Buzzword: “Data Quality”

Data Quality - everyone wants it, and everyone complains that they don’t have “good quality data”.  But how do you define data quality? What are the business benefits associated with the investment required to improve the quality of corporate data? Those are the questions you should be asking when approached by an angry business user complaining they can’t do their job because their data source(s) stink.

I think the most common misconception around DQ is that it’s an all or nothing proposition.  In reality there’s a cost-benefit analysis required to determine the payback associated with improving data quality.  Raising the data quality bar has a cost, and unless you can justify the expenditure you’re wasting corporate resources.

The business case can range from a simple exercise in comparing the cost of automating vs the current cost of manual labor required to fix and/or circumnavigate around incorrect data elements.  For example, it doesn’t make sense to spend a half million dollars implementing a data quality technology solution, to save a couple of hours a week of a business or data analyst’s time.  On the other end of the spectrum are strategic implications such as financial reporting and risk management, where the reputation of the company is at stake (just ask Fannie Mae).

Look at data quality as a bar that you raise and lower based on cost, business benefit, risk tolerance, and other factors that are important to the corporation.

This entry was posted on 20. May 2008 at 17:49 and is filed under Industry Buzzwords. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response or trackback from your own site.