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TDWI Checklist Report | Modernizing a Data Warehouse for Business Advantage

September 29, 2014

There are many good reasons, both business and technical, for modernizing a data warehouse (DW). To sort it all out, it’s best to put business reasons first. After all, in IT we provide technology that supports business goals. When it comes to DW modernization, most business priorities align with the following:

• Big data. The organization wants to capture new data sources and leverage them for business advantage through business intelligence (BI) and analytics
• Advanced analytics. The business is under pressure to compete and grow, based on analytic insights, both short- and long-term
• Real time. Operations need high-performance and real-time technologies so they can close sales, serve customers, and react to market events sooner and more frequently

Technology goals are important, too, especially when they support the business goals of data warehouse modernization:

• Assure capacity for growth. BI/DW professionals can expect increases in data volumes, concurrent users, reports, analytics applications, sandboxes, and complex workloads for analytics, data integration, data quality, real time, and so on. In an effort to “future proof” warehouse capacity, users are increasingly depending on easily expanded data platforms based on racks, clusters, grids, elastic clouds, and Hadoop clusters.
• Diversify the types of data platforms in the data warehouse environment. A modern organization will practice several distinct BI/DW disciplines, such as reporting, visualization, OLAP, and many forms of advanced analytics. Because each discipline has unique workload characteristics, each may need its own standalone tool or platform within the extended data warehouse environment. This fact is driving many BI/DW teams to complement and extend the core DW with analytic databases, appliances, Hadoop, and streaming data tools.
• Turn on new functionality. Sometimes business and technology goals can be met with tools you already have by turning on functions that you haven’t used before, such as data federation services, in-memory functions, and in-database analytics.
• Rip and replace. When appropriate, migrate to new platforms and tools that can handle a broader range of data types and are faster, more scalable, tuned for analytics, and so on.

This Checklist Report explores the leading business reasons for modernizing a data warehouse, plus the common technical measures taken today for data warehouse modernization.

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