Data Governance and Data Quality

Regular readers know that I often blog about the common mistakes I have observed (and made) in my professional services and application development experience in data quality (for example, see my post: The Nine Circles of Data Quality Hell).

According to Wikipedia: “Data governance is an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, business process management, and risk management surrounding the handling of data in an organization.”

Since I have never formally used the term “data governance” with my clients, I have been researching what data governance is and how it specifically relates to data quality.

Thankfully, I found a great resource in Steve Sarsfield’s excellent book The Data Governance Imperative, where he explains:

“Data governance is about changing the hearts and minds of your company to see the value of information quality… data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise…at the root of the problems with managing your data are data quality problems… data governance guarantees that data can be trusted… …

Since I have never formally used the term “data governance” with my clients, I have been researching what data governance is and how it specifically relates to data quality.

Thankfully, I found a great resource in Steve Sarsfield’s excellent book The Data Governance Imperative, where he explains:

“Data governance is about changing the hearts and minds of your company to see the value of information quality… data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise…at the root of the problems with managing your data are data quality problems… data governance guarantees that data can be trusted… putting people in charge of fixing and preventing issues with data… to have fewer negative events as a result of poor data.”

Although the book covers data governance more comprehensively, I focused on three of my favorite data quality themes:

Business-IT Collaboration
Data Quality Assessments
People Power

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Business-IT Collaboration

Data governance establishes policies and procedures to align people throughout the organization. Successful data quality initiatives require the Business and IT to forge an ongoing and iterative collaboration. Neither the Business nor IT alone has all of the necessary knowledge and resources required to achieve data quality success. The Business usually owns the data and understands its meaning and use in the day-to-day operation of the enterprise and must partner with IT in defining the necessary data quality standards and processes.

Steve Sarsfield explains:

“Business users need to understand that data quality is everyone’s job and not just an issue with technology… the mantra of data governance is that technologists and business users must work together to define what good data is… constantly leverage both business users, who know the value of the data, and technologists, who can apply what the business users know to the data.”

Data Quality Assessments

Data quality assessments provide a much needed reality check for the perceptions and assumptions that the enterprise has about the quality of its data. Data quality assessments help with many tasks including verifying metadata, preparing meaningful questions for subject matter experts, understanding how data is being used, and most importantly – evaluating the ROI of data quality improvements. Building data quality monitoring functionality into the applications that support business processes provides the ability to measure the effect that poor data quality can have on decision-critical information.

Steve Sarsfield explains:

“In order to know if you’re winning in the fight against poor data quality, you have to keep score… use data quality scorecards to understand the detail about quality of data… and aggregate those scores into business value metrics… solid metrics… give you a baseline against which you can measure improvement over time.”

People Power

Although incredible advancements continue, technology alone cannot provide the solution. Data governance and data quality both require a holistic approach involving people, process and technology. However, by far the most important of the three is people. In my experience, it is always the people involved that make projects successful.

READ

What To Know About The Impact of Data Quality and Quantity In AI

Steve Sarsfield explains:

“The most important aspect of implementing data governance is that people power must be used to improve the processes within an organization. Technology will have its place, but it’s most importantly the people who set up new processes who make the biggest impact.”

Conclusion

Data governance provides the framework for evolving data quality from a project to an enterprise-wide initiative. By facilitating the collaboration of business and technical stakeholders, aligning data usage with business metrics, and enabling people to be responsible for data ownership and data quality, data governance provides for the ongoing management of the decision-critical information that drives the tactical and strategic initiatives essential to the enterprise’s mission to survive and thrive in today’s highly competitive and rapidly evolving marketplace.

TDWI World Conference Chicago 2009

Not So Strange Case of Dr. Technology and Mr. Business

Schrödinger’s Data Quality

The Three Musketeers of Data Quality