The link between Data Quality and Data Governance September 18, 2007Posted by Dan Power in Data Governance, Data Quality, Data Stewardship.
I suppose it’s possible to have an enterprise data quality program without having a data governance program. But I think having an enterprise-wide data governance program which includes data quality as one of its core focus areas makes more sense.
Data quality without data governance means having to correct the same errors over and over again, and not fixing the problem at the source. But data quality with data governance means bringing the data governance council up to speed on the issue, inviting the different business owners to present their views, and then defining a solution that will be funded, supported and enforced.
Data quality without data governance can be a “solution in search of a problem” – where an IT department adopts data quality tools without directly involving the business, and applies them “in the back room” so to speak. But data quality tools within the context of a corporate data governance program means the business is fully involved from the beginning, from forming the data governance council, to staffing the data management organization with business data stewards, to providing some or all of the funding, etc.
I’ve been encouraging clients lately to start by defining a data governance program, then to build the data governance council and the attending processes, then to start thinking about individual projects underneath the banner of the data governance program. That could include projects like implementing specific data quality tools or a Master Data Management hub.
The holistic approach that I generally recommend recognizes that for most business problems, there’s going to be:
- a People aspect (requiring organizational change, with impacts on corporate culture, accompanied by a political dimension)
- a Process aspect (as the organization realizes that treating information as a true corporate asset means some new work is going to have to be done by someone)
- a Technology aspect (usually requiring new platforms, applications or tools)
- an Information aspect (enriching internal data with trusted external content, for example)
This holistic approach to data governance usually involves wholesale improvements in data quality and in integration. If you want to better capture, store, manage and use information, you’re going to need better data entry procedures, better integration between applications and databases, and an efficient, automated way to find & correct incorrent data.
But you’re never going to be able to get away from the People side of things. People need to be convinced of the importance of data quality as an issue, and of the need for a comprehensive data governance framework as the best long term way to fix data quality problems.