How do you go about ensuring that new data collected, from the initial process to the final steps, is of good quality?

Whether the organisation wants to do this is the Million Dollar Question! The short answer to this question is?Continuous Process Improvement across all areas of data collection and usage. This typically involves implementing preventative steps to improve and monitor processes to prevent Data Quality problems, as opposed to correcting existing data defects.

A data model must be developed to reflect all the data of interest to the solution.

• The solution must be developed based on a rigorous Systems Development Life Cycle (SDLC) methodology.
• Data Security must be considered to ensure that the relevant information is protected from inappropriate access.
• Data Quality requirements must be considered as part of the development process and not as an afterthought.
• Data Governance must ensure that the data in scope has an owner who is accountable for the quality of the Data.

Data Quality should always be considered within the bigger picture of Data Management (DM). The bigger picture of DM can be appreciated by looking at a framework such as DAMA DMBOK. The DAMA DMBOK Framework shows the functional areas that need to be considered to ensure that the data which is collected and used is of good quality. So, for example: