Illustration via the SlideShare presentation: The Social Intranet
How do you know if you have poor data quality?
How do you know what your business processes and technology are doing to your data?
Waiting for poor data quality to reveal itself is like waiting until the bread pops up to see if you burnt your toast, at which point it is too late to save the bread—after all, it’s not like you can reactively cleanse the burnt toast.
Extending the analogy, let’s imagine that the business process is toasting, the technology is the toaster, and the data is the toast, which is being prepared for an end user. (We could also imagine that the data is the bread and information is the toast.)
A more proactive approach to data quality begins with data and process transparency, which can help you monitor the quality of your data in much the same way as a transparent toaster could help you monitor your bread during the toasting process.
Performing data profiling and data quality assessments can provide insight into the quality of your data, but these efforts must include identifying the related business processes, technology, and end users of the data being analyzed.
However, the most important aspect is to openly share this preliminary analysis of the data, business, and technology landscape since it provides detailed insights about potential problems, which helps the organization better evaluate possible solutions.
Data and process transparency must also be maintained as improvement initiatives are implemented. Regularly repeat the cycle of analysis and publication of its findings, which provides a feedback loop for tracking progress and keeping everyone informed.
The downside of transparency is that it can reveal how bad things are, but without this awareness, improvement is not possible.