Simple Metrics for a Successful Data Governance
In the recent past, a number of firms have implemented a framework for Data Governance. Data Governance should not be considered as one off, but as an ongoing exercise to make sure Data is given the appropriate weight in the organisational priorities. Senior management tend to ask the question “Are we there yet?” to show the effectiveness of Data Governance. One way to show the progress of a successful Data Governance is through monitoring of certain key indicators. Let me share with you the top 4 metrics to identify the success of any data governance function.
1) Improvement in Data Quality Scores
2) Adherence to Data Management Standards and Processes
3) Reduction in risk events
4) Reduction in Data rectification costs
1) Improvement in Data Quality Scores
Each and every function which produces / owns the data should monitor the quality of data that it produces. Quality in simple terms is defined as Completeness, Accuracy and Timeliness of the data. There can be a three dimensional score on each of these dimensions or a consolidated score with appropriate weighting. The key is to ensure that these are measured and monitored for improvement.
2) Adherence to Data Management Standards and Processes
As part of the framework, almost all firms establish some kind of Standards / Policies that need to be followed by the all the employees. These documents state the guidelines that needs to be followed under various scenarios. For example, IT should have restricted access to production data, however in exceptional circumstances can be modified by IT with adequate control procedures with certain approvals. There has to be a certification process (either self-certification or through other means) by which each department should confirm adherence to Standards and Policies.
3) Reduction in Risk Events
Whenever there is a data quality issue, there is a possibility that it has resulted in a risk event.
An event could be
a) A penalty / fine imposed by a regulator caused by a misreporting
b) an inaccurate decision due to bad data
c) a client loss due to wrong reporting
Once Data Governance has been implemented, the firm should see a reduction in such type of risk events. If the risk events are continuing to occur, this shows malfunctioning of the governance function.
4) Reduction in data rectification costs
A firm incurs costs to rectify bad data or to enhance the data to meet its needs. The core principle of Data Governance is to “fix at source”, i.e. the bad data is not fixed by the consumers of such data, but is fixed at the source where it originates. Sometimes this could be within the organisational boundaries, or it could be from an external vendor. An organisation should track the costs of rectification to ensure that it is kept to the minimum.
A much more mature data governance function could identify other success metrics such as the impact of an upcoming regulation on key data assets and many more.
If your organisation is evaluating the progress of a successful of a data governance function, the above 4 simple metrics should provide a starting point.
This article was posted in LinkedIn at https://www.linkedin.com/pulse/simple-metrics-successful-data-governance-suriya-n-subramanian-fcca
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