Executive Summary

Operational Analytics: Putting Analytics to Work in Operational Systems

By James Taylor

Analytics, and the use of analytics to make organizations more effective, have become an increasingly hot topic over recent years. With books like Super Crunchers, Competing on Analytics and Analytics at Work promoting the approach, analytics have begun to move into the mainstream. There are many ways to use analytics to improve an organization’s effectiveness and efficiency, and many different ways to apply analytics. This report is about how to use analytics to improve day-to-day business operations.

When you apply analytics to business operations, especially when you apply analytics to operational systems, not every analytic technique or technology is appropriate. Improving the decisions in operational systems is the primary objective of applying analytics in those systems. This requires a focus on executable, operational analytics.

Those applying operational analytics are focused primarily on business process improvement and competitive position, though customer centricity also drives adoption. With a focus on decisions that involve identifying opportunity or assessing risk, customer decisions dominate the operational analytics landscape. In this study, case examples tackle scoring the value of prospective customers to improve targeting, improving customer satisfaction, effectively developing prospects, targeting direct-to-consumer marketing, and optimal scheduling and resource usage – all classic operational analytics problems.

A variety of technologies and approaches can be used to deliver operational analytics, and these can be broken down into different ways to build operational analytics, deploy them and evolve them over time. Whether building analytic models manually or automatically, no matter how those models are deployed into operations and whether those models adapt automatically or are updated manually, the value is clear – better decisions, more accurate decisions and thus more effective business operations.

The technology required for operational analytics is well established and proven. Yet challenges remain. To be successful in operational analytics, organizations must be clear about the decisions they are improving, must start small and expand systematically, and must invest in the management of organizational change. A focus on key performance indicators or metrics and how analytics impact them as well as a vision that is matched with a systematic plan are likewise essential.

Organizations can receive tremendous value from operational analytics and the success stories are becoming more numerous and more compelling. For most, however, there is real work to do if they are to successfully adopt this exciting set of technologies and approaches to changing their business for the better.

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