Quality and Governance are Beyond the Data

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.

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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Podcast: Business Technology and Human-Speak

An excellent recent Marty Moseley blog post called for every one of us, regardless of where we sit within our organization chart, to learn conversational business-speak. 

This common call to action, perhaps first sounded by the George Colony blog post in August of 2006, rightfully emphasizes that “business is technology and technology is business” and therefore traditional IT needs to be renamed BT (Business Technology) and techies need to learn how to “engage in a discussion of process, customers, and operations, not esoteric references to SOA, Web services, and storage management.” 

Therefore, we need to always frame enterprise information initiatives (such as data governance and master data management) in a business context by using business language such as mitigated risks, reduced costs, or increased revenue, in order to help executives understand, as the highly recommended Tony Fisher book details, the need to view data as a strategic corporate asset.

While I do not disagree with any of these viewpoints, as I was reading the latest remarkable Daniel Pink book, I couldn’t help but wonder if what we really need to do is emphasize both Business Technology and (for lack of a better term) Human-Speak.

In this brief (approximately 9 minutes) OCDQ Podcast, I share some of my thoughts on this subject:

You can also download this podcast (MP3 file) by clicking on this link: Business Technology and Human-Speak

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DQ-Tip: “Data quality is about more than just improving your data...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Data quality is about more than just improving your data.

Ultimately, the goal is improving your organization.”

This DQ-Tip is from Tony Fisher's great book The Data Asset: How Smart Companies Govern Their Data for Business Success.

In the book, Fisher explains that one of the biggest mistakes organizations make is not viewing their data as a corporate asset.  This common misconception often prevents data quality from being rightfully viewed a critical priority. 

Data quality is misperceived to be an activity performed just for the sake of improving data.  When in fact, data quality is an activity performed for the sake of improving business processes.

“Better data leads to better decisions,” explains Fisher, “which ultimately leads to better business.  Therefore, the very success of your organization is highly dependent on the quality of your data.”

 

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