Last week’s episode of DM Radio on Information Management, co-hosted as always by Eric Kavanagh and Jim Ericson, was a panel discussion about how and why data governance can improve the quality of an organization’s data, and the featured guests were Dan Soceanu of DataFlux, Jim Orr of Trillium Software, Steve Sarsfield of Talend, and Brian Parish of iData.
The relationship between data quality and data governance is a common question, and perhaps mostly because data governance is still an evolving discipline. However, another contributing factor is the prevalence of the word “data” in the names given to most industry disciplines and enterprise information initiatives.
“Data governance goes well beyond just the data,” explained Orr. “Administration, business process, and technology are also important aspects, and therefore the term data governance can be misleading.”
“So perhaps a best practice of data governance is not calling it data governance,” remarked Ericson.
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From my perspective, data governance involves policies, people, business processes, data, and technology. However, all of those last four concepts (people, business process, data, and technology) are critical to every enterprise initiative.
So I agree with Orr because I think that the key concept differentiating data governance is its definition and enforcement of the policies that govern the complex ways that people, business processes, data, and technology interact.
As it relates to data quality, I believe that data governance provides the framework for evolving data quality from a project to an enterprise-wide program by facilitating the collaboration of business and technical stakeholders. Data governance aligns data usage with business processes through business relevant metrics, and enables people to be responsible for, among other things, data ownership and data quality.
“A basic form of data governance is tying the data quality metrics to their associated business processes and business impacts,” explained Sarsfield, the author of the great book The Data Governance Imperative, which explains that “the mantra of data governance is that technologists and business users must work together to define what good data is by constantly leveraging both business users, who know the value of the data, and technologists, who can apply what the business users know to the data.”
Data is used as the basis to make critical business decisions, and therefore “the key for data quality metrics is the confidence level that the organization has in the data,” explained Soceanu. Data-driven decisions are better than intuition-driven decisions, but lacking confidence about the quality of their data can lead organizations to rely more on intuition for their business decisions.
The Data Asset: How Smart Companies Govern Their Data for Business Success, written by Tony Fisher, the CEO of DataFlux, is another great book about data governance, which explains that “data quality is about more than just improving your data. Ultimately, the goal is improving your organization. Better data leads to better decisions, which leads to better business. Therefore, the very success of your organization is highly dependent on the quality of your data.”
Data is a strategic corporate asset and, by extension, data quality and data governance are both strategic corporate disciplines, because high quality data serves as a solid foundation for an organization’s success, empowering people, enabled by technology, to make better business decisions and optimize business performance.
Therefore, data quality and data governance both go well beyond just improving the quality of an organization’s data, because Quality and Governance are Beyond the Data.
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