Supporting Business Intelligence platforms in mid to large organizations is not just about technology nor is it solely about the information that the technology delivers. Striking the right balance between the type of information required and the framework for delivery requires a defined and methodical approach built on solid governance principles that merge business drivers with appropriate enabling technologies.

Organizations have seen the fruits of implementing data warehouses and are moving at rapid speed to establish Business Intelligence models that seek to leverage an often under utilized and valuable asset - information. The challenge with many of these efforts centers on 1) defining the strategy and 2) building the supporting infrastructure to ensure the overall success of the program. This was no more evident than when I recently attended a BI conference that included 1,500 peers representing dozens of companies looking to identify best in class practices that would take them to the next level. It quickly became evident, based on discussion with industry peers that many organizations are looking to take a giant leap into the world of BI without addressing some fundamental basics.

Defining the Strategy

A solid BI program starts with the basics. A formal data quality program must sit at the base of any effort. In fact, I believe that organizations with existing data quality programs in place to support their source systems are more likely to succeed in establishing a solid BI framework. These organizations get it and they understand the importance of ensuring that accurate and reliable information flows through their core systems. Financial institutions excel in this area because data accuracy is what keeps them in business. Customer deposits, earnings, portfolio summaries have to be correct, you do not get a second chance to be wrong and these companies know that their credibility is vital to sustaining customer relationships and ultimately, profitability. The following are some key items to consider when establishing a BI program in your organization:

-- Establish a Data Quality Czar with primary dedicated resources charged with correcting identified errors, conducting independent audits of existing data, and establishing a metrics program aimed at fostering improved data entry and production support.

-- Ensure that you engage your respective business partners across the Enterprise at the same time to ensure that data mart development is not done in a vacuum. Remember that many functional areas and service teams use the same data in different ways so make sure that you look at marts from a functional AND service channel perspective as well. There may be economies of scale opportunities right in front of you.

-- Avoid the "build it and they will come" mentality by engaging the business and having those stakeholders engage key customers to get a sense of not just what data they are looking for but what they are looking to accomplish with the information.

-- Re-focus ROI opportunities away from the typical "less time" to produce reports and instead incorporate increased capacity estimates, headcount avoidance and demand forecasting from a reporting perspective.

-- Avoid a common mistake that organizations make when implementing new technologies - Do Not Over Customize. Remember, delivering accurate and reliable information in the most streamlined manner possible has to remain the primary goal of the effort. Sending the report to a PDA with embedded images can come later.

-- Finally, don't implement a Cadillac if the Pinto gets you to the same place. For smaller organizations this especially rings true. Leveraging 30-40% of the capability of a technology asset means that you probably missed a tremendous opportunity on the ROI side of the house.