How many times have you been in a meeting where someone claimed victory on a problem while others thought there was still work to be done? Or maybe someone on the team suggested the group may be entering “analysis paralysis” and it was time to move on. I know I have been in both kinds of meetings.
As I mentioned in my post Before You Analyze Data, Understand the Context of the Problem post, I reconnected with a old colleague a while back and we had a good conversation about data and decision making. One topic that we discussed and something that came up after our discussion when I was listening to the book “Outliers” by Malcolm Gladwell, was the concept of determining when a solution is “good enough.”
Specifically, Gladwell was arguing that at some point, inherent talent will only take you so far. After that, what makes someone a true outlier is the amount of time invested on improving their talent. So the argument was that at some distance on the talent spectrum there is a spot of “good enough” where anything beyond that does not have any more influence on the person’s ability to achieve “outlier status”.
This concept summarized part of our conversation perfectly: When you are looking to make decisions on data, when is the data telling you that you are “good enough” and that any more will not increase your chances of being an “outlier”? In our example over lunch, we were talking about a physician’s ranking on patient satisfaction. If a physician has a rating of 4.125 and they are on the 25th percentile and 4.25 is the 99th percentile, is it worth being in the 99th percentile? Unfortunately, many individuals and business respond “yes,” it is worth it. There are many reasons for this thought, sometimes reporting agencies rate you and give you awards for being in the top percentile in ‘x’ number of metrics. Alternatively, you may want to advertise this 99th percentile achievement as a way to show you are better than your competition. Or worse, there may be an incorrect assumption that being in the higher percentile is actually better for the customer in all situations, including in this situation: patient satisfaction. This assumption that higher is always better is one that can lead an individual or a business down the wrong path chasing “perfection” when in reality, “good enough” is all they need.
Related: I’ll Only Stop When It’s Perfect
Now, this is not limited to healthcare and patient satisfaction. With the availability of data increasing by the day, the opportunity for people to use data to solve the wrong problems, or perceived problems continues to increase.
The question we discussed over lunch is “How can we get decision makers to understand what is really important”? In the example above, random variation could bring you in and out of the 99th percentile because the values used to calculate the percentages are so close. This example really tells you that everyone is good at this measurement and that is why the percentages are so close. So if everyone is so good at it, then it means customers really don’t find that measured factor as a determining one that sets people or companies apart. This means time and energy should be spent on improving other metrics that would differentiate you or your company!
While there is probably not a silver bullet to this problem, they key point I concluded from this book at my conversation is sometimes you need to take a step back and ask yourself, “Is this good enough, or does the business still need to improve”? This timeout and introspective review is critical to ensuring that time and money is not wasted in analysis paralysis activities or trying to improve something just to hit specific numbers, especially if there is no evidence that these improvements in numbers will actually improve business performance. So if you end up in this situation and you find the numbers you are targeting will not actually drive better business outcomes, you have hit “good enough” and it is time to move on to a new problem to solve.