How to Effectively Use Data Governance Maturity Models

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3-stages-of-maturityOne of the leading business management concepts today is the Continuous Improvement Process (CIP) methodology. And as CIP practitioners know, regular feedback and assessment of change impacts are critical. A first step in this process is establishing a baseline of the current operational environment. For Data Management organizations the result of this baseline is measured against a Data Governance maturity model.

Fundamentally, the factors a Data Governance maturity model considers are related to the organizational construct, procedural and qualitative definitions, and the operational support capabilities. In other words, it is an assessment of the people, process, and technology components of a Data Governance program.

One common mistake many organizations make when assessing their Data Governance (DG) maturity is to assume there is or should be a single answer to this question.  That is rarely the case.  Typically, each data domain (e.g. Material, Supplier, Customer, etc.) has a differing level of maturity, and that is OK.  Depending upon many factors within the organization, the business impacts and requirements for each domain - higher or lower levels of maturity are necessary.

Identifying the gap between the current maturity and what is required to successfully support and operate the business requires the DG team to collaborate primarily with key business stakeholders. This includes several levels of the organization, from the data requestors and users to the executives determining the business objectives and priorities.  Knowledge of not only the existing operational challenges, but also future direction of the business is critical to properly aligning the DG program.

Many times the “problem” domain is known based upon business impacts, but the details of the DG maturity assessment are where actionable items are identified within the people, process and technology components. To be successful, these three areas must have complimentary levels of maturity.  Establishing the right balance between these areas and developing an improvement plan that achieves that balance is critical.

A misstep many organizations make is implementing new technology and assuming that will solve their problems.  This can result in either the automation of a bad process or the failure of the technology project due to the lack of definitions and specifications.  When a change in technology is indicated by the maturity model, ensure the other areas (people and process) are also addressed as part of the plan.

Using a maturity model to benchmark and monitor a Data Governance program’s capabilities, CIP opportunities, and improvements is a proven method for creating measurable business value.

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