Also, take the case of a large organization with millions of data files on inventory items sourced from tens of thousands of suppliers. Data needed to be organized for easy access to facilitate rapid shipments to virtually anywhere in the world at a moment’s notice. However, when the organization closely examined the state of its inventory data, comparing records and files in multiple systems, it discovered numerous duplicate serial numbers. One particular serial number had mistakenly been applied to both a common item used in trash collection and to a vital electronic circuit board that held the brains behind a sophisticated piece of hardware. When the BI dashboard was queried for the number of deployments of that circuit board to gauge what costs an upgrade or recall might incur, the answer came back in the millions rather than hundreds. The solution to this problem called for a data quality implementation that ensured data for each serial number was distinct, and the rest of the data feeding their decision-support and inventory management systems was accurate and up to date.

Data quality problems typically result from seemingly inconsequential, easy to miss errors… usually human in origin. For instance, type-Os among the few lines of descriptive information from a legacy system that now need to be loaded into a new BI system, or in fixed fields originally hard coded into an order entry system forcing data entry operators to type into another field, irregardless of what that field was truly supposed to contain. Or perhaps the error resulted from the creative manner in which a support person used the status or comments field to capture valuable information. Several years later that creative interpretation of information is being tapped as the centerpiece of a new application. As so often happens in IT, the original intent was noble and accurate, but as times and requirements change the data must change with them.

Quite obviously, correcting data anomalies can go a long way towards recouping your BI investment and ensuring that the project will meet its stated goals. To that end, there are 10 steps to follow that will take the data quality risk out of BI.