Effective Analytic Strategies and Successful Data Management

By Phil Hatfield April 22, 2013

In a previous post, I wrote about how an effective analytics strategy must first start with good quality data — and lots of it. While good data provides the foundation on which to build, managing that data provides the framework for success.

The first step in developing an effective data management process is to build an analytic database that links all the data sources to be used in an analysis. But doing so can be a challenge in and of itself. As my colleagues Darlene Pogrebinsky and Gerry Gloskin and I outlined previously, managing analytic data requires the ability to deal with asynchronous data element update schedules as well as the development of multiple indexes to handle ad hoc analytic queries.  Managing data for analytics is a different discipline requiring special techniques compared with managing operational data stores. Above all, managing data for analytics requires a flexible approach.

Successful data management necessitates the use of an agile, practical approach. Kelley Blue Book vice president Dan Ingle seems to concur in an excerpt from Secrets of Analytical Leaders: Insights from Information Insiders, by Wayne Eckerson, appearing on InformationManagement.com: The “keys to success are pretty straightforward: 1) build things iteratively and incrementally using an agile development process, 2) adapt to circumstances and not be wedded to a particular solution or methodology, and 3) foster teamwork to increase productivity and effectiveness.”

Further, data management benefits from an iterative approach so work can change as needed. On InformationManagement.com, software consultant Biraja Ghoshal offers some similar thoughts regarding how to get started with an analytics initiative. Where to begin? “The process of delivering business results through analytics is one of continuous improvement,” he says. So rolling out a new analytics strategy does not need full integration at the outset. Rather, results can still be achieved using a phased implementation that will also allow for testing and refinement before full integration.

To remain competitive, continue to look for new data sources and/or additions to existing data that were previously unavailable, along with new ways to analyze and integrate information. In addition, monitor changing analytic requirements so you can adjust your data management strategy accordingly. In combination, you’ll be putting the best data to the best use.