When faced with a database that is screaming for attention, many employees choose to look the other way. For the brave few who realize the importance of data quality, it can be overwhelming to make the needed changes and launch the database cleansing initiative necessary to create a difference. Should they go it alone?

As Jim Harris writes for the Data Roundtable, one of the "laws" of data quality is that it's everyone's responsibility. Harris discussed the first and second laws of data quality in previous posts, noting that data is either being used or wasting space and that it has inertia. Law No. 2 expresses that without constant improvements and movements forward, a program will stall and deliver disappointing (if any) results. The overarching message for each of these laws is that data quality cannot be ignored, nor is it a one-time project to be forgotten about.

Data quality significantly impacts information technology and overall business performance. Therefore, all employees, managers and C-level executives need to get on board.

It's not that easy, however, few people want to get involved in data quality and will likely blame other groups for problems that can arise from low-quality data. This does nothing to address the problem at hand and wastes time, just as having duplicate or inaccurate information takes up valuable storage and system space.

"Data quality initiatives require the collaborative effort of business and technical stakeholders working together," he says. "A true collaboration is built on accepting shared ownership of the challenge as well as shared responsibility for its success - or failure."

Harris observes that a large number of data quality problems arise because no one is taking responsibility for the information. Without policies and guidelines, employees have no reference for how to manage and maintain accuracy in databases. By collaborating with colleagues, everyone will become "owners" of the data, taking on a stake in its quality and working together to solve the problems.

"Assuming that it is someone else's responsibility is a fundamental root case for your organization's data quality problems," Harris says.