News

The What, Why, When, and How of Data Warehouse Modernization

Posted in: Business Intelligence - Oct 18, 2016

Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever as they support operationalized analytics and wring business value from machine data and other new forms of big data. In the age of big data analytics, it’s important to modernize a DW environment to keep it competitive and aligned with business goals.top

Mastering MDM: Lessons from B2B Leaders in Managing Data

Posted in: Master Data Management - Oct 13, 2016

Mastering MDM: Real-World Lessons in Managing Data. Best practices for building an A+ framework for MDM.top

Setting Your Data in Motion: Why Fluidity Is the Goal to Mastering MDM

Posted in: Master Data Management - Oct 13, 2016

Let Your Data Flow! Learn why fluidity is the key to mastering MDM. 5 best practices for setting your data in motion.top

Enabling Self-Service Analytics with Intelligent Data Integration

Posted in: Business Intelligence - Oct 05, 2016

One of the strongest trends in information technology (IT) today is self service, which puts the power of creating data-driven solutions in the hands of the business user. This way, IT organizations are offloaded; they needn’t create unique datasets, reports, and analyses per user, which frees up IT’s time for other tasks. Furthermore, a broad range of end-users – mostly mildly technical business people – needn’t wait for help from IT, thereby giving them greater agility and creativity, while reducing the time to value and allowing them to apply their business expertise to a well-targeted solution. Therefore, self service is a win-win situation – but only if key pieces of technology are in place.top

Driving Advanced Analytics with Intelligent Data Integration

Posted in: Business Intelligence - Oct 05, 2016

TDWI has noted in recent years an upsurge in the adoption of advanced analytics, whether query-based analytics (which relies on complex SQL statements to define recent business events) or predictive analytics (which uses data mining and statistical methods to anticipate future events). Regardless of the method, its requirements for data integration are quite different from the practices for data warehousing we know so well.top

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