Formulate Essential Data Governance Practices

The creation of a  Data Governance function at your organization is a critical success factor in implementing Master Data Management.  Just like any machine on a factory floor, Master Data is an Asset.  An Asset implies ownership, maintenance and value creation: so too with Master Data.  To borrow an analogy from the Manufacturing world, Transactional data is the Widget, and Master data is one of the machines that makes the Widget.  It is part of your organization’s Value Chain.

Unfortunately, firms starting on the road to MDM fall peril to one of two pitfalls on either extreme of the Data Governance mandate.  The first pitfall is to treat MDM as a one-time project, not a program.  Projects have an end-date, Programs are continual.  Have you ever heard of an Asset Maintenance Project?  That’s a recipe for crisis.  Firms which maintain their assets as a Program do far better.

The second pitfall is ask the Data Governance team to do too much, too fast.  Governance cannot do much without some asset to govern.  Have you ever heard of a Machinery Maintenance Program instituted before you figured out what type of machinery you needed, what the output requirements were, or before you made the capital purchase?  I haven’t either.  First, you acquire the capital.  You do so with the expectation that you will maintain it.  Then you put it into production. Then you formulate the maintenance schedule and execute that plan.

In order to successfully stand up a Data Governance function for your Master Data Program, you’ll need to understand these essential roles in Data Governance: Executive Steering, Data Owners, Data Stewards. 

Follow these Do’s and Don’ts:

Do establish an Executive Steering Committee for all Master Data practices in your enterprise, focused upon strategic requirements, metrics and accountability.

Do establish Data Quality Metrics.   Tie them to strategic drivers of the business. Review them regularly.  Your MDM toolset should provide analytics or dashboards to provide this view.

Don’t ask the Steering Committee to own the data model or processes – that is the Data Ownership role.

Do establish a Data Ownership group for each domain of Master Data.  Ownership teams are typically cross-functional, not simply the AR manager for Customer Master Data, or the HR manager for Employee Master Data.  As you evolve down the maturity path, you will find that master data has a broad set of stakeholders – Do be ready to be inclusive.

Do establish regular status meetings where Data Ownership meets with the Executive Steering Committee to review priorities and issues.

Don’t require that Data Owners “handle the data”.  That is the Data Stewardship role.

Do formalize a Data Stewardship team for each domain of Master Data.  Data Stewards are “data people” – business people who live in the data, but with no technical skills required, per se (though technology people can contribute to a Data Stewardship team).

Don’t restrict Data Stewards to just  the people who report to the Data Owner – think cross-functional!

Do anticipate conflicts – Data Owners should have some political skills.  The reality is that Master Data is valuable to a broad set of constituencies within an enterprise.  Be practical as it relates to one faction’s “Wish List” and keep moving the ball forward.

Without a Data Governance function, MDM tends to be a one-time project (“Clean it Once”) and fails to deliver real value.  Without a clear vision of how Data Governance support MDM, it can hold things up. A rational Data Governance function does not need to hold up the execution of a Master Data project – it supports it. Keep Data Governance strategic, cross functional, and flexible. Then, let the MDM technology team deliver the tools.

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