The Professional Problem, 3 Things They Need, and 1 They Don't Have
Professional people like researchers, engineers, accountants, and managers often have a fantastic education and extensive work experience. They got into the real world and realized that, very often, the data handed to them was muddy, needed to be cleaned, didn't match up with other related data, or had one of the other thousands of problems that can happen. On projects, they ran into accuracy problems that caused them to make faulty decisions.
This is what I call the Professional Problem, and it applies to anyone who handles a large amount of data on a regular basis. You'll know if you have it.
In a world that has gone massively online over the past decades, we are awash in data, but our education on how to handle and manage data absolutely has not kept up. Not even close. In over 20 years of designing and integrating small and massive data systems, I see that one issue keeps coming up: people can't handle data to save themselves, and, if they do get (kind of) good at it, they get good at using the wrong tools that will limit them in the future.
The good news is: the amount of training needed to overcome the problem isn't massive, and the people I mentored in the past found their skills to be invaluable.
I compare this situation to a stool with 3 legs. If one of the legs is missing, the stool falls.
People that have either been university educated, or weren't, but became really good at their business, usually have three things in common:
1. They are good at their subject matter (say, an engineer or researcher)
2. They are good at crunching the numbers when they have data (Excel pro, researchers with stats software)
3. They don't know how to obtain, manage, or massage data well. If even the simplest data problem is introduced, #1 and #2 fall down.
They continue to have problems. Why? It is because they are missing #3.
Because they are missing #3, they must spend extra time to make sure the data inputs are correct (wasting time), rely on others of varying skill sets (possible inaccuracies), or, worst of all, they don't catch an issue and present results that don't represent reality (embarrassing themselves when someone points it out).
In this new world made of data, it is no longer enough to just be good in our area of expertise. We must get good at data. In my previous post, I talked about the high value that data techniques can bring to professionals who use them. Stay tuned for more techniques on this blog, and our YouTube channel. Also check out our Power Professional course, delivered in-person.