For more than three decades, organizations have relied on data warehouses to support business information consumers’ needs for descriptive analytics to help inform about the current state and to help influence ongoing business decisions. And although organizational analytics programs are increasingly augmented with machine learning and advanced algorithms for predictive and prescriptive analytics, the ongoing need for business intelligence (BI) supporting descriptive and operational analytics applications will remain. What has changed over time, though, is the increasing sophistication of the data consumers and their growing awareness of the breadth and depth of corporate data assets.

Business BI consumers are no longer the “customers” of the data warehouse team – they are their partners. And this suggests that the best way to empower business information consumers is to provide accessibility to organizational data configured in ways that both simplify the production of analytics and speed time to knowledge.

In this paper, we consider the historical approaches to developing data warehouses and how growing end-user sophistication has increased the criticality of ensuring data clarity and consistency of the semantics across a variety of data sources. The paper then discusses the concept of enterprise data intelligence, and how that is facilitated through the use of a data catalog. Finally, we provide some recommendations associated with the characteristics of data intelligence tools and look at ways that data models, data governance tools, and data catalogs should interoperate to help re-envision the ways that data governance can drive business intelligence solutions.

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Topics: Data Modeling, Data Governance, Metadata
Products: ER/Studio Enterprise Team Edition, ER/Studio Data Architect, ER/Studio Business Architect, ER/Studio Team Server Core