Without a doubt, business intelligence (BI) applications have successfully made their way into the hearts and minds of business users. BI systems are no longer the domain of IT departments. Each day, BI-based corporate dashboards are facilitating well-informed decisions by synthesizing, filtering and serving up bottom line numbers, facts and trends to users across the organization. With strategic BI tools at their fingertips, enterprise business users can establish, modify and tune business strategies and processes in relative real-time, helping them gain competitive advantage, improve business operations, drive profitability and achieve larger corporate goals. In the process, BI has become an everyday tool for customer service and revenue enhancement.

Even though there are signs of an economic upturn, that doesn’t mitigate the need for tighter controls and close scrutiny of operations by executives. Executives need to maintain this vigilance to squeeze out every ounce of productivity possible, though with less resources – less personnel, less budget and certainly much less time. Further complicating matters has been the real-time craze of the Web as both a competitive force and a productivity-boosting asset to BI systems through emerging Web services capabilities.

Yet, for all of the sophistication behind today’s BI suites and systems, their effectiveness within each enterprise hinges on the intellectual raw material they are asked to process…. incoming data. The application’s ability to help executives base decisions upon outputs is ultimately dependent upon the relative quality of data within the enterprise application. Whether the data depicts inventory, sales, suppliers, financials or customer metrics, without correct, comprehensive and timely data that accurately reflects an executive’s universe, BI systems will ultimately fail. As the Achilles’ heel of BI, data quality has proven time and time again to be an indispensable step in ensuring the ultimate success of multiple enterprise systems – most particularly business intelligence. Without it, the system can lose credibility among users, and in the utmost worst-case scenarios, lead to disastrous business decisions.

For instance, if you are an insurance carrier that wishes to learn from your BI dashboard, who the top 10 companies that employ your customers are, your premise would logically be that the names of those top companies were spelled consistently in all of your databases. However, if one of those top customers was First Manufacturing Company, and one data mart had it stored as “1stManufacturingCo,” another had it as “FirstMfg” and a third had it correct, each would show up lower on your list than the one company would if all data references were consistently accurate.