DQ-Tip: “Data quality tools do not solve data quality problems...”
Jim Harris in Books, Data Quality, Events tagged DQ-Tip, DataFlux IDEAS, David Loshin Tuesday, October 5, 2010 at 5:00AM Data Quality (DQ) Tips is an OCDQ regular segment. Each DQ-Tip is a clear and concise data quality pearl of wisdom.
“Data quality tools do not solve data quality problems—People solve data quality problems.”
This DQ-Tip came from the DataFlux IDEAS 2010 Assessing Data Quality Maturity workshop conducted by David Loshin, whose new book The Practitioner's Guide to Data Quality Improvement will be released next month.
Just like all technology, data quality tools are enablers. Data quality tools provide people with the capability for solving data quality problems, for which there are no fast and easy solutions. Although incredible advancements in technology continue, there are no Magic Beans for data quality.
And there never will be.
An organization’s data quality initiative can only be successful when people take on the challenge united by collaboration, guided by an effective methodology, and of course, enabled by powerful technology.
By far the most important variable in implementing successful and sustainable data quality improvements is acknowledging David’s sage advice: people—not tools—solve data quality problems.
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Reader Comments (4)
Jim,
I couldn't agree more.
Sadly, some organizations appear to believe that, because they have spent money on data quality tools and have data quality metrics in place, that all will be sorted out.
Data quality tools can help spot common problems, correct these problems and help to identify the root causes, but cannot fix the root causes of data problems. In isolation these tools will not deliver sustainable improvements.
Sustainable improvements to data quality require people to behave differently and comply with the requirements of processes, systems and regulations.
Julian
Obviously true. Who said the opposite? :-)
As I understand it, the sentence was said at a data quality tool user conference.
I hope you people at the conference were able to take the subject to the next level.
As Julian also says, data quality tools are actually able to solve some specific issues. First and foremost, we use data quality tools to tidy up when the harm is done because it is more cost efficient to use a tool opposite to human labor. The current trend in data quality tool capabilities is, as I see it, to be deployed further upstream and closer to the root cause and thereby assisting people in doing a better job.
Having been a data quality tool maker myself (as you Jim) I know tool makers need valuable input from users and experts on how to make better tools, as I have no doubts that technology will continue to play a greater part of data and information quality improvement, just like every other technology enabled discipline.
So true! I battle the "silver bullet tool" notion on a daily basis.
Too often data quality vendors promote their software as an automated end to the data quality problem. Fact is, data quality issues arise from people driven errors and, unless those are eliminated, you usually have an ongoing issue.
Once people rally behind the cause of solving the issue and implement continuous process change, you can make a case for tool based solution, but it takes awareness and action by people to resolve.
Funny how we talk about the same things over and over, huh?
Thanks for your comments, Julian, Henrik, and William. Your feedback is always greatly appreciated.
@Julian — Yes, I agree with you and Beth Breidenbach, who says that “human behavior is both the root cause and the solution. Technology doesn’t cause or solve the data quality challenge. Rather, it’s a tool that exacerbates or aids human behavior in either direction.”
@Henrik — Yes, the sentence was said at a data quality tool user conference, albeit on a vendor-neutral training day. David's workshop was discussing data quality maturity and this particular remark was made during an open discussion with the class regarding sustainable data quality improvement. And this particular data quality tool maker (i.e., DataFlux) continues to do an excellent job both soliciting and incorporating user feedback in order to improve their tool—as I am sure Omikron Data Quality does as well :-)
@William — Well said, William. And yes, it is funny how we talk about the same things over and over, and over again :-)