Great question. So we touched upon it in an earlier conversation. I’ll break it down to 3 common problems.
One is assuming the data is clean.
I mentioned that all data is dirty. You have to figure out how to ask that question with the data being dirty.
Two is assuming the question itself is correct.
Which is every question is trying to get at an action. And often, the question itself has to be rephrased multiple times until you can find the right question to guide an action.
And the third problem is time.
Because doing an analyses right is super-time consuming, due to modeling issues, structuring issues, query issues, and math issues, we get excited and we are not able to dive as deep. So analyses, people do phDs on these analyses, and they spend months trying to isolate and understand. In an environment when you have days or weeks to come up with an action, you end up making mistakes, unless you have some support or tooling to help accelerate that process.
And I’ll add one more: the last thing is the illusion of self-service. An untrained person is not able to look at a dashboard and figure out what they should do. I had to go through robotics training, so I could make decisions, to learn how to understand data. And often anyone thinks they can make a decision by looking at a dashboard. I don’t believe there’s ever been in a history of decisions one that someone found on their own through looking at a dashboard. You have lucky situations, but that dashboard doesn’t necessarily say whether it’s good or bad. So let experts do the analyses, and you consume them. Everyone wants to do self-serve analytics, but they may not be qualified to do that.