Too often, in my humble opinion, those of us in IT think in a problem/solution paradigm.  The line of reasoning goes something like this–our company has a business problem that can be fixed with some kind of software. 


Or the IT infrastructure itself is not performing at an acceptable level and needs to be repaired or enhanced via hardware, software or some combination of both. When people think in terms of “fixes” and “repairs,” not surprisingly after a “problem” has been identified, the obvious next step is to find a vendor that can supply the tools needed to fix or repair the situation.


In the data quality arena, however, simply looking for “tools” to fix problems may not be the best approach to selecting a data quality software vendor.  In fact, data quality should not be thought of as a discrete series of problems that need to be resolved, but instead should be understood as an iterative process that needs to be managed.


The process begins by identifying and measuring the impact poor data quality has on achieving business goals and moves through the development of business rules to define data quality, designing and implementing data improvement strategies, data cleansing, monitoring results on an ongoing basis and then starting all over again.


Different software applications have been developed to address different points of the process. So while the first step in selecting a data quality vendor is to identify where in the process their software can be applied, and whether it will accomplish your goals within the context of your infrastructure – that can’t be the last step. You want to select a vendor that can be your partner in data quality improvement over the long term.


So what should a partner be able to provide?  Perhaps the most important is that the vendor has an understanding of the big picture and the ability to help you address that big picture now and in the future. Improving data quality is not just name-and-address cleansing, geo-coding or supplementing data via third-party enhancements and so on. It represents a sustained effort to make sure a company has the quality of data it needs to meet its business objectives.


This approach does not mean that a single vendor must provide technology solutions that can be applied to every single point in the data improvement process.  But each vendor used must be able to understand the strategy for data quality and be able to make an appropriate contribution to the overall goal.

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