Business Intelligence for Less Than $25K

Business Intelligence (BI) provides valuable and meaningful data in a dashboard environment, but it can quickly become expensive. Associated Grocers (AG) in Baton Rouge implemented BI for less than $25,000. Senior Vice President Steven A. Miller explains how it was done.

I began learning the benefits of BI in 2006 by reading such magazines as CIO and DM Review. I was intrigued to find that companies that implemented BI found information that provided the basis for targeted investment and growth of their companies. I wondered what was buried deep in our data. Cost estimates for such enterprise-level tools grew well into the six figure range. Even after negotiating the lowest possible cost for a turn-key implementation, the cost was still too high for such a new technology deployment at AG. Justification was based on information that we expected to present, but required people to act on the information. Without the right system and people, the returns might not materialize. In October 2006, I attended a BI presentation from a local vendor at a business and technology trade show in Baton Rouge, La. The vendor showed me that by using Microsoft tools such as SQL 2005, report services and analysis services, a company could create a BI tool using Excel as a means for display. Since AG had recently standardized on Microsoft products through a Microsoft Enterprise Agreement (MSEA), it was conceivable that this would be the most effective way for AG to get started on our BI quest, but we had no idea how to start. (Also read Four Tips for Better Business Intelligence.)

Just a few weeks later, the accounting department approached me with a series of questions concerning profitability. They were concerned about fluctuations in margins on freight and gross sales. I took the opportunity to explain that if we cubed up the data in SQL and allowed users to connect to the data using Excel, reports could be generated without running any lengthy access queries--rapid analysis of the problem at hand was the only justification I could offer. I convinced the organization to spend $5,000 with a local vendor to show us how to cube up the data and build reports in Excel using pivot tables. I called it the mini-data mart. Over the course of the next few weeks, the vendor began learning our business and assisted in creating cubes that analyzed the sources of margin fluctuations on sales and freight. Unfortunately, the gross margin analysis proved too difficult to determine because of the selling system structure and varied promotional programs. As a result, there was no way of accurately backing out the current markup and applying the prior markup to determine the difference in expected margin. Valuing Business Intelligence.)

The freight revenue analysis, however, was a fairly straightforward application. The accounting team arrived at the same answer that my team did in about the same amount of time, even though I was creating an entirely new system. Despite the fact that freight revenue had increased, the number of cases delivered to longer distances from the distribution center was less than expected. This report is now a standard report for AG.