The world is changing, people are changing, organisations are changing, and this is no different for data requirements. An organisation needs to accept this and makes sure that change requirements are captured, impact assessed, acted upon and communicated. 

During my career I’ve seen many approaches to configuration changes or data change management. I have seen approaches in organisations range from being totally non-existent right through to exemplary practices. It’s true that the process takes resources i.e. time and effort; to firstly put Change Management in place, and then the effort to maintain the process as a business as usual activity. However, this time and effort will reap dividends by reducing rework, ambiguity, process failure, etc. and create a defined and agreed data specification that will meet the needs of the business in terms of information requirements.

The principles around data change management

The management of change is like any other business process and needs to be documented, managed and communicated.  The objective of a change management process is to ensure that changes align with organisational strategies and objectives and minimise disruption. Change implementation involves the recording, evaluation, prioritisation, authorisation, planning and even testing of changes. The process should also help filter out changes that may have negative impacts or be risky to implement.

Change may unlock new benefits, support greater organisational capabilities and reduce risk but implementation of the change may introduce or heighten existing risk of failure. Controlling change may not completely remove the risk but Change Control activities will significantly reduce the risk. A key element of change control is naturally governance as the current integrity of the data needs to be protected.

Initiating change

Usually, the change process starts because a new requirement has been identified, an error needs to be fixed or the removal of ambiguity. A change requester should have comfort that their proposal is fully considered by the right people, within reasonable time and that they are informed of the outcome.  To be told simply ‘No’ without any reason will lead to frustration and disengagement from the process. Knowing that a request will be seriously considered and explored will encourage new ideas and not stifle them. Also, the decision-making history may be useful if/when future similar requests are made. 

Even if a change is rejected, having a record of the proposed change and reasons for raising it can mitigate risks if it is subsequently found that the change would be beneficial. The overall log of changes submitted will help show engagement of areas of the business with the overall data governance approach.

I recently found that being able to refer to the change log helped defuse tensions and issues related to a series of changes that were being reviewed.

Positives and Negatives of Change Management

So, what are the positives?

Good Change Management processes will provide: –

  • a history of changes and the decisions for those changes
  • a reference point to ‘undo’ changes that should not, with hind sight, have happened
  • stakeholders with information as to what ‘good’ looks like
  • a decision and authority mechanism where conflict on interests may occur – thus resolving different view points
  • quick decisions when needed
  • even if a change is rejected, there is an audit trail to show the change was considered and reasons for rejection

However, a poor Change Management process may: –

  • stifle new ideas
  • be perceived, perhaps correctly, as bureaucratic
  • prevent process improvement and even data quality improvements
  • lead to confusion as to what is ‘good’
  • lose the history of change and the ability to undo successfully
  • introduce incorrect classification of entities
  • seriously impact the quality of your data and in turn your decision-making processes.

So, if stakeholders agree, impact is assessed, communication is ready………..

But, understand the full impact of the change before you make it.

DPA help organisations to successfully specify, manage and exploit their data to best advantage. See more at

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