Accessing data at the speed of business is critical to remaining competitive in a digital-first world. But if you’re relying on outdated architecture where your data is trapped in silos or lost in a data lake, access to the functional data you need is seriously limited. When your existing framework is no longer serving your business, it makes sense to transition to a modern data platform, but you may have hesitations about whether it can help you succeed.
To help you better understand this solution and what you need to gain from it, we are looking at data platform capabilities and sharing five modern data platform imperatives that will help achieve a more logical data management system.
What is a modern data platform?
With so many emerging solutions, data is a very complicated environment, so let's start by clearly defining what a modern data platform is and its capabilities.
A modern data platform is a flexible, cloud-based, end-to-end data architecture that supports collecting, processing, analyzing, and delivering data to the end user in a way that is aligned and responsive to the needs of the business.
On the surface, aside from it being cloud-based rather than on-premise, modern data platform capabilities aren’t different from traditional data architecture. The difference is in how new technologies have expanded their capabilities. Here are some of the ways modern data platforms can deliver more for your organization:
Data ingestion
Bringing new data into the environment is the first step to managing data. In a legacy architecture, that is mainly done through batch processing. Batching collects and processes data at specific time periods or intervals. By leveraging the higher computing capacity of a cloud-based architecture, data can be streamed in real time to data storage units, eliminating bottlenecks and delays to keep data moving through the system in a more fluid manner.
Quality and governance
With AI integrated into the architecture, data quality and governance tools can be automated, speeding up how new data sources are analyzed, categorized, and assessed for security concerns.
Security
Security measures can be integrated at the base level for new data products, providing inherent encryption whether it’s at rest or in transit. Within a modern data platform, security measures are implemented to dynamically filter and obscure data as needed to support your organization’s security policies.
Storage
Cloud-based architecture offers the potential for nearly unlimited storage and offers a pay-as-you-go model, so you only need to invest in the volume of storage you need today. As your data storage needs increase in the future, you can add and seamlessly integrate additional space without creating silos for new data.
Transformation
In legacy architecture, transformations such as quality adjustments and business logic need to be applied in the early stages of data flow during large batch processing. While this ensures that the downstream usage of the data is more performant, it also locks the business rules in place which removes flexibility in how the business looks at and interacts with the data.
The expanded computing power and advanced tools in a modern data platform offer a more flexible timeline to add transformations to the data. Business rules and logic can be applied later in the data flow and adapted to suit changing needs.
Discovery
Data discovery is streamlined through integrated tools within a modern data platform that can automatically scan, categorize metadata, and organize it so the most appropriate data is accessed more easily and quickly.
Delivery
In a legacy architecture, data delivery visualization tools required the data to be specifically structured prior to business usage, whether for reporting, data extracts, or API access. Now, visualization tools have advanced features that support access to semi-structured and unstructured data without the need for intensive (and expensive) data processing. Integrated tools simplify both data extraction and data sharing and have built-in security and monetization features.
DevOps and DataOps
In a modern data platform, DevOps/DataOps are cross-platform and cross-language supportive, which makes it easier and faster to coordinate development and release implementation tasks when architectures are built using multiple tools.
5 modern data platform imperatives
The overall framework, capabilities, and patterns of managing data are universal within a modern data platform. However, no two platforms are the same. Each one is highly customized to support the data and data needs of the organization and require different combinations of tools or features to achieve specific functionalities and cover the needed capabilities.
You still need to ensure your platform manages the data in a way that aligns to your organization’s unique needs, and this means that five modern data platform imperatives must be met.
1. Greater flexibility
The greatest challenge of legacy data architecture is the lack of flexibility. The physical servers can’t be added to or modified easily to meet the changing data needs of your organization, so they need to be built with the capacity for future data needs. This is easier said than done given the rapidly changing landscape and the sheer volume of data you’re taking in.
A modern data platform is incredibly flexible. It allows you to consider your data needs today and budget accordingly rather than trying to predict your data needs in the future which requires a significantly larger investment. As you need to increase data storage, adopt automation, or pivot in your data needs, these updates can be integrated seamlessly into the platform.
2. Improved access
The people and applications accessing data need it in real time and in the proper format, but the needs of your data science team vary greatly from the needs of your business intelligence team. A modern data platform must support a faster time to market for data assets, and one way it does this is through a medallion architecture.
A medallion architecture creates a multi-layered framework within the platform to move data through a pipeline to the end user.
- Bronze layer: Raw data is collected directly from the source systems with little to no transformation and stored here to provide a base layer of full history for additional processing.
- Silver layer: Data from multiple sources are curated, enriched, integrated, and organized in a structure that reflects the data domains of the organization.
- Gold layer: Data needed to support specific business drivers is aggregated and organized so it can be used for dashboard creation and self-service analysis of current states and trends.
This architecture allows a diverse user base to access the data in the form that best suits their needs. Data scientists can access raw data from the bronze layer to identify new and emerging patterns, business applications can access data in the silver layer to produce data products, and business users can access the gold layer to perform analytics and create dashboards.
3. Incremental implementation
Rather than transitioning to a modern data platform in a single, giant step, we recommend an incremental move. This makes it significantly easier and faster to focus on the current data products your organization needs, like reports and dashboards, while you are starting to build out the initial infrastructure.
An incremental implementation lets you take a clear, informed look at the data you need, how you need it, and how it aligns with your business drivers. You can then choose to add, adjust, or stop processing certain data to put more focus on the data that will answer pivotal business questions. At the same time, building only what you need when it’s needed, an incremental implementation saves money and avoids bringing over old data that no longer serves your business.
4. Better communication between IT and business users
A modern data platform needs to support improved communication between your IT or data engineers and your business users. As data flows through the framework and reaches the end user in the language they speak, the end-user has greater clarity. For business users, this may mean seeing gaps in how the existing data is not directly answering their questions and needs to find a different way to utilize the data. For the data engineers, this may mean seeing opportunities in how to filter out aberrations in the data to improve the aggregated data. This clarity allows the teams to work together to target solutions that will cover existing or emerging needs.
5. Re-focus valuable resources
Once the initial data set is built, we apply repeatable patterns to the mechanics controlling data ingestion, storage, and delivery. Having a proven framework that can be repeated to unlimited data sets saves time and reduces the cost of building, operating, and maintaining the platform. Your data team can refocus their time on higher-level tasks, including improving data quality and speeding up delivery.
Whether you have questions about data platform capabilities and functionalities or you’re ready to make the shift to a modern data platform, we’re here to help! Set up a call to talk to an expert or visit our modern data platform hub to learn more.
