Data governance means different things to different people


Easter 2017 has just passed.

 

For practicing Christians, Easter is an important religious holiday. Arguably, Easter commensurate the founding premise of the Christian faith. In South Africa we celebrate a long weekend – both Easter Friday and the Monday are public holidays.

For Christians, the Easter Sunday Church service is one of the most important of the year. For them, Easter represents their faith.

In recent decades, Easter has become commercialized. For retailers it is an opportunity to sell lots of chocolate. And, for some families, the Sunday morning Easter egg hunt has taken over from the church service as the core focus of Easter.

For Jews, on the other hand Easter is a non-event. The weekend, however, is typically very close to the Jewish holy days of Peisach. Although they may not recognize Easter, religious Jews re also observing this time of the year.

For some of us, the Easter weekend eastermay simply be seen as an opportunity to take two days off work and enjoy a long weekend.

A Google search for images reflecting Easter reflects some of this diversity.

 

One concept – but with very different meanings depending on the person.

Similarly, the concept of data governance may mean different things to different people, depending on their goals and objectives.

From an audit perspective, data governance may be seen a series of checks and balances for the appropriate use of data.

From a compliance perspective, data governance may be seen as defining the policies to ensure compliance with data related regulations and dependencies.

From a master data management vendors perspective, governance may be defined as processes to confirm and merge duplicate customers.

If my business goals is to acheive data quality, I may think of data governance as the processes required to define and implement data quality rules, and to act on the results.

And many more….

Each of these definitions is correct – yet none of them are complete.

The challenge when implementing data governance is to be able to see past individual, siloed definitions and requirements and build towards a bigger, unifying goal.

When we make this connection we are able to join the dots of each of our myriad, individual interpretations and begin the move towards unlocking value.

 

 

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