Data Quality Management versus Improvement - Just Semantics?

Data Quality Management versus Data Quality Improvement...what's the difference?

Maybe it's just me, but it seems that Data Quality Management is a term used in cases where Data Quality Improvement is more appropriate. In fact, Data Quality Management appears to be a universal term for all things dealing with data quality.

Management of data quality implies that some level of quality exists that needs to be managed. The quality may get better or deteriorate, but "management" suggests that efforts are aimed at keeping data quality at or above certain quality levels. These efforts may include fixing or cleaning up data, but the implication of the term "management" is maintenance.

Improvement of data quality implies that data is not at a desired level of quality and needs to be improved. These efforts could include assessing the current state of critical data, formulating improvement programs with targeted results (e.g., cut the error rate by 50% within 90 days), and implementing those programs. The term "improvement" conjures up a mix of changes aimed solely at making data quality better than it is.

Why should we care? I think the distinction is important because an organizational program labeled as "Data Quality Management" can cause certain paths to be taken. If the program is or should be around quality improvement, it is much more difficult to get out of data maintenance and reorient efforts around improvement and prevention.

Data quality management is ideal for IT organizations because IT is good at putting technologies in place to maintain the data. Leaving data quality to IT puts accountability for the quality of data with those who administer it. Plenty has been written and put into practice around reinforcing accountability for data quality with those who create it...usually the business. This makes sense because the creators and consumers of data have the most skin in the game when it comes to data quality. Since improving quality is usually a recipe of people, process, and technology changes, the business is probably better suited to drive a quality improvement effort. While it is not a universal truth, odds are that leaving data quality to IT will result in lots of technology being thrown at the problems, but data creators will still create poor quality data.

It seems to me that this is much more than semantics. The implications of labeling efforts around data quality inaccurately could steer organizations in less-than-desirable data quality directions. If an organization wants to maintain its data, then "data quality management" seems appropriate. However, if data quality goes beyond maintenance with a focus around improvement, then "data quality improvement" makes more sense to me. Data quality management versus improvement - just semantics? I think not.