Data analysis is booming. Currently, it is also one of the few occupations that is impossible to fully automate. If you want a little bit of job security, our advice is to study up on your statistics and data science knowledge.
Success in anything comes from good decision making, and in business, that means comparing risk, reward, and all of the most relevant information you have at your fingertips.
At this point, you may be wondering why I’m making such an obvious statement, and the reason is that I think it is a good basis for a point I’ve been itching to make for a while now, and it ties into Industry 4.0.
In the modern world, decision making is being revolutionized by tech in the form of software and AI intended to crunch enormous amounts of data.
With deep learning neural networks, for example, we have a new tool that enables us to seemingly hedge our bets when making the really big decisions. However, for all of its technological advancements, the field of data analytics still requires a very human element, and this may always be so because it is essentially impossible to predict all of the variables that a business will encounter when making a decision.
So, as it stands now, the business world finds itself in dire need of an old skill with a new definition: Data Analysis. If you have this skill, then you should probably find a way to market it. If you do not have this skill, then acquire it. Oh, and this isn’t just coming from me; this advice comes from top execs such as Eric Schmidt, who is an executive chairman at Google’s parent company Alphabet, and Johnathan Rosenberg, an adviser for Alphabet CEO Larry Page.
As I mentioned earlier, there is no software that can completely replace human ingenuity and critical thinking skills, and yet, without all of this fancy new software, that ingenuity wouldn’t be able to make the most informed decisions possible.
Data analysis, then, is a two-sided coin these days. Let’s dig a bit deeper by starting with the human element that is critical thinking.
Human problem solving is geared toward translating information into logical conclusions, a good example of this being the scientific method.
Using this approach, we compare values, look for patterns, and draw statistical analyses, but that analysis isn’t necessarily focused on statistical data because people simply aren’t trained to process large amounts of data.
And that’s fine. After all, big data isn’t something that any one person can analyze with a simple spreadsheet; it requires the connection of independent databases for statistical analysis.
Additionally, the most useful conclusions are often not the most obvious, and they can even be counter-intuitive, such as when scientific advancement is derived from an accident.Critical thinking in data analysis is a skill competitive employees will need.Click To Tweet
The human element in data analysis has to do with making a judgment after gathering enough evidence to make a decision, which is something that machines still aren’t any good at. We may have AI that can come close to diagnosing people as well as a real doctor, for example, but saying ‘that’s good enough’ is like trying to convince an officer that you almost stopped at that red light. When it comes to life or death situations we know that we can’t trust AI to think for themselves, and to that end, we program the thinking for them.
Despite that fact, more and more tasks are being automated, so humans are finding themselves under a constant pressure to create more jobs that machines can’t do as well as a human. For example, the data that is used to train neural networks has to be highly curated and prepped for machines or specific algorithms to be able to process them, so almost all of our current data analysis is focused on converting data sets into actionable insights.
Or, to put it more simply, we can guide the significant data gathering powers of AI, and that’s good because it helps us make a final judgment call, which the AI can’t be trusted to do. And that brings us to the other side of the coin: the machine element.
Let’s take a quick trip into the land of contemporary business intelligence culture. If you look to your left, you’ll see a lot of people marketing themselves as data analysts, and if you look to the right, you’ll see the statistical variations that they should be (but aren’t) focusing on.
Oh sure, they analyze data to isolate patterns and make logical inferences like a good human should, but their methods are mostly superficial without a trusty machine to generate data for them. As I stated earlier, humans can catch things that are either extremely subtle or counter-intuitive, but their ability to do that is directly influenced by the information that is put in front of them.
Thus, if you are the kind of person who is adept at feeding the right data to an AI, there is job security out there for you, but make sure you study up on your statistics. AI know nothing of market demand because it would require AI to understand desire, but if a human knows how to monitor statistics to judge what people want they can theoretically make a model to predict market demand.
Remember folks, as advanced as computers have become, and despite the fact that one of the major technological endeavors of our day is the creation of intelligence (artificially, of course), computers are still inherently stupid. They’ll only do what we tell them to, so their considerable powers are at our fingertips and they are in no way truly autonomous.
Educators have a good term for the way that data analysts should interact with their software: scaffolding. Scaffolding is what you do when a student doesn’t know the answer to a question, and it takes the place of just feeding them the answer. A good teacher is able to determine what questions will support the student toward finding the correct answer because whoever does the thinking is the one who is going to achieve the learning.
With a machine, if you don’t subtly guide it to gather the information that you want, you won’t find the answer you are looking for. It is true that this skill isn’t something that comes naturally for every person, but it takes such a high level of logical inferencing that modern computers will never possess it.
So, in summation, humans need AI to gather massive amounts of data, and AI need humans to guide their search and make inferences based on the information generated.
Both are absolutely necessary to make useful analyses so that businesses can make profitable decisions, so the skill of data analysis is going to be one of the most competitive skills in the market for the near future.