The modern digital world has a strange relationship with data. How so? Because it isn’t really all that logical. That might seem counter-intuitive. After all, what could be more logical than data and the analyzing of it? The big problem is the assumptions that go into analyzing that data, particularly those concerning the relationship between cause and effect.
Typically, we fall into one of two traps. We either regard the observed data as causes and we then try to guess the effects, or we regard the data as an effect and try to figure out how to cause more of that effect or to avoid it if it’s undesired. Both traps are typically sprung due to false assumptions. Let’s look at a simple ad on your favorite streaming service. Be honest, you skip them every chance you get. That means when you do actually watch one, it gets the attention of the algorithms. If it is seen more as a cause, the assumption will be that because you watched the whole ad and you will actually go out and buy the product. If seen as an effect, it will likely be assumed that you were interested in the product and so watched the ad for that reason. The people behind it will then basically figure out how to get you to watch more of those same ads. The truth though may be something far different. The person who let the ad play through was busy cooking dinner and answering questions from two small and inquisitive children and so was too distracted to skip the commercial. In that scenario (the most likely one it would seem) the ad didn’t lead to a sale and the person really wasn’t interested in the product to begin with. In fact, since algorithms dictate that many similar ads will now flood the unfortunate person’s phone, likely leading to a total dislike of the company and all its products.
Modern medicine actually provides many examples of this phenomenon. Too often, symptoms are all that is treated and no attempt is made to try to get at the actual cause of the illness. Think about how many times your doctor has just given you a pill to treat something when all that was really needed was some changes to your diet. In this case, there isn’t even any real interest in the cause of the illness. This is obviously something that could be a problem down the road. At the least, it will lead to more pills as our patient’s health continues to deteriorate.
So, how does one use data logically? How can we get the best use of our data and not make a million assumptions in the process? First, by looking for the real causes of the effects that we observe on a regular basis. We actually try to learn why you watched the ad, or bought that new computer monitor, or sought a doctor that wasn’t afraid to tell you to stop eating doughnuts. How do we do that? By going directly to you, to the generator of all this valuable data. If we need to know the cause behind a certain action, we’ll just ask. In that way, we and our business clients get real, trust worthy data that illuminates cause and effect like never before.
The TARTLE model also promotes transparency, an important quality when aggregating and analyzing data. If you don’t trust a company or other organization then you are likely to not give them any data to work with if you can help it or even to provide false data. With us though, your data is both protected from hackers and firmly in your control. It only gets shared if you want it to be shared and only shared with whom you want to share it. With you in control, it seems easy to assume that your data will be accurate and up to date, something all organizations crave.
TARTLE’s system builds and capitalizes on that trust, taking the time and effort to get to the heart of things, to root causes and from there, see how the affects develop. In doing so, we can finally use data logically in a way that can be used not just to increase the bottom line but to be a benefit to all.
What’s your data worth?