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Data Quality Assurance vs. Risk Assessment
We have Data quality assurance controls missing in our environment, and it seems to be a very high risk situation. If a data quality assurance control is missing, does it mean that risk rating will be high?
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    • You may refer Data Quality Assurance Methods and DQ Method Risk Assessment Checklist for a back-ground. In-brief, A lack of Data Quality Assurance Control does not mean that Data Quality risk rating is high. There are many factors, one needs to take into account to assess the risk and create business case.

    A lack of Data Quality Assurance method has to be seen from following perspectives:

    • Volume and Value Risk: How much volume and value of transactions may be impacted due to this gap?.
    • Probability Assessment: What is the probability of a data quality issue happening?, and what has been the history?. For example- let us say that you don't have the tracking of duplicate file upload in a data exchange interface. However, if there has been not data quality issue for last many years, one needs to look at the priority of fixing the gap.
    • Post-Facto Trapping and Fixing: If you have mechanisms to trap the data quality issue as soon as it happens and have safe and secure methods to fix it, before it gets cascaded further, the risk gets reduced.
    • Criticality of Data: If the lack of DQ assurance gap can lead to a data quality issues related to financial data or data, which can give us a financial, legal or compliance exposure, the risk gets higher.
    • Level of external impact- If the DQ assurance gap may lead to a data quality issue, which cannot be trapped and can touch the external entities, the risk goes higher. For example, if a lack of batch-process control may lead to generation of wrong billing statement.

    NOTE- The above factors are not related to a 'present' data quality issues, but risk related to a lack of data quality assurance control, which 'may' lead to a data quality issue.

    DISCLAIMER: We don't recommend a lenient view on lack of data quality assurance methods. However, in a real-life situation, there is always lesser money compared to what is needed to fix all DQ gaps. The above considerations help you to prioritize and plan the DQ assurance gaps. We have seen that getting them fixed as part of larger initiatives always helps.
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