Despite our best efforts we still receive lots of inquiries from organizations that confuse and conflate data hubs with data lakes and data warehouses. In truth, the term “data hub” is the where the issue has come from. About two years ago a couple of us were looking at a number of topics and research areas and we connected the dots. The connective tissue was that idea that organisations ought to be conspicuously determining, “from where should our organizations maintain data (and analytics) governance policy that are executed, or the trusted source of data itself?”
It turns out that so many organizations had assumed all data and/or policy should be centralized (think ERP), or widely distributed at the edge (think standalone business application or IoT edge device). It turns out that a ‘new’ approach, once that permits the notion of intermediate nodes to store/execute the policy or trusted data itself somewhere within all the spaghetti of systems, yields real efficiencies and drives agility. Those “nodes” we introduced as ‘data hubs’, knowing that the word “hub” was already well used. It turns out the idea is real useful and popular, once you get over the nomenclature challenges. But for some reason, too many folks confuse these nodes with larger sets of collections of data – transaction and stream data – often referred to as data warehouses or data lakes. Data hubs tend to be small in comparison to data warehouses and data lakes; data hubs don’t store transaction information.
Anyway, we have a growing set of notes published on the topic, and presentations we update at our Data and Analytics Summits series around the globe. Hopefully this material is starting to help you become more agile with data sharing, data (and analytics) governance, and data (and application) integration.
- Use a Data Hub Strategy to Meet Your Data and Analytics Governance and Sharing Requirements
- Implementing the Data Hub: Architecture and Technology Choices
- Infuse Your Data Hub Strategy With Data and Application Integration
- Data Hubs, Data Lakes and Data Warehouses: Choosing the Core of Your Digital Platform
- Data Hubs: Understanding the Types, Characteristics and Use Cases
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