Why isn’t our data quality worse?

In psychology, the term negativity bias is used to explain how bad evokes a stronger reaction than good in the human mind.  Don’t believe that theory?  Compare receiving an insult with receiving a compliment—which one do you remember more often?

Now, this doesn’t mean the dark side of the Force is stronger, it simply means that we all have a natural tendency to focus more on the negative aspects, rather than on the positive aspects, of most situations, including data quality.

In the aftermath of poor data quality negatively impacting decision-critical enterprise information, the natural tendency is for a data quality initiative to begin by focusing on the now painfully obvious need for improvement, essentially asking the question:

Why isn’t our data quality better?

Although this type of question is a common reaction to failure, it is also indicative of the problem-seeking mindset caused by our negativity bias.  However, Chip and Dan Heath, authors of the great book Switch, explain that even in failure, there are flashes of success, and following these “bright spots” can illuminate a road map for action, encouraging a solution-seeking mindset.

“To pursue bright spots is to ask the question: What’s working, and how can we do more of it?

Sounds simple, doesn’t it?  Yet, in the real-world, this obvious question is almost never asked.

Instead, the question we ask is more problem focused: What’s broken, and how do we fix it?”

Why isn’t our data quality worse?

For example, let’s pretend that a data quality assessment is performed on a data source used to make critical business decisions.  With the help of business analysts and subject matter experts, it’s verified that this critical source has an 80% data accuracy rate.

The common approach is to ask the following questions (using a problem-seeking mindset):

• Why isn’t our data quality better?
• What is the root cause of the 20% inaccurate data?
• What process (business or technical, or both) is broken, and how do we fix it?
• What people are responsible, and how do we correct their bad behavior?

But why don’t we ask the following questions (using a solution-seeking mindset):

• Why isn’t our data quality worse?
• What is the root cause of the 80% accurate data?
• What process (business or technical, or both) is working, and how do we re-use it?
• What people are responsible, and how do we encourage their good behavior?

I am not suggesting that we abandon the first set of questions, especially since there are times when a problem-seeking mindset might be a better approach (after all, it does also incorporate a solution-seeking mindset—albeit after a problem is identified).

I am simply wondering why we often never even consider asking the second set of questions?

Most data quality initiatives focus on developing new solutions—and not re-using existing solutions.

Most data quality initiatives focus on creating new best practices—and not leveraging existing best practices.

Perhaps you can be the chosen one who will bring balance to the data quality initiative by asking both questions:

Why isn’t our data quality better?  Why isn’t our data quality worse?

Comments

[Dylan Jones]

Very good points Jim, we so often focus on the negatives, the fact is that some people and some processes are not flawed when it comes to DQ, the key as you say is to learn from them. It's also a far more positive method of fostering change.

[Jim Harris]

Thanks for your great comment, Dylan.

Many change efforts are resisted because they rely almost exclusively on negative methods.

Even data governance maturity models (which continue to get more attention these days) are really inherently negative, i.e., “Your organization is immature.”

Even when it is true, and it often is, who likes to be told that? Should it really be surprising that the typical response is denial?

Cheers,

Jim

[James Standen]

I immediately thought about the many "lessons learned" sessions I've been in, and what percentage of lessons were success related and how much time we spent focusing on issues.

The other advantage of learning from success is that it gives a chance to highlight and praise the hard and successful work of the team - motivating them for the next round.

Because in data quality, we know there is always another round...

Great post Jim.

[Gordon Hamilton (DQStudent)]

Great post Jim.

They say that sport coaches never teach the negative, or to double the double negative, they never say "don't do that".

I read somewhere, maybe Daniel Siegel's stuff, that when the human brain processes the statement "don't do that" it drops the "don't", which leaves it thinking "do that".

Data quality is a complex and multi-splendiforous area with many variables intermingled, but our task as DQ Evangelists would be more pleasant if, as you suggest, we were helping people rise to the level of the positive expectations, rather than our being codependent in their sinking to the level of the negative expectation.

Cheers, Gordon

[Jim Harris]

@James — Thanks for the learned comment :-)

I am a big advocate of learning from our mistakes, but most people (myself included) don't even consider learning from our successes. I think there is a general tendency to assume that since success is usually expected, it is not worth examining, only celebrating — which is also why failure typically only leads to complaining.

As you said, taking the time to highlight and praise the hard and successful work of the team can make them appreciate success, as well as motivate them to take on the next challenge.

@Gordon — Excellent point about DQ Evangelists needing to help people rise up to the level of positive expectations.

[Jill Wanless]

Wow! A new favorite post I think!

I LOVE the idea of this. Why DON'T we ever think about it?

Is it because we perceive it to be less effort to look at 20% than 80%? Or is it because we generally already know what is causing the 20% error rate? That would make an interesting survey...

I am going to re-tweet this AND e-mail it to a whole ton of people.

Thanks Jim!!

[Jim Harris]

Thanks for your comment, Jill — and for feeding the OCDQ Blog Positive Propaganda Machine :-)

Even though I was familiar with negativity bias from reading other books (I read way too many books), I will admit to really being taken aback when I read about our natural tendency to only use a problem-seeking mindset in Switch.

I immediately thought of 80% accuracy. Isn't it a bit strange that it is stated negatively?

It's as if we are perceiving it as 80% inaccuracy.

Best Regards,

Jim

[Corinna Martinez]

Jim,

If problem solving is the emphasis and most of that is geared toward the negative spin, then we need to teach people to do more than just solve 1/2 of a problem and THEN you will get more people asking the second set of questions.

This is where philosophy and being able to argue both sides of a matter come in handy. Only once all sides of an issue are addressed we can say we have indeed solved the matter or at least better understand it. Even root analysis requires looking at all indications and maybe some outliers for what is causing an anomaly.

Skewing the matter toward the issues, problems, "What/Who needs to be fixed" instead of what is truly happening and why is indicative of what is rewarded both in our educational systems and workforce. Who gets the job? The balanced observer of the circle of life and the role we all play or the "problem solver"?

DQ and testing experts pride themselves on being able to find the errors and report them back to the originators. They don't get very good response either and then spend a career complaining about the non-responsiveness of programmers, management, users and all the rest of the error creators/purveyors.

So can you change an industry to go from the gloom and doom of a black hat mentality to one of lightness and creativity of a white hat mentality?

When we do, we find the new questions ever so much more interesting than the prior solutions.

[Jim Harris]

Thanks for your great comment, Corinna,

Especially for bringing the Six Thinking Hats of Dr. Edward de Bono into this discussion.

To save everyone the trouble of clicking back and forth between here and the provided Wikipedia link (which is a great read), the Six Thinking Hats are the following distinct states identified and assigned a color:

White (Questions) — considering purely what information is available, what are the facts?

Red (Emotions) — instinctive gut reaction or statements of emotional feeling (but not any justification)

Black (Bad points judgment) — logic applied to identifying flaws or barriers, seeking mismatch

Yellow (Good points judgment) — logic applied to identifying benefits, seeking harmony

Green (Creativity) — statements of provocation and investigation, seeing where a thought goes

Blue (Thinking) — thinking about thinking

I agree that our negativity bias has us wearing the black hat most of the time, and I definitely agree that if we can wear the white (and yellow and green) hat more often, we will find the new questions not only much more interesting than the prior solutions, but we will also find the right balance of new and existing solutions that help us move forward.

Best Regards,

Jim

[Jim Harris]

From the SmartData Collective, Michele Goetz commented:

“Most data quality initiatives focus on developing new solutions—and not re-using existing solutions. Most data quality initiatives focus on creating new best practices—and not leveraging existing best practices.

I'll make the argument that many companies do look at re-using existing solutions, but to leverage existing best practices may not be enough or they don't know the full set of best practices. At the heart of managing data quality I think you hit on something important, the practice and discipline.

Data quality has been a back of the house, in the closet endeavor. Build a quality process on a quality server, put in a closet, run it continually and let it go. 3-5 years later, the question is, "Why is our data so bad?" Simple, you didn't change your services to match your changing business. Or, you don't understand how your solution works, and are scared to modify it for fear of breaking what you think is working. You look at it, scrunch your eyebrows, and decide, easier to start again.

Best practices? Well, mostly that is in the hands of the business. Purchase information to augment data. Hand clean and reconcile. Manually analyze and monitor. All are best practices - you manage your data. But, technology could make life so much easier! Technology is sometimes the missing link. The other aspect is that the focus is on the business process best practice, but the data is forgotten - a by-product of the process. So, there is best practice, but the DQ controls are minimal.

I think to your point on, why isn't our data quality worse? There are those who are asking, the ones that are holding the purse strings and see that the business is moving along and growing or is healthy. The data is good enough in their eyes. It's those that actually rely on the data that know better. So, it is back to building the ROI case.

Great perspective!”

And I responded:

Thanks for your comment, Michele.

Your feedback is greatly appreciated.

Best Regards,

Jim