By Elliot King

Elliot King

Edward Deming taught us long ago about the virtuous cycle of continual quality improvement–plan for change; execute the change; study the results and then take action to improve the process. But Deming’s PDSA (plan, do, study, act) cycle is a generic approach. The cycle has to be modified and customized to address targeted areas for quality improvement.

The key steps in the virtuous cycle for data quality improvement are
assessment, measurement, integration, improvement and management. Each process
is important but assessment is the critical first step.

Data quality assessment is a multi-pronged exercise and the key is to start at
the end. What business tasks and processes can be hurt by inaccurate, invalid
and incomplete data? And in what ways will poor quality data increase costs,
reduce revenues, hurt efficiencies or otherwise inflict pain on the
organization? This exercise helps to identify the data sources that should be
examined.

After you have determined where to look, you can profile your data to uncover
anomalies and data flaws and then bring those flaws to the attention of the data
users. In some cases, data anomalies may be harmless and have little impact on
actual business activities. In that case, no remedial action is warranted. But
when poor data quality does interfere with business operations then further
action is needed.

The last piece of the assessment puzzle is to correlate the identified data
issues with performance through a defined set of data quality business rules
such as completeness, accuracy, and consistency. The rules provide a framework
within which data quality can be measured.

The rule of thumb with assessment is relatively easy. First determine where poor
quality data will have the most impact within your organization. Then figure out
if it has to be fixed.


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