Part of the answer lies in providing more user-friendly BI tools. Hatch offers four tips for achieving that goal.

1. Explore new user-friendly business intelligence tools. New ways of delivering BI can help in your quest to extend business intelligence throughout the enterprise. One new method to consider is BI accessed through a third party—for example, software as a service BI or on-demand BI. In addition, look into the availability of BI as an embedded capability within enterprise applications such as ERP and CRM. Since users are already familiar with your enterprise applications, they may find BI products offered through these providers easier to learn.

2. Find ways to integrate Web 2.0 information into BI. Web 2.0 data sources and other unstructured data do not obviate the need for traditional structured data, says Hatch, but they can be used to boost BI efforts. Amassing large sets of historical data reveals trends, performance metrics and specific business calculations: These are the foundation of most BI efforts. But the ability to enhance that historical data with relevant and timely information found in blogs, comments and competitors' websites is becoming more important for delivering actionable information throughout the enterprise.

3. Give users BI tools that they can be trained to use autonomously. Employees are more likely to use and embrace business intelligence tools that they can use independently. To create an environment of business intelligence self-sufficiency, establish a group composed of both business users and IT representatives to collaborate on prioritizing user needs and choosing or developing BI tools. Hatch also advises being attuned to inflated vendor claims and involving vendors in proof-of-concept and pilot projects.

4. Consider operational business intelligence. New BI offerings that automate data collection, assembly and delivery processes are one of the most promising areas of business intelligence. To figure out if they're right for you, look for data generated by business processes that lend themselves to automated analysis and even actions taken on the basis of that analysis. For example, some financial service organizations use applications that automatically analyze fluctuations in currency rates, and that automatically initiate trades based on those decisions. In many manufacturing organizations, data analysis is done automatically on the progress of chemical interactions—temperature, viscosity and color of a mixture, for example—and changes to the mixture are automatically made at the back end before it ever reaches people on the production line.