Where Do You Fit In?

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Management, Data Quality | , , , , , ,

By Elliot King

Too often, those of us with our noses to the grindstone have no time to look up. We are so busy putting out fires, monitoring and maintaining what we have, or trying to launch new initiatives that we never look around to see how other organizations are dealing with similar issues.

This may be particularly true in the data quality world. Data quality is often seen as an internal problem and it is often addressed differently in different settings, both organizationally and technically. Indeed, even the terminology is not consistent across industries.

So a recent study conducted by the International Association for Information and Data Quality (IAIDQ) working in conjunction with the Information Quality Program at the University of Arkansas, Little Rock (UALR-IQ) reveals some very interesting trends. The survey of 270 data quality professionals identified the top challenges faced by data quality professionals.

Heading the list is a lack of accountability and responsibility for data quality, followed by too many data and information silos to manage, a lack of awareness and discussion of the size and impact of data quality problems and a lack of understanding of what data quality means. These challenges are fundamental and each was tabbed by more than 50 percent of the respondents.

Considering the basic nature of the challenges, perhaps it should be no surprise that 66 percent of the respondents believed that the effectiveness of the data quality efforts in their organization were only OK (some goals were met) or poor (few goals were met.) Ironically, 70 percent claimed that their organizations recognized that data and information were important strategic assets and managed it with that in mind.

So what is driving companies to improve their data quality efforts? According to the survey, the top driver is just a general desire to improve the quality of data, which was cited by 68 percent of the respondents. Other important motivations to improve data quality were the desire to improve business intelligence, and compliance and legal considerations.