Modern Data Platforms

Large and enterprise level organizations traditionally rely on one of two types of data architecture: monolithic or distributed.

In a monolithic architecture, all data is stored in a single location, such as a data lake. While this may seem like an organized solution, all the information in raw, unstructured, and structured formats for the entire organization is in one place. Finding what you need can be like finding the proverbial needle in a haystack. Data must often go through the IT department for processing before getting into the hands of the end users, leading to bottlenecks and delays.

In a distributed data architecture, the data is stored across multiple sources, including data warehouses, IoT, business applications, and even localized spreadsheets. Without cross-team sharing and technology that can pull the data into a system that is easy to read and search, finding the information in this framework is also very slow and marked by redundancies and inefficiencies.

Regardless of whether your organization relies on a monolithic or distributed architecture, you’re most likely struggling with delayed access to data, having difficulty integrating new data into an existing framework, and facing challenges with security and governance. Finding a solution will require modernization — of your data architecture and the data platforms you have in place.

Legacy Data Platforms

While data architecture is the framework for how data is ingested, stored, and then delivered, the data platform you use is the end-to-end technology solution that determines how data is not only ingested and stored, but also processed, analyzed, retrieved, and presented to the end user, whether that’s a business application or a data consumer.

Like legacy data architectures, legacy data platforms cause difficulties in scaling, democratizing, and accessing data due to the following challenges:

• Technical and functional barriers within your legacy data architecture and integration framework stand in the way of delivering data at the velocity you require   
• Slow and disorganized data delivery blocks the advanced analytics you need to create dynamic, successful strategies   
• Overly rigid or slow-moving storage models make it difficult to pinpoint where specific data is located, or access data for analytics use cases
• Redundancies and competing capabilities cause added difficulties when trying to obtain up-to-date data   

Discover how an annuity organization shifts from a legacy data platform to handle changing market trends

With a better understanding of how your legacy data architecture and platform may be holding you back from maximizing the potential of your information, let’s consider how modernizing can align with your business drivers and help you achieve your goals.

While still offering an end-to-end solution to manage your data at every stage, a modern data platform is a cloud-based platform in which updated technologies are implemented to ensure optimal scalability, democratization, and access. Modern data platforms offer many of the same capabilities as a legacy architecture, but new technologies expand them to offer greater flexibility in how your organization stores, processes, and accesses data.

Discover how a modern data platform can help your organization succeed

Technologies Within Modern Data Platforms

• Cloud-based (allows for scalability and pay-as-you-go storage so you can focus on your current data needs)
• Machine learning (to ingest and transport data as well as apply security and governance guidelines)
  Layered architecture (to organize and process data for specific users while improving the flow of data Cloud ETL over local transfer (leverages the power and performance of a cloud platform to transform data quickly rather than rely on batch processing)
• Implement BI tools (improve analytics)

Learn how a modern data platform transforms analytics for a major manufacturer
Find out how a proof of concept shifted the way an oil company viewed their data

Making the transition to a modern data platform can be difficult, but careful planning can help mitigate common risks and pitfalls. As you work through your options, here are key guidelines to keep in mind:

• Align with the business. Check in with business users to build a thorough understanding of the processes, decisions, and business drivers your data needs to support
• Assess the gaps. Objectively assess how the current state of the data landscape supports the business and technical asks, and captures the key gaps and barriers that impact how the organization can achieve desired outcomes   
• Investigate vendors. Work with an external partner or your own team to perform a full vendor assessment, so that you avoid choosing one-off solutions and keep your data program scalable over the long-term
• Build a transition strategy. Ensure a successful transition to a modern data platform by designing a strong, scalable foundation, mapping out how you’ll build on it to provide incremental value to the business, and planning for what data to migrate and when 
• Roll out changes. Equip business users to leverage the new technology capabilities your modern data platform provides to deliver critical use cases faster and achieve better outcomes   

Regardless of the size of your organization, you might find it helpful to tackle vendor selection and modernization planning with an external partner. In our experience, a third party can help businesses align internally, troubleshoot obstacles, and avoid common roadblocks. An experienced team of data professionals can help you understand the evolving landscape of cloud platforms and the impact that architecture changes are likely to have on your business processes.

While transitioning to a modern data platform can improve the system in place, let’s consider the solutions to modernizing an outdated data architecture, whether it’s a monolith or a distributed model.

Data Fabric: An End-to-End Integrated and Automated Framework

Data fabric is a framework used in many modern data platforms that weaves your data into a unified layer from the legacy systems, including lakes, warehouses, and apps. The fabric also consists of multiple technologies, including data virtualization (more on this below) as well as machine learning and artificial intelligence. Data is not only centralized to improve speed and access, but the framework is also automated to immediately integrate and process new data as it is introduced and apply security and governance measures across the layer.

Learn more about speed-to-value delivery with data virtualization and data fabric

Integrating the data through a fabric offers a holistic, data-focused look at decision making. For example, when you have multiple data platforms, such as one for supply chain and one for customer data, a fabric allows you to look at both data sets at the same time. Unifying the information gives you a more cohesive view and provides deeper insights to drive stronger decisions.

Increasing data democratization with a data mesh

While data fabric is a technology-driven framework that integrates data from across multiple sources, a data mesh is a methodology designed to distribute data from a centralized source. Typically used in an inflexible monolithic architecture, a data mesh gives every business domain control over their own data.

Using a data mesh puts access, governance, and compliance in the hands of the business domain, and they get to decide how the data is stored, processed, analyzed, and shared. By adopting this methodology, data consumers can bypass IT pipelines and the bottlenecks they cause and get data in real-time and in the language they use.

Learn whether a data fabric or data mesh is best for your organization

When you need data in real time, but your data is stretched across multiple disparate sources, consider integrating data virtualization into your data strategy. Data virtualization is a technology that integrates data from across a variety of sources without moving it. Instead, it creates a data abstraction layer that rests above the individual sources so business intelligence teams, analytics teams, and other data consumers can access semantic data at a logical level, regardless of its format and source.

Learn how data virtualization can speed time to market and improve the value of your data

If your organization is struggling with getting the data you need in a timely manner or wants to streamline workflows, implementing data virtualization into your overall strategy can help you achieve your goals.

Consider the top three data virtualization use cases