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?
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?
Loading...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?
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