Change Management Strategies to Address Data Quality Opinions and Concerns

In my blog post, “Data Quality: Opinions and Impressions Matter the Most,” I explained why opinions and impressions are significant to data quality. Once a bad opinion or impression is created, it is quite hard to win back favor. When dealing with a serious organizational impact, it is paramount to initiate and lead a strategic change management initiative to address the gaps.

The following steps can help in forming an effective change management strategy:

a)      Understand and assess the impact across the affected user community. This can be achieved by informal or structured interview techniques or examining the way related data/information is being used. Also understand how people corroborate with other sources to validate the data infrastructure that once failed them.

b)      Do a reality check to understand if the root-causes of the problems are determined and addressed. Understand the risk scenarios that might lead to the data quality problems again.

c)       Socialize the finding from above two steps to the sponsors and make sure they appreciate the current state and current risk. Explain the gaps between the reality and the perceptions (or fears.) If there are no major gaps here, then there may not be a need for change management initiative yet.

d)      Clearly define the targets for change management initiative and define specifically what the perceptions are and what they should be. Then define how the perception can be changed. Special emphasis should be given to why there is a need for changes in the perceptions and what it is costing the organization. People typically appreciate if a dollar amount is shown for the loss organization is incurring due to the wrong perceptions on the data quality.

e)      Began campaigning for a pilot group, take inputs, change or adjust the campaigns and material used. When reaching out to the complete effected population, understand that individuals absorb the perceptional changes at different rates and so allow enough time for change to materialize.

f)       Finally, measure and re-baseline the perceptions and impressions about the data quality issue on hand. Then repeat the cycle again until the gap is addressed to a reasonable extent.

Once a serious data quality issue affects an organization, it is essential to initiate a professional change management process to understand the impacts of the perceptions and impressions the incident created.  This process will allow you to match findings against the reality (or truth) and, if required, initiate effective campaigns to drive the change needed. The cost and the effort for this is another reason why it is often said, “prevention is better than cure (more so for the quality issues.)"