As a data quality consultant, when I begin an engagement with a new client, I ask many questions. I seek an understanding of the current environment from both the business and technical perspectives. Some of the common topics I cover are what data quality solutions have been attempted previously, how successful were they and are they still in use today. To their credit, I find that many of my clients have successfully implemented data quality solutions that are still in use.
However, this revelation frequently leads to some form of the following dialogue:
OCDQ: “Am I here to help with the enhancements for the next iteration of the project?”
Client: “No, we don’t want to enhance our existing solution, we want you to build us a brand new one.”
OCDQ: “I thought you had successfully implemented a data quality solution. Is that not true?”
Client: “We believe the current solution is working as intended. It appears to handle many of our data quality issues.”
OCDQ: “How long have you been using the current solution?”
Client: “Five years.”
OCDQ: “You haven’t made any changes in five years? Haven’t there been requests for bug fixes and enhancements?”
Client: “Yes, of course. However, we didn’t want to make any modifications because we were afraid we would break it.”
OCDQ: “Who created the current solution? Didn’t they provide documentation, training and knowledge transfer?”
Client: “A previous consultant created it. He provided some documentation and training, but only on how to run it.”
![3 Ways to Get Your Data Into Shape [INFOGRAPHIC]](https://www.smartdatacollective.com/wp-content/uploads/2015/08/3-ways-get-data-into-shape-infographic-330x220.jpg)
A common data quality adage is:
“If you can’t measure it, then you can’t manage it.”
A far more important data quality adage is:
“If you don’t know how to maintain it, then you shouldn’t implement it.”
There are many important considerations when planning a data quality initiative. One of the most common mistakes is the unrealistic perspective that data quality problems can be permanently “fixed” by implementing a one-time “solution” that doesn’t require ongoing improvements. This flawed perspective leads many organizations to invest in powerful software and expert consultants, believing that:
“If they build it, data quality will come.”
However, data quality is not a field of dreams – and I know because I actually live in Iowa.
The reality is data quality initiatives can only be successful when they follow these very simple and time-tested instructions:
Measure, Improve, Repeat.
