RESEARCH & RESOURCES

Big Data Analytics

TDWI Checklist Report // Big Data Analytics

August 10, 2010

There are two major trends causing organizations to rethink the way they approach doing analytics.

Big data. First, data volumes are exploding. More than a decade ago, the Data Warehouse Terabyte Club highlighted the few leading-edge organizations whose data warehouses had reached or exceeded a terabyte in size. Today, the notion of a terabyte club seems quaint, as many organizations have blasted through that threshold. In fact, it is now time to start a petabyte club, since a handful of companies, including Internet businesses, banks, and telecommunications companies, have publicly announced that their data warehouses will soon exceed a petabyte of data.

Deep analytics. Second, organizations want to perform “deep analytics” on these massive data warehouses. Deep analytics ranges from statistics—such as moving averages, correlations, and regressions—to complex functions such as graph analysis, market basket analysis, and tokenization. In addition, many organizations are embracing predictive analytics by using advanced machine learning algorithms, such as neural networks and decision trees, to anticipate behavior and events. Whereas in the past, organizations may have applied these types of analytics to a subset of data, today they want to analyze every transaction. The reason: profits.

This TDWI Checklist Report is designed to provide a basic set of guidelines for implementing big data analytics. The analytical techniques and data management structures of the past no longer work in this new era of big data. This report will help you take the first steps toward achieving a lasting competitive edge with analytics.

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