterns. These insights, not discoverable with traditional analytics, may empower the financial industry to make higher-value, lower-risk decisions. In this brief article, we discuss three potential opportunities that Fusion expects should add high value to the financial services industry. Advanced analytics for banking Machine learning uncovers patterns in complex data to drive a predictive outcome. This is a natural fit for the banking industry as firms are often working with imperfect information to determine the value of incoming customers. How it works: Traditional models vs. machine learning Credit scorecards represent the basis of most credit card issuance decision making. Whether a firm leverages off-the-shelf models or applies bespoke modeling, Fusion expects the following is representative of a credit scorecard: In the aggregate, these models are highly valuable. But on a per-applicant basis, patterns and details are lost. In machine learning, we can explore detailed and expansive public and private data about segmented applicants for marketing purposes in real time. For example, we can supplement our existing models with data that can be used to segment potential customers such as: Regional FICO trends Educational attainment Social media sentiment analysis Mortgage and equity analysis Much, much more Machine learning can apply artificial neural networks to uncover patterns in your applicants’ history across millions of data points and hundreds of model statistical training generations. When detecting these patterns, machine learning models can uncover risk in approved applicants and value in sub-prime applications. For example, by exploring existing customers, machine learning could potentially reveal that applicants with low FICOs but high educational attainment for a specific city suburb have historically resulted in minimal write-offs. Conversely, a potentially high FICO applicant may have recently moved into a higher-net-worth neighborhood, requiring a high expenditure on a financial institution’s credit lines, resulting in repayment risk. Ultimately, your customer data can tell a far richer story about your customers’ behavior than simple payment history. Machine learning opportunities Financial services firms can gain more insight and capitalize on the benefits of machine learning by applying their marketing dollars towards customers who are more likely to fit within their desired financial portfolio. Lifetime customer value for customer with limited credit data Currently, credit score is determined based on traditional data methods. Traditional data typically means data from a credit bureau, a credit application, or a lender’s own files on an existing customer. One in 10 American consumers has no credit history, according to a 2015 study by the Consumer Financial Protection Bureau (Data Point: Credit Invisibles). The research found that about 26 million American adults have no history with national credit reporting agencies, such as Equifax, Experian and TransUnion. In addition to those so-called credit invisibles, another 19 million have credit reports so limited or out-of-date that they are unscorable. In other words, 45 million American consumers do not have credit scores. Through machine learning models and alternative data (any data that is not directly related to the consumer’s credit behavior), lenders can now directly implement algorithms that assess whether a banking firm should market to the customer segment, thereby assigning customer risk and scores, even to credit invisibles (thin-file or no-file customers). Let’s look at a few sources of alternative data and how useful they are for credit decisions. Telecom/utility/rental data Survey/questionnaire data School transcript data Transaction data – This is typically data on how customers use their credit or debit cards. It can be used to generate a wide range of predictive characteristics Clickstream data – How a customer moves through your website, where they click and how long they take on a page Social network analysis – New technology enables us to map a consumer’s network in two important ways. First, this technology can be used to identify all the files and accounts for a single customer, even if the files have slightly different names or different addresses. This gives you a better understanding of the consumer and their risk. Second, we can identify the individual’s connections with others, such as people in their household. When evaluating a new credit applicant with no or little credit history, the credit ratings of the applicant’s network provide useful information. Whether a bank wants to more efficiently manage current credit customers or take a closer look at the millions of consumers considered unscorable, alternative data sources can provide a 360° view that provides far greater value than traditional credit scoring. Alternate data sets can reveal consumer information that can increase the predictive accuracy of the credit scores of millions of credit prospects. This allows companies to target consumers who may not appear to be desirable because they have been invisible to lenders before, which can lead to a commanding competitive advantage. ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue Optimizing marketing dollars to target customers Traditional marketing plans for credit card issuers call to onboard as many prime customers that meet the risk profile of the bank. However, new customer acquisition is only one piece of the puzzle. To drive maximum possible profitability, banks can consider not only the volume of customers, but also explore the overall profitability of a customer segment. Once these high-value customer segments are identified, credit card marketers can tailor specific products to these customer segments to deliver high value. Machine learning can assist both in the prediction of total customer value, as well as the clustering of customers based on patterns and behaviors. Identifying high-risk credit card transactions in real time Payments are the most digitalized part of the financial industry, which makes them particularly vulnerable to digital fraudulent activities. The rise of mobile payments and the competition for the best customer experience push banks to reduce the number of verification stages. This leads to lower efficiency of rule-based approaches. The machine learning approach to fraud detection has received a lot of publicity in recent years and shifted industry interest from rule-based fraud detection systems to machine-learning-based solutions. However, there are also understated and hidden events in user behavior that may not be evident but still signal possible fraud. Machine learning allows for creating algorithms that process large datasets with many variables and helps find these hidden correlations between user behavior and the likelihood of fraudulent actions. Another strength of machine learning systems compared to rule-based ones is faster data processing and less manual work. Machine learning can be used in few different areas: Data credibility assessment – Gap analytics help identify missing values in sequences of transactions. Machine learning algorithms can reconcile paper documents and system data, eliminating the human factor. This ensures data credibility by finding gaps in it and verifying personal details via public sources and transactions history. Duplicate transactions identification – Rule-based systems that are used currently constantly fail to distinguish errors or unusual transactions from real fraud. For example, a customer can accidentally push a submission button twice or simply decide to buy twice more goods. The system should differentiate suspicious duplicates from human error. While duplicate testing can be implemented by conventional methods, machine learning approaches will increase accuracy in distinguishing erroneous duplicates from fraud attempts. Identification of account theft, unusual transactions – As the rate of commerce is growing, it’s very important to have a lightning-fast solution to identify fraud. Merchants want results immediately, in microseconds. We can leverage machine learning techniques to achieve that goal with the sort of confidence level needed to approve or decline a transaction. Machine learning can evaluate vast numbers of transactions in real time. It continuously analyzes and processes new data. Moreover, advanced machine learning models, such as neural networks, autonomously update their models to reflect the latest trends, which is much more effective in detecting fraudulent transactions. Summary Bottom line: machine learning can leverage your data to develop patterns and predictions about your customers and applicants. These machine learning models are typically simpler to develop and deploy and may be more efficacious than traditional financial services modeling. These models also enable a more detailed forecast about your customers, allowing you to reduce risk while targeting more profitable customers through their lifetime with your credit card services. Related resources Case study: Machine learning predicts outcomes in financial services Case study: How Donatos uses machine learning to retain customers 5 tips to keep the wealth in your company Fusion Alliance has extensive experience in the financial services industry and serves as a preferred solutions provider for many prominent financial services institutions, including Fortune 500 firms. If you’d like to discuss your organization, let us know.
Amazon, Netflix, Airbnb, Uber, and other disruptors have raised the bar on what customers expect from a business. These online giants have figured out how to use their customer data to make personalized recommendations and predict when customers are going to buy — and present offers at just the right time. Brands that use personalization report an average growth of 20% in sales (Monetate research), and customers feel less spammed and more like they’re in control of the experience. It’s no surprise that consumers are looking for that same personalized, frictionless experience when interacting with their financial institutions, whether through mobile banking, your website, at a brick-and-mortar branch, or at one of your ATM locations. And it pays off for banks who can engage their customers. According to a 2013 Gallup study, fully engaged customers bring in an additional $402 in revenue per year to their primary bank, as compared with those who are actively disengaged. Even better, the research said 71% of fully engaged customers believe that they will be customers of their primary bank for the rest of their life. That could be your bank, but only if you can reach your customers in ways that feel natural and valuable to them. Customers want to be engaged with the right messages at the right time Imagine if you could understand your customers so deeply and predict their buying patterns so clearly that you could deliver targeted marketing only to those ready to invest in more products with your bank. Not only that, what if you could know what to say to them and on which channels to reach them? How would that impact your business? The trend is clear: financial institutions must adopt a customer-centric business model now to ensure success in the future. This puts banks like yours at a crossroads, and the problem is where and how to embark on that journey. Tackle your greatest challenges The formula seems simple. Increase your engagement and you’ll increase your revenue. But meanwhile, you’re under pressure to acquire new customers, maintain your base, forecast/reduce risk, manage capital, navigate security compliance and financial regulations, and optimize the business. You may also grapple with siloed data, legacy systems, and outdated processes, all seemingly monumental challenges that may adversely affect your customer experience. For example, your customers and employees may not have access to the right data at the right time to provide an optimal experience. Or, from a marketing standpoint, different departments within your company may be targeting the same customers, resulting in too many emails. Or your customers may get untimely messages about promotions that have passed or receive communications that don’t apply to their current situation. This creates frustration and a poor user experience that may be enough to make your loyal customers turn away. Other banks have been in your shoes, facing the same challenges and fears, but they’ve made major strides in putting the focus on the customer. They’ve found success through the “magic” of machine learning (ML). ML enables your staff to prioritize your over-capacity bankers’ focus and marketing spend on opportunities that are real. ML is a modern technique that uses algorithms to analyze enormous amounts of data. Machine learning models learn on their own and identify insights and patterns to predict future behavior. Machine learning algorithms connect the dots far faster and deeper than people can, exposing patterns in your customers’ behavior that empower your team to take actions that will impact your business’ top and bottom lines. Unlike traditional analytics tools, ML can evaluate account holders, securities, and transactions in real-time. If you want immediate decisions integrated in the moment, machine learning is the answer. And, good news, even though you may feel you are behind the curve right now, you have something that the younger fintechs you compete against don’t − a wealth of historic data that can be “mined” by ML to answer your specific business questions. Some organizations need help in improving the quality of their data for effective use in the machine learning model, and that’s not an uncommon challenge. But good data will be your key to success. Machine learning applications in finance Banks have found many successful ways to leverage machine learning. For example, they use it to answer specific business questions across all departments, including: How do I increase my customer wallet share, including: What are my best opportunities for cross-sell/remarketing my existing customers? Can I identify customers that we can convert from other banking institutions? Can I identify loan-default risk early enough to take an action? Can I dynamically price securities based on investor demand and market saturation? Can I predict my cash and reserve activity to optimize liquidity levels? Can I identify account holders’ attrition activity before they disengage? What percentage rate and product messaging would make my ideal prospect buy? The first step towards engaging customers with the right messages at the right time is to capture what questions your bank wants to solve. With these questions in hand, you can move to the next step, seeing how much predictive value these “use cases” for machine learning will give your financial organization. Case in point, these very questions are how it started for a large, institutional bank sitting on decades of financial transaction data we worked with. They wanted to more accurately predict member activity and drive better returns on cash reserves – and leveraged machine learning to do it. Our machine learning model identified patterns in their transactions, which spanned hundreds of credit unions and billions in cash to predict the deposit activity of millions of credit union members on a daily basis. The result? We freed $40 million in excess cash reserves. The insights gleaned also empowered the organization to pass on greater returns to members by selling short and long-term securities, arbitrage, and reducing borrowing fees. Another institution, Primary Financial Corporation (PFC), found great success using machine learning to improve their sales targeting. PFC wanted to predict CD issuers’ funding needs and institutions’ desires to invest. They developed machine learning models that synthesized PFC’s financial and competitive data to price securities, identify buyers, and project trade profitability. By the time the first phase of the project was complete, PFC could predict with over 80% accuracy and 70% precision the likelihood of a particular investor to buy a given investment. The common thread in these stories is that both organizations had an abundance of historic data at their fingertips, but they hadn’t explored how ML could help them retain more deposits, sell more products, or reduce their financial risks. The rapid predictive insights that machine learning continues to provide to both companies has been game-changing. And both are now exploring other ML applications. Get started Machine learning is widening the gap between banks who embrace it and their competitors who haven’t. If you don’t improve your banking experience, your customers will turn to another bank or even be serviced by a fintech. As you navigate how to become that customer-centric organization you want to be, explore machine learning as a way to get you closer to your customer and see rapid results. Start by coming up with specific questions that your business needs to answer, and take time to learn more about what machine learning can do in your organization. Contact Fusion Alliance to discuss if ML is right for your project. ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue
Artificial intelligence (AI) and machine learning (ML) have completely transformed mobile development. Mobile app users today are often looking for an easy and relevant user experience — one that has been customized for them. The best way to get there? Machine learning. Machine learning identifies anomalies and patterns that ultimately optimize the user experience. If your technology conversations have stalled at the brainstorming or ideation phase, consider why. If you don’t have a clear answer, you’re not alone there either. “Strategic decision makers across all industries are now grappling with the question of how to effectively proceed with their AI journey,” says Marianne D’Aquila, research manager, IDC Customer Insights & Analysis. Despite questions about how to proceed, organizations know they need to invest in ML for mobile before current competitors, and those waiting in the wings, figure out how to profit from it first. Considering the speed at which machine learning is being adopted and spreading, and its potential to quickly help companies on multiple fronts, the time for execution and implementation is now. Here are the top three reasons that make machine learning development for mobile important right now: 1. Machine learning for mobile increases app security “Facial recognition” ($4.7 billion, 6.0%) and “fraud detection and finance” ($3.1 billion, 3.9%) were among the top five categories of AI global investment in 2019, according to the AI Index 2019 Annual Report (an independent initiative at Stanford University’s Human-Centered Artificial Intelligence Institute). It’s not surprising. From TikTok’s recent security flaws to Target’s $18.5 million settlement, app vulnerabilities and potential data breaches are breaking news, and there are few signs of a slowdown. While the short-term financial impact can hurt, the long-term cost of losing the trust of customers and partners can be even more painful. Companies that receive users’ personal information (e.g., passwords, billing addresses, answers to security questions) for processes such as app authentication or making purchases must continually optimize how the data is used. Through machine learning and automating parts of the process, you can identify anomalies faster, allowing you to see patterns and manage potential weaknesses more quickly. Operationally, ML can detect and staunch security issues related to data inside your company, such as logistics or pricing anomalies, that could be a drain on resources. For example, if one of your products is selling faster than usual via a shopping app, it could be related to a pricing error. Do you really want that $450 device on sale for $4.50? The mobile application landscape is comprised of a wide variety of operation system versions, devices, and software systems. This creates a much greater number of attack surfaces that attackers can target. (A first step to optimizing security is risk evaluation and awareness. Contact Fusion to hear more.) 2. Machine learning leads to increased mobile privacy It could be argued that the recent news cycle around privacy indicates a real desire for clarity, if not outright skepticism. In more than 3,600 global news articles on ethics and AI from mid-2018 to mid-2019, the dominant topics were “framework and guidelines on the ethical use of AI, data privacy, the use of face recognition, algorithm bias, and the role of big tech.” You’ve heard about Russia’s role in the 2016 election and the use of personal information for ad targeting. These sorts of debacles haven’t led consumers to give up on digital. Instead, they are demanding more privacy oversight and are being more cautious about the apps they use. Privacy concerns are complementary to security issues. While security comprises keeping personal data from hackers, trolls, or criminals, privacy is more related to keeping personal data in a person’s own hands, away from any individuals or organizations that don’t need to be privy to it. For example, if you use an activity tracking app to record runs, you might appreciate a note when you hit a milestone: “You had a personal record today!” Machine learning makes it possible for the mobile app to directly detect this activity and send a congratulatory message without any human intervention. There’s no need for a stranger to know you clocked a fast 10K. Machine learning on the edge further increases privacy by eliminating the need for data to be sent to the cloud. When ML on the edge is in place, individualized data never leaves the device, keeping the user’s personal information in their own hands at all times. Amazon, Alexa, and Google Home employ ML on the edge, as some functions are offloaded to a device while others have to go to the cloud. In addition to supporting privacy, the reduced travel time for data makes these apps and devices faster. 3. Machine learning for mobile helps create personalized customer experiences Consumers expect their demographic, behavioral, and other personal data to be secure and private, while they also want increasing levels of personalization. Delivering on these demands can be a delicate, real-time balancing act for companies, but machine learning helps make it possible to juggle data acquisition with protection and those prickly questions around how to use the data to everyone’s advantage. But is there a clear business case to pursue personalization? According to a 2019 Salesforce report, the answer is yes, as 75% of 8,000 consumers and business buyers surveyed expect companies to use new technologies to create better experiences. Machine learning for mobile enables you to make user-experience headway on several fronts. First, it can help you build a baseline of customer app usage. Once you have that baseline, you can see patterns in user behavior. Next, particular behaviors or deviations from the baseline can trigger delivery of a relevant coupon, suggested product to explore, or a reminder to revisit an abandoned shopping cart. Even more sophisticated, ML can serve up colors, screen layouts, and language that appeal most to a particular user. And with machine learning, the reactions are in real-time. The more your user engages with your mobile app, the more refined and personalized the experience becomes. Through machine learning, your brand becomes more closely aligned with the customer experience that your customer desires. Getting started can feel uncomfortable at first, but at Fusion, we’ve found that organizations often have low-hanging fruit ripe to benefit from machine learning for mobile. You just need to be able to see and then act on those opportunities. Working alongside you on this journey should be people who understand data science and machine learning, and who can uncover weaknesses to target. Now is the time to move forward on machine learning for mobile initiatives. Current market conditions indicate a shortage of professionals in machine learning and data science. Fusion fills this gap. If you’re interested in hearing more about machine learning for mobile, let us connect you with one of our experts.
Ever tried to detangle a big box full of wires and cables to get to that one power cable? It often seems that no matter how much care we put into placing those cables into the box, we inevitably end up with a tangled mess that tests both the physical integrity of the box and our patience. If you feel (or are starting to feel) that your automated UI testing efforts are like a box full of cables, then this post is for you. Some initial thoughts If you are coming from the preceding post, Automated UI Tests: Taking the Plunge, then you probably have a solid notion of what we’re going to express here. The ideas and advice are not designed to be a definitive list of items that, if followed, guarantee success in automated UI testing conversion. Part of what makes the world of software development exciting is that every situation is unique and presents its own challenges. Let’s help get your creative juices flowing and provide the right context that can get you on a confident path of automated UI testing, with your own artifacts and processes. Let’s start with flaky tests If you are familiar at all with testing environments, you know which flaky tests we’re talking about. Sometimes they pass, sometimes they fail. There doesn’t seem to be a rhyme or reason for the pass/fail, and you find yourself holding your breath every time a build is triggered. Flaky tests can be toxic to your efforts in several ways, but the most direct impact happens when the people responsible for that app begin to lose confidence in the automated tests. Angie Jones delivered a fantastic talk at SauceCon 2017, full of strategies and conversation-starters surrounding this topic of the flaky test. The bottom line: don’t allow these to fester. The first steps are to isolate them and move them to another branch so that they stop poisoning a build that otherwise provides consistently valuable information. Where the wild browsers roam It can feel like graduation day when you’ve just finished a batch of automated tests; you have finally completed a phase of the project, and it’s ready to run for the whole world to see. Then the reality of browser/device coverage sets in, and the companion-reality of run time rears its ugly head. For example, in the context of a web app, it is typically expected you have the ability to execute against at least the most current versions of the Big Four (Chrome, Safari, Firefox, and IE). You get to a point where spinning up virtual machines on your development box feels slow and inevitably hijacks your computer, making it difficult to continue working during a build. Luckily, there are a number of solutions out there to address this problem. Cloud providers such as SauceLabs and Browserstack will work for some situations, while a hosted solution like Element34’s Selenium Box will work for others. If your needs are not horribly extensive, you could also look into standing up your own Grid. A word of advice: you might find that if you developed all of your tests against one browser, then it may not behave as expected on the others. This could (and very well may) be its own post in the future. Keep in mind that you may need to add branching logic depending on the browser or device in question. Additionally, the sentiments from the previous post are relevant here: if you are embarking on a new idea for multiple browser/device/host support, start with a small subset of your tests. It will be easier to work out the kinks of your brand-new Grid with a limited number of tests that can finish in a few minutes rather than testing your whole suite. What’s in a framework? “Framework” is one of those words in our industry that can mean radically different things to different people. We’re referring to it here as a repeatable, generic solution that helps you quickly bootstrap new projects. No matter which technology and tools stack you are using, there are going to be ways to cook up little bits of the process that are transferable. Thoughts toward this method usually come around the same time you want to start adding automated UI coverage to the next app on your list. A good number of the frameworks we’ve built and seen hit these major feature points: Handle input and desired properties Do you want to be able to specify things like the browser, host operating system, versions, additional app binaries, and properties from an external configuration file? What would you prefer to be command-line arguments? What about external data files? Manage the object that controls the browser, device, etc. This is the WebDriver object in Selenium WebDriver world. How do you get this object and fire up the application? Do you want to abstract it behind a factory? Do you need to proxy any of the behavior to start and stop the browser, emulator, or simulator? Establish your reporting features of choice How are you currently digesting the output that your builds produce? Are the mechanisms that produce that output tightly coupled to the tests of a particular app? How would you abstract those reporting mechanisms so that they can be used for any app? If you’re entertaining different options here, give a look. Include examples What does a typical test flow look like? If you’re using page objects, what would a simple one include? This is not only useful for newcomers to the automated testing effort, but also serves as a good, executable reminder when starting a fresh project. Closing remarks Considering there isn’t a silver bullet solution to most of the problems in software development, detangling your own box of cables and wires doesn’t have a surefire checklist for success. At the very least, take comfort in the fact that you are not alone, and you can use the advice above to help get the wire-wrangling process started.
In the quest to solve its most pressing challenges, the banking industry is being transformed by its adoption of artificial intelligence (AI) and machine learning (ML). Financial institutions are under pressure to better understand their customers, drive a more personalized customer experience, acquire new business, forecast risk, prevent fraud, comply with increasing regulations, improve processes . . . the list goes on and on. Most banks continue to use traditional, expensive analytics tools to tackle these challenges, but they struggle to keep pace with demands, and the tools are difficult to maintain. Machine learning relies on statistical and artificial intelligence approaches to rapidly uncover patterns in complex data, patterns that can’t be discovered through traditional tools. The impact of machine learning in banking While adoption of machine learning in finance is in the early stages, institutions who have leveraged this secret sauce are finding it to be a differentiator. For example, a large regional bank leveraged ML to predict institutional customers’ likely deposits on a daily basis, freeing $40 million in excess cash reserves. Another institution, credit union service organization Primary Financial Company, used ML to synthesize financial and competitive data to price securities, identify buyers, and project trade profitability. PFC can now ascertain with over 80% accuracy and 70% precision the likelihood of a particular investor to buy a given investment. For these companies, their early ventures into ML have certainly moved the needle on what they can accomplish. We spoke with three artificial intelligence and machine learning experts at Fusion Alliance to tap into their experience with banks, learn where the market is headed, and get answers to some common questions. Q: What do you see as the 2020 trends in machine learning for banks and credit unions? A – John Dages: 2020 is the year where we see machine learning become more democratized. Historically, machine learning engagements have required substantial data science and model training investments. However, the major ML platforms are evolving and providing advanced automated machine learning and feature analysis toolchains, lowering the barrier of entry for ML projects. Our team is also actively monitoring new “explainability” techniques to add deeper transparency for ML-based predictions and insights. Historically, the black-box nature of some ML algorithms (specifically deep neural networks) makes it difficult to relate to business principles. Ideally, these emerging techniques will increase confidence in ML models early in their lifecycle. In the banking sector, we have seen a great deal of capital chase trading and investments, but we are also seeing ML flow into loan operations, cash management, and general risk. A – Sajith Wanigasinghe: Machine learning applied to fraud detection is a major trend. Artificial intelligence is beneficial here because ML algorithms can analyze millions of data points to detect fraudulent transactions that would tend to go unnoticed by humans. At the same time, ML helps improve the precision of real-time approvals and reduces the number of false rejections Another leading trend is using robo advisors for portfolio management. Robo advisors are algorithms built to calibrate a financial portfolio to the user’s goals and risk tolerance. Chatbots and robo advisors powered by natural language processing (NLP) and ML algorithms have become powerful tools with which to provide a personalized, conversational, and natural experience to users in different domains. A – Patrick Carfrey: Personalized delivery of banking services is going to improve in 2020. New products are entering the marketplace that enable consumer and commercial bank customers to receive relevant account information in real-time, at the grain and timeliness that customers want. Q: What is your favorite machine learning use case for banks right now? A – John Dages: Machine learning will change the way banks see credit risk. FICO and the five C’s of credit are limited in features, captive to three agencies, potentially biased, and outmoded. The models we are building will allow lenders to view a complete picture of a borrower, offering customized predictions on creditworthiness. The banks that adopt this model will see an increase in lending opportunities while better understanding the liabilities on the balance sheet. A – Sajith Wanigasinghe: Customer lifetime is my favorite use case, where we can predict how valuable would a customer be within X number of years so that the bank can establish a good relationship with the customer in the early stages. A – Patrick Carfrey: Remarketing/cross-selling is a powerful option for banks right now. Given all the customer data that banks own, including deposits, transactions, and more, ML can tell if a customer is a good target for a new product in the bank’s portfolio. This is especially relevant as customers are expecting more. Being able to predict customer needs supports that need. Related Article: 4 ways banks can leverage the power of machine learning Q: What is the one machine learning data tool you can’t live without? A – John Dages: Excel. Sure, the enterprise data tools are highly capable (and the team spends a lot of time there), but the ability to quickly navigate data, perform simple transforms, and share data with a tool everyone knows is critical. I can’t remember a project where we didn’t get exemptions to install Excel in the banks’ datacenters. A – Sajith Wanigasinghe: TensorFlow framework would be one of the tools that I can’t live without because it’s the number one framework that I use every day and in 99% of our projects. TensorFlow is an open-source machine learning library which helps you to develop your ML models. The Google team developed it, and it has a flexible scheme of tools, libraries, and resources that allow me to build and deploy machine learning applications. A – Patrick Carfrey: TensorBoard. This is TensorFlow’s visualization toolkit, and it provides a nice visual interface for tracing key metrics through the model training pipeline. Deep learning models can get complex quickly, and being able to explore a model outside of the command line is nice. Clients love the graphs, too! Q: What are the biggest machine learning myths you wish more people understood? A – John Dages: For those beginning to develop an AI/ML center of excellence, there is going to be a gravity to focus on the cutting edge (deep learning, cognitive science, others). While there is obviously value there, there are a multitude of “traditional” machine learning practices and algorithms and that are lower complexity. A deep neural network should be the last resort, not the first option! A – Sajith Wanigasinghe: That machine learning and AI will replace humans. In fact, machine learning and AI will help you do your job much faster and better, and enable you to focus on the satisfying and important human elements of your role — including creativity and strategy. Think of machine learning and AI in terms of a tool, not a replacement for humans. A – Patrick Carfrey: For every machine learning project I’ve delivered, our clients will inevitably ask “We love the model, but can you tell us more about how the model is making the predictions?” This is a surprisingly challenging question to answer, particularly for black-box neural networks. Fusion has a variety of techniques to provide additional details, but they aren’t necessarily directly correlated to the actual model we’ve developed. If it is insights you seek, not decisions, consider business intelligence tools and processes in lieu of machine learning. There is room for both! Meet our panel of experts: John Dages With 15+ years of technology leadership experience, John brings a unique perspective to companies on their advanced analytics journey. He led numerous machine learning initiatives for large enterprises across industries. Those projects range from customer acquisition and retention to securities pricing and trade analytics. John’s background in application development, analytics, systems integration, and I&O helps him formulate how businesses can use data to drive competitive advantage and engineer true intellectual property. Sajith Wanigasinghe Sajith is an expert in machine learning, artificial intelligence, and enterprise-wide, web-based application development. He applies his experience and insights to help enterprises identify and solve challenges across the business that are ideal for machine learning. Sajith led teams that have revolutionized the financial, insurance, food, and retail industries by introducing advanced, intelligent forecasting systems that are powered by machine learning and artificial intelligence. He holds a B.S. in computer science from Franklin University. Patrick Carfrey Patrick joined Fusion Alliance over six years ago, leading a variety of application development initiatives for a flagship Fortune 500 client. Patrick is a firm believer that software is social, choosing to spend as much time in front of end users to build the best possible product. In that capacity, Patrick has developed and deployed practical machine learning solutions to help better understand and predict customer behavior to drive maximum engagement. He is the Competency Lead of Java at Fusion and holds a B.S. in computer science and engineering from The Ohio State University.
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