RESEARCH & RESOURCES

LESSON - Getting Started with Data Governance

By Daniel Teachey, Director of Corporate Communications, DataFlux

As companies collect more information about their customers, products, suppliers, inventory, and finances, it becomes more difficult to accurately maintain that information in a usable, logical framework. This can severely complicate regulatory efforts, because the information within applications and databases— data pertaining to customers, products, employees, suppliers, and financial transactions— provides the foundation for audit reports and various other compliance efforts.

The data management challenges facing today’s businesses stem from the way that IT systems have evolved. Enterprise data is frequently held in disparate applications across multiple departments and geographies. To address the spread of data, many companies implement enterprisewide data governance programs, which attempt to codify and enforce best practices for data management across the organization.

Data governance encompasses the people, processes, and technology that are required to create a consistent enterprise view of a company’s data. By concentrating on the health of the data, organizations can address the lifeblood of their enterprise, helping create better data to support any business initiative.

The Path to Data Governance

Like many enterprise initiatives, data governance programs often start small before finding the sponsorship and support needed to transcend organizational boundaries. Through an established data governance maturity model, organizations can identify and quantify precisely where they are—and where they can go—to create an environment that delivers and sustains high-quality information.

The data governance maturity model looks at the technology being utilized, along with the people and policies associated with the governance initiative, to ascertain the level of data governance sophistication within that enterprise. In the first stage, Undisciplined, an organization has few defined rules and policies regarding data quality and data integration. The same data may exist in multiple applications, and redundant data is often found in different sources, formats, and records.

The danger for Undisciplined companies is the real and constant threat that the underlying data will lead to bad business decisions that may, in turn, lead to security or compliance violations. Often, a cataclysmic failure (i.e., failed compliance audit, drastic decrease in customer satisfaction) shakes the organization out of complacency and leads to further progress.

At the next phase, the Reactive stage, a company begins to organize a data governance program, either through grassroots efforts or, more likely, through an executive-driven effort fueled by an earlier failure. At the Reactive stage, organizations try to reconcile the effects of inconsistent, inaccurate, or unreliable data as bad records are identified. Here, the gains are often seen on a departmental or divisional level, but the company is starting to establish some best practices for data governance.

At the Proactive stage, the data governance program becomes cross-functional and has explicit executive support. To build a single view of a customer, for example, every part of the organization—sales, marketing, shipping, finance—has to agree on what attributes make up a customer record. The final phase, the Governed stage, is where data is unified across data sources according to business rules established by an enterprise data governance team. At this final stage of the maturity model, a company has achieved a sophisticated data strategy and framework, and a major culture shift has occurred. Instead of treating issues of data quality and data integration as a series of tactical projects, these companies have a comprehensive program that elevates the process of managing business-critical data.

Although individual applications are still in use by a Governed company, the data that they access comes from a single repository that is propagated across the IT infrastructure. This provides the ultimate in control for the enterprise, as all reports and dashboards pull from the same pool of information.

This article originally appeared in the issue of .

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