By Scott Moore, Director of Presales, Semarchy
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. But 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.
That’s why more recently, the term EDH has been retranslated to Intelligent Data Hub, or IDH – a software platform with a robust set of data management capabilities to discover, integrate, manage, and govern data. It traverses multiple applications to measure and monitor data quality, process efficiency, and other data management outcomes.
The IDH manages a single master data hub for all core data assets comprising customers, products, accounts, locations, assets, facilities, employees, suppliers and much more.
The origin of the Master Data Management hub
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 master 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 management hubs with agreed-upon semantics for analytics and applications. This isn’t to say that companies no longer need Master Data Management; they just realized they needed to be more enabled, agile, and able to move more quickly.
This tendency puts more pressure on chief data and analytics officers (CDAOs). According to a new Gartner survey, CDAOs are now tasked with a broad range of responsibilities, including defining and implementing D&A strategy (60%), oversight of D&A strategy (59%), creating and implementing D&A governance (55%), and managing data-driven culture change (54%).
The survey further reveals that analytics-driven organizations are investing more in many of their D&A functions, including data management (65%), data governance (63%), and advanced analytics (60%). The rise in investments, coupled with the increasing demands on data and analytics, signifies a growing confidence in CDAOs and an acknowledgement of the data office as an indispensable business function.
Evolving the data hub strategy
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. However, a data management hub doesn’t bring true value to a business without people: data literacy, data governance, and a business-driven data development model are what’s needed to make your data sparkle.
While the first EDH was 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 management 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. Moving to an Intelligent Data Hub may seem like a steep hill to climb, but it can be smooth and non-disruptive if your data hub strategy comprises a planned step-by-step migration.
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.
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