When the concept of BI emerged, it was often viewed as icing on the cake. While transaction systems were considered necessary for running day-to-day operations, BI systems were initially intended to provide historical insights on how or why the company was performing as it was. Of course, the benefits of traditional "back office" BI, performed by an elite group of business analysts at division or corporate headquarters, has certainly helped thousands of organizations slice and dice corporate performance, enabling them to identify costs, profits, or opportunities for improvement. Nonetheless, BI has traditionally been a reactive solution that was the traditional domain of your company's "rocket scientists."

Recently, we've seen a growing cross section of customers working BI proactively into everyday operations. At Moneris, Canada's largest credit card processor, merchants who subscribe to the company's services are now receiving daily analytic reports benchmarking their performance against regional or product segment averages. Meanwhile, Ford is cross-purposing data from its engineering system to provide instant trend analyses on warranty and repairs to over 30,000 users across its network of affiliated dealerships. And a leading semiconductor manufacturer is applying the results of quality inspection analyses of wafer samples and converting it into visual, geocoded maps using ESRI mapping software. That's helping production line operators visualize immediately the results of their work.

Operational BI is obviously more immediate. Not as obviously, it also is more democratic. When BI applications are embedded into the daily routine of an organization, chances are, it will reach well beyond the few power users back at headquarters.

However, as BI grows more democratic, you need to take the implications of democracy seriously. Unlike transaction systems, which are typically required for processing the company's business, BI systems have traditionally been considered optional. Admittedly, when BI becomes embedded in day-to-day operations, that may no longer be the case. Nonetheless, there still is the need to ensure that the system is used to maximum benefit.

As with any new tool, you may face challenges if your colleagues view the system as just another diversion that takes them away from their daily work.

Make the Most of Your Enterprise Information
Consequently, the role of feedback loop becomes more important than ever as operational BI spreads analytics to the masses. Make it easy for the people who use the system to suggest changes or additions. Admittedly, for anyone who manages BI systems, listening to more voices makes life more complicated. But in all likelihood, by reaching a wider cross section of your organization, you are likely uncovering more opportunities to use your company's trove of information to wider benefit. Therefore, it only makes sense to listen. Remember, nobody knows their job better than the people on the line who do it – and the same thing applies to data.

Your company has just invested $500,000 in a new analytic system that delivers key performance metrics covering your field service force. The system provides data such as the number of service calls completed per day and the number of service repairs that were completed on the first visit. The following quarter, data from the system shows a 10 percent productivity gain. The question: is the data believable?

We convened a panel of ROI award winners at Summit 2004, this year's annual user conference, and learned some important tips when designing the metrics used in an analytic system. When people know they are being measured, it is human nature to get scared that management is watching and to temporarily work "by the book." However, the panelists said, if the metrics are arbitrary, eventually people catch on and operators revert to old habits while management tunes out. The moral of the story? When designing any kind of performance metric, get a reality check from the people who will be measured. Use the carrot rather than the stick. By gaining their input on what measures are meaningful, the metrics can be viewed as self-improvement, rather than punitive tools.