The last few years saw a boom in people who changed their career from other areas of work to exactly data analytics. But what exactly happened, and why?

It may be difficult to look at all people under one hood, as they probably have many different reasons, but I still see a number of trends in the society that collided and made more people, and especially some other people than previously, decide to pursue a career in data analytics.

Often, society changes as a result of several causes working together. There is not necessarily one cause to one effect, it is more of a mix. And as soon as any development has reached a point where it can be described, it continues from there, now becoming something else. This kind of dynamics also counts for the data analytics area.

First, there was the appearance of AI. While this in itself isn’t the same as data analytics, it reached out to a large group of people who were not traditionally interested in IT above a normal user’s level. But when needing to learn how to create prompts for an LLM, there was suddenly a new kind of connection with the computer – prone to guide some people into an even closer relation with computing.

Second, the BI business that had been through a period of decline, after its initial heydays in the beginning of the 2000s, started to rise again. Especially such vendors as Qlik and Tableau brought BI to the table again, and when also Microsoft put more efforts into the area, with their typical big scale marketing activities, BI suddenly became known to a lot of people. It began looking like if it was a needed skill, similar to using text processing and spreadsheets.

These vendors saw a rise in sales and the bigger interest on top of a hype of the terminology “data-driven”. All of a sudden, it had become fashionable for business leaders to be data-driven, even though it wasn’t always understood what it meant. But soon, it had become clear to most that it meant buying a BI system of some kind.

And third, which is a somewhat peculiar development in society: autism and “being on a spectrum” suddenly became positive. We saw a wave of people who were diagnosed with autism, or who self-diagnosed as such, and there were companies who began talking about how they in particular were looking for autists, when hiring.

Now, autism is among other things about being able to concentrate on details, and being interested in diving into a thought process. The opposite of being superficial, you could say. And that is a great match for data analytics – and all other kinds of analytics, I would say.

These three developments made it, all of a sudden, both possible and needed for many analytical people to start paying attention to this new kind of work.

It was further fuelled by the developments in data science, where big data and analytical tools had been introduced and become commonly used, but by people with a data science education or similar, something with a technical level and some systems perspectives that wouldn’t be needed for looking at data, and make dashboards and reports from them – if the work could be split between different groups of people, then it would be easier to fill all the new positions, and so it happened.

Data analysts appeared, some as a follow-up to the previous statistician role, and others “out of the blue”, bringing in their analytical skills, mainly. The technically skilled would then work with creating databases and the extracts and transformations needed, while the analysts would look at the prepared data with a front-end BI tool.

And Where Did This Lead To?

As of today, it looks like the business world has stepped back a bit – it doesn’t need nearly as many data analysts as it did during some time, and, hence, the movement toward such a job title is not as significant anymore.

Many of those who did get in during the good years may still be there, now having some specific skills with the tools, procedures, and needs of the company where they work, but there is not as much talk about the phenomenon anymore: that “anyone” can become data analysts, especially if they are an inward, autistic type of person.

Actually, I think that many of those who did move into such a job, weren’t any more autistic than most other people. But they felt comfort in using this label for themselves, to sort of excuse why they were less popular in social life, etc., even though I would claim that “social life” also had it’s part of the blame. There is not necessarily anything special with a person who isn’t accepted in a social context – it can just as well be that the others aren’t very open to such differences that are common between people.

In other words, I will not deny that autistic people or people on an autism-spectrum fit well into such a role, but it is likely that many other people also fit in. And the way the recruitment processes work in many companies has probably led to quite many on the specter being rejected when applying for a data analyst job, so many of these jobs are probably occupied by non-autists.

Very many people have an analytical sense. They both like and are good at studying problems and finding their solutions. The many detective stories in books and on TV should indicate this, but also the many jobs as Support Engineer, etc., where one person helps another find the nature of a problem and then helps them move forward toward a solution.

In fact, that’s how most jobs work, now that I am thinking about it. A plumber looking at your tubing at home will as the very first thing ask you some questions about the nature of the problem, then do some research of their own, and finally move on to first describe the problem, calculate a price, and then fix it.

The difference that makes some people want to work with data analysis, is a bit more subtle: it’s about the scope of analysis. How long it takes, and how wide the thoughts need to get away from the core topic – how much of the world needs to be included in the thinking. And, of course, something about feeling comfortable when working with numbers.

It is not strange, thinking a bit further, that data analytics or BI, whatever it is called locally, is being performed to some degree by many different people in the organization. It isn’t an isolated task like database server installation, or the adjustment of a machine in the production – it is an area of skills that overlaps partly with other skills possessed by people in many other jobs.

And that could speak for being a lot more open to who you would hire for data analytics work. If you then need several people to work in that area, this allows for each of them to specialize a bit, meaning that they don’t even need all the same practical skills – which should open up the candidate field even further.