Improve data quality

Data quality is about meeting explicit and implicit needs: does the data match the use? Data quality is a major problem for many organisations. At undesirable moments, for example, when replacing an application, migrating data can be very time-consuming. Bad data can also cause specific problems, such as the reliability of reports.

In general, data quality is an important pillar of data management. It requires continuous attention over an extended period of time in order to achieve and maintain this level. There are several routes you can take when you start working with data quality. You can focus on the structural design, but you can also work on concrete quality problems. See the roadmap below for a number of common routes.

We have many years of experience with data management and data quality in various organisations and situations. In the context of the Environment and Planning Act, we have developed a quality framework and a concrete step-by-step plan to ensure that government organisations are prepared for the Environment and Planning Act. We have drafted improvement plans for the design and improvement of data management and data quality.

We incorporate data quality in the design of processes and systems. We have participated in the development of quality dashboards. In addition, we examine possible improvement in the field of information provision from an organisational wide perspective, in which data quality is a common theme. We frequently notice data is fragmented across departments, processes and systems, leading to numerous inconsistencies. We then define roadmaps in which we place improvement measures in logical order. We also include data quality in situations when asked to assess or set up data management in a more general sense.

We have documented our vision on data quality and its relationship with data management in the form of a whitepaper.