IBM Systems Magazine, Mainframe - November/December 2013 - (Page 28)

Teach the mainframe to predict the future using analytics and automation "Y 0AUL $I-ARZIO s )LLUSTRATION BY (ARRY #AMPBELL ow do you identify which clients are poised to leave for a competitor and preemptively extend just the right retention offer? How do you predict, with a high degree of certainty, if the loan you're about to write will eventually default or if the payment request you just received is likely to be fraudulent? If your answer to such questions is rooted in traditional business intelligence reporting-or worse, you find them unanswerable- your company is at a distinct and measureable competitive disadvantage. This fact was demonstrated in "Outperforming in a DataRich, Hyper-Connected World," a joint study of more than 1,100 executives worldwide, conducted by the IBM Center for Applied Insights in cooperation with the Economist Intelligence Unit and the IBM Institute of Business Value. The 2012 report showed that leaders in the advanced analysis of data patterns for H predicting future outcomes outperform their peers in several meaningful measurements: Ā Ã ÃÃũŮÃWLPHVÃWKHÃUHYHQXHÃJURZWK Ā7 Ã ÃÃÃ ZLFHÃWKHÃHDUQLQJVÃEHIRUHÃ interest, taxes, depreciation and amortization (EBITDA) ĀÃÃŪŭÃWLPHVÃWKHÃVWRFNÃSULFHÃ Ã Ã appreciation Analytics is no longer just a tool for improving business efficiency; predictive business analytics is quickly becoming a means for top-line growth and bottom-line savings. What does it take to turn your business systems into predictive decision management systems? Perhaps more importantly, what are the implications for your existing infrastructure? Not All Decisions Are Created Equal In "Decision Management Systems: A Practical Guide to Using Business Rules and 28 // NOVEMBER/DECEMBER 2013 Predictive Analytics," author James Taylor, CEO of Decision Management Solutions, outlines four principles that address the characteristic capabilities of a decision management system- which is an agile, analytic and adaptive system focused on the automation of decisions. From an infrastructure perspective, Taylor's first principle-begin with the decision in mind-is probably the most important. Considering that typical business systems are designed to handle repetitive tasks, he argues the best outcome is usually achieved by focusing on automating business decisions that are repeatable by nature. Decisions that are mainly of a strategic nature-those typically made by executive management to establish and maintain the overall direction of an organization- aren't good candidates for automation. Although high in value, they're usually ad hoc, oneoff decisions that aren't repeatable.

Table of Contents for the Digital Edition of IBM Systems Magazine, Mainframe - November/December 2013

Table of Contents
Editor's Desk: On the road with analytics
IBM Perspective: Extending IBM's commitment to big data and analytics
Trends: IBM Capacity Management Analytics for zEnterprise cost-effectively ensures optimal performance
IT Today: IBM DB2 Analytics Accelerator V4.1 expands the value of high-performance analytics
Case Study: The State of Consolidation: Oklahoma improves service levels by consolidating its IT on zEC12
Cover Story: Big Data's Big Impact: zEnterprise hybrid technology helps redefine business analytics
Feature: The System z Crystal Ball?: Teach the mainframe to predict the future using analytics and automation
Tech Corner: Alternative metrics deliver a more complete view of hardware for server RFPs
Administrator: QMF extends its analytics capabilities to help meet the demands of big data and mobile
Solutions: TestBase; Vanguard Offline
Stop Run: Historic property has much to teach retired IBMer
Reference Point - Global Events, Education, Resources for Mainframe
2014 Mainframe Solutions Edition Product Index

IBM Systems Magazine, Mainframe - November/December 2013