For Business Process Professionals

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	November 12, 2009 In-Database Analytics: The Heart Of The Predictive Enterprise by James G. Kobielus with Boris Evelson, Rob Karel, Charles Coit	Average: 10  (5 ratings)

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Executive Summary

Visionary organizations are adopting an emerging practice known as "in-database analytics," which supports more pervasive embedding of predictive models in business processes and mission-critical applications. With in-database analytics, enterprises migrate their predictive analysis (PA), data mining (DM), and other compute-intensive analytic functions from separate, standalone applications to execute in the enterprise data warehouse (EDW). Doing so allows IT professionals to leverage the EDW's full parallel-processing, scalability, and optimization features. In-database analytics can help enterprises cut costs, speed development, and tighten governance on advanced analytics initiatives. Business process and applications (BP&A) professionals should implement in-database analytics in conjunction with ongoing efforts to consolidate and scale their EDW.

TABLE OF CONTENTS

• Traditional Advanced Analytics Often Runs Into Scalability Issues
• New Twists In Traditional In-Database Analytics Practices
• New In-Database Approaches Are Promising But Far from Mature
• Industry Evolution Toward Comprehensive Cloud Analytics

RECOMMENDATIONS

• Push Advanced Analytics Deeply Into The Enterprise Data Warehouse

WHAT IT MEANS

• Future-Facing Enterprises Thrive On Ubiquitous In-Database Analytics
• Supplemental Material
• Related Research Documents

This is an excerpt

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