5 data governance mistakes to avoid

Keep ‘data governance’ from becoming a dirty word in your organization

By Jill Dyché and Kimberly Nevala

Data governance has become a veritable rubric for all things data. Google the term and you’ll come up with references to data quality, metadata, data warehousing, data ownership and data security – to name just a few.

Dyche sascom
Jill Dyché is an acknowledged speaker, author, and blogger on the topic of aligning IT with business solutions. As the Vice President of SAS Best Practices, she speaks, writes, and blogs about the business value of analytics and information.

Data governance is, simply, an organizing framework that aligns strategy, defines objectives, and establishes policies for enterprise information. As promising as that might sound, data governance has failed in more than one well-meaning company because people misinterpreted its meaning, its value, and the shape it would eventually take. Once data governance becomes a dirty word, an organization rarely gets a second chance. “You can’t use the word governance here,” one executive confided recently. “We’ll have to call it something else.” Here, we provide advice to save you from similar fates.

MISTAKE #1: Failing to define data governance

Using “data governance” synonymously with “data management” is a common mistake. Data governance is the decision-rights and policymaking framework for corporate data. Data management is the tactical execution of those policies. Both require executive commitment, and both require investment, but data governance is a business-driven process, while data management is an IT function. How you define data governance and how your organization understands it is crucial. Your governance program must clearly define and articulate its mission and value.

MISTAKE #2: Failing to design data governance

Designing data governance means tailoring it to your company’s specific culture, organizational structures, and decision-making processes. If you design a program for minimizing security breaches when your company cares more about enriching the customer experience, you’re designing the wrong program. 

Your company’s needs are unique and your data definitions, rules, and policies should be too. Deliberate design ensures that governance supports the way your company does business. It also ensures that constituents know what data governance will look like before it’s launched.

sascom 20141q kimberly nevala
Kimberly Nevala is responsible for industry education, key client strategies, and market analysis in the areas of business intelligence and analytics, data governance, and master data management at SAS. She is the co-author of the first e-book on data governance, The Data Governance e-Book: Morals, Maps and Mechanics.

MISTAKE #3: Prematurely launching a council

An earnest visionary perceives the need for data governance. A council of data stakeholders is convened. Everyone agrees to meet regularly, discuss prevailing data issues and address problems. At the follow-up meeting, fewer people show up. Someone complains the company has never really defined the term “customer.” Someone else pipes up about bad data on the billing system. A sidebar conversation starts on CRM consolidation. A third meeting never happens. In this all-too-common example, data governance isn’t overtly canceled. It simply fizzles. Until a core team of stakeholders deliberately designs a data governance framework that includes guiding principles, decision rights, and the appropriate governing bodies, no cross-functional council will have the clarity or the mission to affect change.

MISTAKE #4: Treating data governance as a project

In a well-intended effort to fix what’s broken, many companies will announce a data governance “project” with flourish and fanfare. When data governance is formed as a discrete effort, however, instead of being “baked in” to existing processes, it will fail.

When an initiative is deemed a project, it is, by definition, finite. The reality of data governance is that it should be continuous and systemic. As information needs change, data volumes increase, and new data enters the organization via new systems or third parties, decisions about how to treat, access, clean and enforce rules about data will not only continue, they’ll proliferate. A structured, formal, and permanent process should be retrofitted into the way a company develops its data and conducts its business.

MISTAKE #5: Prematurely pitching data governance

In the first phase of its data governance program, a national financial services company solicited several business and IT subject-matter experts to function as data stewards. The stewards were tasked with identifying high-impact data issues within their domains that governance would rectify. The stewards did an excellent job. The problem: There was no defined procedure to validate, prioritize or resolve the ever-increasing flood of identified business problems whose root causes could be attributed to data issues.

The team expended significant effort to expose painful data sores without a method to heal them. A majority of the issues uncovered were good candidates for governance, but the lack of appropriate expectation-setting led to frustration and mistrust. Data governance became a dirty word, and getting business owners back to the table to talk about implementation remained an uphill battle.

Conclusion: Take your time and do it right

The mantra “think globally, act locally” is particularly apt when embarking upon data governance. The issues addressed by data governance are far-flung and pervasive, so successful programs begin with a series of tightly scoped initiatives with clearly articulated value and sponsorship.

While an incremental approach takes time, not to mention patience, it engenders support by demonstrating the value of governance in a context relevant to each stakeholder or sponsor. Most important, a phased approach establishes data governance as a repeatable, core business practice rather than one-time project.

A longer version of this article was originally published as a white paper by TDWI titled Ten mistakes to avoid when launching your data governance program.

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Create a data governance master plan

Follow these broad steps to kick-start your data governance program with a master plan:

  • Inventory business needs and requirements for data governance.
  • Draft a data governance charter document.
  • Create guiding principles for data governance.
  • Design a stakeholder interaction system, including decision-making bodies.
  • Allocate decision rights, specifying accountability for data decisions and escalation processes.
  • Recommend the type of data stewardship most appropriate for the corporate culture.

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