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. Empowering the data consumers requires some key aspects of operational data governance, including:
Read the 9-page whitepaper "Data Catalogs, Business Glossaries, and Data Governance for Customer BI Enablement" by David Loshin to learn about 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. Automated data catalogs are excellent for data discovery and documentation. However, they are enhanced with corporate knowledge that data architects can use to supplement and expand enterprise data awareness, and this paper examines how a modern enterprise data catalog will capture and document a broad array of metadata that help the data architects and practitioners enable data consumers. When considering how data catalogs enhance the context for reporting and analytics, we identify two insights that can influence the modern business intelligence stack. 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.
The whitepaper discusses:
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The presenter, David Loshin, is the President of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. David is a recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 20 years, David is also a prolific author regarding business intelligence best practices, with numerous books and papers on data management.
ER/Studio Enterprise Team Edition is the leading business-driven data architecture solution that combines multi-platform data modeling, business process modeling, and enterprise metadata for organizations of all sizes. With an extensive feature set, the ER/Studio suite provides robust logical and physical modeling with ER/Studio Data Architect, business process and conceptual modeling with ER/Studio Business Architect, business glossaries with ER/Studio Team Server, and more, to build the foundation for data governance programs.
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