Not long ago, Gartner analysts coined a new term for the data and analytics industry—the Enterprise Data Hub (EDH)—to refer to the smaller scope of purposeful and governed data activity companies were utilizing to support agility. (More recently, the term EDH has been retranslated to Intelligent Data Hub, or IDH). As some data technology vendors began incorporating “Data Hub” in their product naming, companies looked for consistent definitions of both what a data hub is, and how to architect with them. Today, as we reflect on the challenges we’ve seen at our clients while adopting the IDH into their modern data architecture, two questions have bubbled to the surface: why is there so much confusion around this concept, and what is it that customers really care about? Or, perhaps more astutely: how did we get here? And where is the true value that is driving the adoption of the IDH?
The answers begin with a quick recap of the evolution of Master Data Management. Through its well-documented definition and purpose, Master Data Management fixated on mastering sets of business data based on programmed (hard-coded) rules to enforce predefined rules and synchronize (bi-directional) operational systems on one set of “golden rules”. For data management and governance programs, this was a much-needed directive. However, many companies were unsuccessful at rolling out Master Data Management projects due to their complexity and cost, along with the risky, ambitious nature of achieving the goal of having a single, agreed-upon set of data semantics shared across the enterprise. Simultaneously, data analytics needed mastered data and lineage to create a data hub with quality data attributions to provide insights. Thus, analytics-driven organizations began to move away from operational system integrations to smaller, localized data hubs with agreed-upon semantics for analytics and applications. This isn’t to say that Master Data Management is no longer needed, just to illustrate how companies realized they needed to be more enabled, more agile, and able to move more quickly.
Business data enablement, agility, and speed are the three biggest factors influencing data and analytics. Our data and analytics—and the systems we use to architect and manage it—need to be evolutionary. They need to start small, continue to grow and refine, and look toward the future.
Here we see the true value of IDH: We don’t always need an enterprise-grade boil-the-ocean style of hard-coded, master rules and governance. There is a lot of value from all the other parts of data if we could master them based on a specific application or set of semantics. While the first EDH were born out of the need to integrate data to a certain set of semantics to support specific business analytics, analysts are now driven to achieve this same need independently, and with all data hubs throughout the company. Data management professionals can agree on more localized semantics, put it on a dataset, and make hubs available—and they can do this quickly, accurately, and in ways that instantly add value without worrying too much whether this hub will continue to grow and expand into enterprise acceptance.
The future of data management, we believe, will be largely influenced by—and will rely upon—scalable, agile, distributed architectures that benefit from business involvement. This is where the Intelligent Data Hub proves its value, not only in agile analytics but in agile data management. We are currently testing this hypothesis, and we look forward to sharing the results with you in a future Insight Paper.