The Scarlet DQ
Jim HarrisThe Scarlet DQ is the superhero name of Jill Wanless (aka sheezaredhead).
Just kidding—I would never reveal a superhero’s secret identity.
Although I was never a big fan of the book, the title of this blog post is inspired by The Scarlet Letter by Nathaniel Hawthorne, where the novel’s protagonist Hester Prynne is forced to wear The Scarlet Letter A as a badge of shame for committing the act of adultery, which lead to the birth of her daughter Pearl.
The book came to mind while I was reading the commendable comments received last week on The Poor Data Quality Jar, where a recurring theme was the valid criticism of the “public humiliation” aspect of having employees put money into the jar when they contribute to the organization's poor data quality.
Using such an approach to help organizations illustrate the costs of poor data quality is equivalent to making the offenders wear The Scarlet DQ as a badge of shame, which will only make it far more likely that data quality issues will not be reported at all.
But even without my “swear jar” inspired idea, I think that the fear of public humiliation is what prevents poor data quality from being acknowledged by many organizations, which often leads to a major data quality related crisis that “no one saw coming.”
For example, if you are in need of some quiet time alone for taking a good power nap in a conference room, then try scheduling a meeting to discuss known data quality issues and their root causes. If your organization is like most, then you could probably book one of those really nice conference rooms with the big comfy reclining chairs—because nobody will attend your meeting.
Data quality can be somewhat of a taboo topic. Many organization assume that their data quality must be “good enough” otherwise “how could we possibly still be in business?” Nobody likes to talk about data quality for one simple reason:
If data quality issues exist (and they do), then no one wants to be blamed for causing or failing to fix them.
It’s as if everyone is afraid that they will be forced to wear The Scarlet DQ.
This is one of the many human dynamics that can render even the best technology and proven methodology completely useless.
What Say You?
Please post a comment and share your recommendations about how to foster an environment in which poor data quality can be reported freely without fear of blame or reprisal. All viewpoints are welcome. Nathaniel Hawthorne references are not required.
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Reader Comments (10)
When I think about root causes of many of the data quality issues I have witnessed, the original data entry was actually made in good faith by people trying to make data fit for the immediate purpose of use. Honest, loyal, and hardworking employees striving to get the work done.
Who are the bad guys then? Either it is no one or everyone or probably both. When I have witnessed data quality problems solved it is most often done by a superhero taking the lead in finding solutions. That superhero has been different kind of people. Sometimes it is a CEO, sometimes a CFO, sometimes a CRM-manager, sometimes anyone else.
Thought provoking post Jim, as usual.
I am interested to read the comments that will come on this, it is a topic that needs lengthy discussion and debate. You should put up a "Good ideas required" sign on it!
The "blame and shame" culture prevalent in a lot of businesses definitely drives people to shy away from owning up to data errors, or even worse, they brush them under their carpet. I must admit to having been part of this culture on a number of occasions.
If you take Jack Welch's 'meritocratic' approach, you can integrate data quality into every employees KPI's, but you need to put a positive spin on it. Perhaps, rather than having a negative strike to performance, it could be a neutral or positive strike.
For example, the KPI could state that keeping to good quality data would not have a detrimental impact on performance remuneration, but over delivery on data quality will have a significantly positive impact on data quality, thus incentivising resources to overachieve on data quality. The incentive will have to be large enough for them to make an effort though ...
Jim, I just love the use of the word 'superhero' in the same sentence as my name :)
I hope the folks on my old team don't read this though...knowing them they'll start referring to me as the SDQ before the end of the day!
This idea is something that I was pondering just recently because of my new coffee maker that goes 'beep' 5 times when the coffee is finished brewing. I hate the beep. It's really annoying. Besides the fact that it will wake others up if you get up too early (4 am is not that early ok!), it really bugs me that the list of 'features' described on the product packaging made no mention of the beep. The washer beeps, the dryer beeps, the microwave beeps and the car beeps if you don't buckle up. It drives me crazy! Then I thought wow! What if every time someone made a poor contribution to Data Quality a loud beep went off. Some teams would beep more than others. If it was a really bad error, like creating a duplicate record, then the beep could be much louder: say like a siren. My thinking is that in about 1 hour there would be no more data quality errors.
The risk of this approach though is that there might be no more data, as people stop entering the data into the system because they are afraid of the beep.
Like Charles says, positive reinforcement is one strategy that could work. A KPI that measures what positive steps the employee took to improve data quality would be more readily acceptable than a negative one. And, it will also allow the organization to benefit from the hundreds or thousands of great new ideas that are generated from the employees as a result.
Thanks once again for a thought provoking post!
Jill
The mantra I had when I was in the phone company and tackling Info Quality issues to beat the band was that, in the absence of a framework that tells them what to do, and why it is important to be done to a certain standard, well meaning people will apply their creativity to solve the immediate problem they have, resulting in a creativity cascade, which looks a lot like a river of unmentionables.
This applied to the front-line staff in call centres (who were always blamed by IT for the quality of the data), as well as IT teams (who were always blamed by the Business for the quality of the data).
While it is tempting to think that if we just find the right KPI for people they will suddenly produce highly polished information, the reality is that the whole area of incentivisation and reward is fraught with danger due to the oft contrary nature of human psychology. Deming recognised this and wrote about it (albeit not recognising the psychology aspects until later in his career) and I've written about it in the IAIDQ's newsletter in recent issues (See: IAIDQ Publications).
We're hoping to run a webinar on this particular aspect of the change challenge in the coming months.
Check out IAIDQ.org or our twitter feed for details!
I work with engineering data and I find that the users of the data are not the creators of data, so by the time that data quality is questioned the engineering project has been completed, the engineering teams have been disbanded and moved on to other projects for other facilities. I am sure that if the engineers had to put the spare part components on purchasing contracts for plant maintenance, the engineers would start to understand some of the data quality issues such as incomplete part numbers or descriptions, missing information, etc.
I find that snacks at my data quality meetings help attendance.
Thanks everyone for your excellent comments. I continue to be very grateful for your feedback.
@Henrik Great point. It is often all too easy to villainize data entry for causing data quality issues, as if they were somehow deliberately attempting to undermine the quality of the organization's information. Like you said, they actually do make every effort to make data fit the immediate purpose of use. Many times, it is the unknown future uses of the originally entered data that is the context for what in hindsight appear to be obvious data quality issues.
@Charles "Good ideas required" would make a great name for a blog! I too must admit to having occasionally been part of the "blame and shame" culture. I do like the positive KPI incentive idea, but I worry about how the current economy has affected all incentive programs, where fear of losing a job could be contributing to the "brush it under the carpet" philosophy.
@Jill I like the beep/siren idea, but I also have to agree with your conclusion that the Pavlovian conditioning would likely result in no more data due to fear of the beep.
@Daragh A creativity cascade by well meaning people resulting in a river of unmentionables is a great phrase! Within that context, apathy might actually be a more preferable response :-) Thanks for the additional feedback on the challenges of the KPI approach - I am looking forward to seeing an IAIDQ webinar on the topic.
@Jackie Great point about the snacks improving attendance at data quality meetings! And excellent example of how bridging the disconnect between data creators and data users is an essential perspective necessary to illustrating the importance of data quality.
Jim,
Great post.
For example, if you are in need of some quiet time alone for taking a good power nap in a conference room, then try scheduling a meeting to discuss known data quality issues and their root causes.
Very true, but boring and unimportant (as you know) are hardly the same. Perhaps if organizations and senior folks held their people accountable for data quality, this wouldn't be the case. If people were comped on data quality, then wouldn't the interest level perk up?
Is the question of "who is to blame" really that important?
For me, it is more important to ask "what needs to be done to improve the situation".
I don't think that assigning blame helps much in improving the situation. It is very rare that people cooperate to "cover up their mistakes". I found it more helpful to point out why the current situation is "wrong" and then brainstorm with people on what can be done about it - which additional conventions are required, what can be checked automatically, if new functionality is needed, etc.
Of course, to be able to do that, you've got to have the right people on board that trust each other - and the blame game doesn't help at all. Maybe you need a "blame doll" that everyone can beat in order to vent their frustrations and then move on to more constructive behavior?
The Scarlet DQ is conjuring images of a strawberry Dairy Queen Sunday on a warm July afternoon…
Maybe the meeting shouldn’t be held in a stuffy office in the first place – take it to the ice cream shop down the street – more people would attend that way.
I’m not actually making light of this, I think that carrots are better than beating people with sticks, however in a corporate culture of the ‘can do’ attitude that is very willing to cut corners to get the job done on budget, the solution that has the best chance of working is *someone* in power that has the understanding, foresight and ability to ensure data quality is looked after from the get-go.
@Phil Compensation based incentives for data quality would definitely perk up the interest level but I not sure how viable it would be, especially when data ownership can be tricky enough without it. And as I noted above with regard to Charles' positive KPI incentive, I worry about how the current economy has affected all incentive programs.
@Thorsten I love the idea of a "blame doll" - you might want to patent that! :-)
@Paul A DQ meeting at the DQ does indeed sound good. Carrots (or better yet, soft serve ice cream) is much better than sticks. James Standen wrote an excellent blog post about that very idea: Data quality behavioral modification?