The Goldilocks of Data Governance


For a number of my clients Data Governance is becoming a business as usual (BAU) function. They have created the data governance organisation (DGO), identified and appointed data owners and data stewards, and are now grappling with the day to day realities of managing data in large, politically sensitive and slow moving corporate environments.

As they reach this level of maturity a the challenge has shifted from the initial effort of selling the concept of data management to the operational challenge of meeting identified key performance indicators.

Typically, the DGO has been created to achieve some combination of:

  • operational data quality improvements
  • cost savings for enterprise data management
  • in order to prove compliance with legislation.

Two common mistakes can be made at this time.

At one extreme, the DGO can take an extremely limited view, focusing on a single business function or business process to measure improvement. If core metrics for other critical business functions are ignored then these business users will not get value from data governance, and it may ultimately fail as an enterprise programme.

At the other extreme, the DGO may try to measure too much, overwhelming business with a torrent of irrelevant reports and statistics. Once again, if the valuable information is buried a mass of trivia then business may again not see value and data governance may fail.

Like Goldilocks, the data governance organisation must choose to measure what is right for the business – neither too big nor too small! Take the needs of all critical business functions into account and focus on measuring improvement to the core data set that enables prioritised and critical business processes. Once you are showing improvements on these core elements you can refine and add to the core set in a sustainable manner.

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