Passionate About Precision In Data And Information
I believe that data quality impacts business performance, customer service and financial results. Errors in data disrupt processes and, in particular, errors in master data and reference data propagate to cause multiple disruptions.
Over time there is a divergence between the content of information systems as originally designed and the way those systems are used in practice. Data elements in the system are stretched and misused to meet practical needs of operations. The resulting lack of clarity about the data in the system causes increasing inefficiency and disruption of business processes.
There are three steps to restoring data quality:
- make good data definitions - these form the standard to which the data should conform
- cleanse the data that does not conform
- make sure data stays clean
Defining The Data
Making good data definitions requires input from the people who work with the data and who understand the business process. How do they name and describe the things that they work with? Knowledge of the business and its processes is needed to create the definitions.
Most of this work takes place outside the operational information system. Only when proposed changes have been adequately reviewed are they applied as updates to the operational system.
Keeping Data Clean
The definitions created in the first step and the results found in the second step form the basis for redesign and reorganisation of the data management processes. And they provide the material for a detailed and specific set of rules that ensure the operational data continues to conform to the standards.