Data Mesh and Fabric Confusing? Data Leaders Need to Unpack
Next-Gen Data Architectures

Data architectures and their supporting technologies and capabilities (including analytic data infrastructure, and data engineering and data catalog products) face increased technology and business pressure to meet the requirements of more complex, diverse, and distributed business intelligence (BI) and analytics use cases and applications. The scale, distribution, mission-criticality, and pace of change facing data leaders and their teams is outpacing the ability of current architectural approaches. Data architectures that lack flexibility, adaptability, and scalability lead to challenges as organizations struggle to capture benefits and achieve positive returns from their BI investments.

In the face of these challenges, although data leaders realize they need to modernize data architectures to better support BI and analytics needs, they are unclear on how to do so in a game-changing manner. Approaches that continue to emphasize centralization, consolidation, physical data movement, and use-case-specific optimization remain common. Although better extract, transform, and load (ETL) processes and data-warehouse enhancements may yield marginal value gains, they will not create the breakthroughs needed to support the BI needs of the modern, Hyper-Decisive® organization.

Forward-thinking data leaders know that something new is needed. And many of them suspect that data fabric and mesh concepts are part of the story. But the market is confused on what those concepts are, how they relate, and how to act on them. Active Data Architecture™ leverages these ideas to build infrastructure for BI that is more adaptable, optimized, and automated than traditional project- or use-case-specific, hardwired, centralized, and manually managed approaches. Data leaders that adopt this vision likely will bring their organizations greater degrees of BI success.

Active Data Architecture supports a platform-independent layer that sits between physical data stores and points of data consumption. It includes various data management capabilities, including virtualized and distributed data access, data governance, and security. At its core, Active Data Architecture serves as an abstraction layer, translating business and physical structures. It is an architecture dynamically optimized for performance, scalability, and cost management.

A fundamental use case for Active Data Architecture is the idea of data products (or data as a product)—elevating the notion of data inherent value for achieving specific business outcomes. An Active Data Architecture helps to elevate the status and importance of data to the level of a “product” by separating the management, governance, and use of data from the specific technical systems in which it may be housed. In essence, an Active Data Architecture provides (among other things) a layer of abstraction that enables organizations to manage and apply data in an application-independent manner. Data leaders can amplify the value they deliver by orienting architecture discussions toward the idea of data products, which support specific strategic business outcomes.

The concepts of data mesh and data fabric are sometimes associated with an Active Data Architecture. They involve managing distributed data, enabling consolidated data views, and provisioning data to various process and application points of consumption. Data mesh links together distributed data sources and enables these capabilities in a pre-programmed, practitioner-managed, and manually optimized fashion. Data fabric builds on these same capabilities and adds elements of automation to help make the Active Data Architecture truly dynamic, self-organizing, and continually optimized.

Data leaders and their teams can clearly identify opportunities to modernize in the direction of Active Data Architecture concepts. Optimal starting points include business outcomes underserved by current approaches and data flows heavily oriented toward physical and centralized data stores. By gaining an understanding of Active Data Architecture and supporting concepts (including data mesh and data fabric), and applying existing data engineering capabilities toward these opportunities, they can better enable delivery of data products, help create more business value from data, and move their organizations further toward being Hyper-Decisive.

Data leaders can best position themselves and their teams for success by educating key constituents on the value of Active Data Architecture concepts, and linking the ideas with current and future strategic BI investments. They also must develop a plan for showing the value of this new approach, expressing it in metrics such as speed of deployment, performance, reliability, and adaptability.

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