Friday, 14 June 2013

5 steps to valuable data quality measurement

Kaplan and Norton's Balanced Scorecard is a way of defining and measuring strategic performance . It is probably the most used tool in management today. A data quality department may wish to have their progress measured on one or more of the quadrants. 

But the full corporate scorecard itself can provide great guidance as to the strategic direction of data quality measurement and remediation for the whole of your organisation.

1.  Identify key data items
For each goal in your balanced scorecard, identify the fields, tables and databases that are critical to the delivery of the scorecard objectives. If you can, also include the data items that are being used for the scorecard measures.

2. Prioritise each data item
 The key to this part of the exercise is to ensure that the most important data items for each measure get the full attention they deserve. You will possibly have a lengthy list of data items for each goal within your balanced scorecard. Cut out the items that are not important. If you still have a large amount of fields, try to give priorities and weighting to them.

3.  Agree business validation rules
Once you have a comprehensive list of fields and tables, go to your business and agree the business rules that you will use to validate each field. 

4.  Measure the quality of your data
Apply the business rules to all fields and tables as per above. Roll all the scores up into the items that you originally started with. You now have a scorecard of data quality in relation to your corporate Balanced Scorecard.

5.  Take it onwards with actions
What you have developed is a powerful baseline that informs your colleagues exactly how well the quality of data underpins your strategic corporate values and goals. Next steps are to prioritise any remedial action that is required, and agree all targets for improvements.

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