Everyone seems to have a data quality problem.
Overcoming data quality issues remains challenging for many organizations.
At Data Clan, we have developed a framework to help organizations better impact data quality outcomes, by focussing on three key factors that we have found are critical to success:
- Data quality specification – the starting point is a clear expression of what quality means. Our approach – following best practice from the Data Management Association (DAMA) and fully aligned with government policy – focusses on standard measurements of quality coupled with a clear understand that the goal is not perfect, it is fit-for-purpose.
- How to measure – the degree of automation of data quality measurement varies considerably, and as maturity grows the need for appropriate tooling to support measurement also grows. We can help you identify the kind of tooling you need, and how to deploy it to maximize its impact.
- Where to measure – we often find organizations focussed on static measurement, that is to say analysing the quality of datasets after the fact. Whilst this is one important time to measure data quality, it is rarely sufficient, especially as more and more systems become integrated, and data quality issues propagate more quickly. Our approach includes two further measurement points that help to ensure data starts right and stay right.