Melissa Team | Analyzing Data Quality, Data Management, Data Quality | data quality issues, data quality programs, direct mail, high return

By Elliot King

But an ad hoc, event-by-event assessment of performance generally is not good enough. Organizations need to measure themselves over time to determine how close they are to achieving a specific goal.

With this in mind, the IT research group Gartner has devised what it calls the Data Quality Maturity Model. The model has five stages–aware, reactive, proactive, managed and optimized. Other versions of the model list the stages as undisciplined, reactive, proactive, and governed. I think the undisciplined terminology is harsh but probably more descriptive.

As is often case, the first step in improvement is becoming aware that something needs attention. A surprisingly large number of companies do not address data quality issues in any systematic fashion. Once companies wake up to the fact that data quality is important, the next step is to react to the problems they uncover. Then, they can implement processes, procedures, policies, and technologies to minimize problems developing in the future.

In the final stages of the model, organizations come to recognize data quality as an important strategic competency integral to maximizing the value and business benefit of the information they have captured. Appropriate technologies, procedures and governance structures are in place and managed appropriately.

Perhaps the most important element of the model is not the description of the stages themselves, but the implication that data quality programs have to develop over time. After all, nobody starts out running. But too many enterprises have not even begun to crawl.