To harness data for insights and business growth—and ultimately create a data-driven culture—organizations need a holistic approach to data architecture and a strategy that’s efficient and doesn’t involve manually patching together many solutions. As a result, many organizations are adopting a data fabric approach while implementing a modern day data architecture and storage solutions such as a lakehouse.
A lakehouse is an emerging architecture that offers the low cost and flexibility of a data lake with the performance and structure of a data warehouse. A concise overview of IBM’s watsonx.data is discussed in my earlier blog here.
A modern data architecture ensures data is accessible to relevant data users based on their unique workflows. Data fabric is an architectural approach that simplifies data access in an organization and facilitates self-service data consumption. Teams can use this architecture to automate data discovery, governance, and consumption through integrated end-to-end data management capabilities. Whether data engineers, data scientists, or business users are the intended audience, a data fabric delivers the data needed for better decision-making.
A data fabric elevates the value of an organization’s data by providing the right data at the right time, regardless of where it resides. A data fabric brings together capabilities as shown in figure 1 below. If designed and implemented correctly, a data fabric can help avoid the cost and complexity of integrating a plethora of point solutions. Instead of a fragmented group of products that have been stitched together, a data fabric offers a single, holistic solution that is built to work seamlessly.
Figure 1: Data-fabric: delivering Data Product as a Service to watsonx.data and the wider watsonx platform
Data Fabric – a Cornerstone to Successful Data Governance
As part of a modern data fabric, data governance helps creates an end-to-end user experience rooted in metadata and active policy management that empowers users to view, access, manipulate, and analyze data without the need to understand its physical format or location and without having to move or copy it.
The technology components of the IBM data fabric approach enable organizations to automatically apply industry-specific regulatory policies and rules to their data assets, securing them across the enterprise with:
- An AI-augmented data catalog that helps business users easily understand, collaborate on, enrich, and access the right data
- A metadata and governance layer for all data, analytics, and AI initiatives that increases visibility and collaboration on any cloud
- The ability to mask data dynamically and consistently at a user-defined granular level
- The ability to create anonymized training data and test sets while maintaining data integrity
A data fabric also aids in delivering quality data and ultimately accelerating collaboration. While this blog post skims the surface on the subject of data fabrics, lakehouses and governance, a more in-depth read is available in my new book “The Lakehouse Effect – A New Era for Data Insights and AI”, which is available for download at no cost.