Are You Afraid Of Your Data Quality Solution?

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…

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.”

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.

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