For over 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. People augment organizational analytics programs with machine learning and advanced algorithms for predictive and prescriptive analytics. However, the ongoing need for business intelligence 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 intelligence consumers are no longer the customers of the data warehouse team–they are their partners. This suggests that the best way to empower business information consumers is to provide accessibility to organizational data. This data needs to be configured in ways that both simplify producing analytics and speed time to knowledge.
Read the whitepaper “Data catalogs, business glossaries, and data governance for customer business intelligence enablement” by David Loshin to learn more about the historical approaches to developing data warehouses. Also discover 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 whitepaper then discusses the concept of enterprise data intelligence, and how the use of a data catalog facilitates that.we provide some recommendations associated with the characteristics of data intelligence tools. And we look at ways that data models, data governance tools, and data catalogs should inter-operate to help re-envision how data governance can drive business intelligence solutions.