Data Analyst Jobs Might be the New ‘It’ Career This Decade

For some time now, the demand for people with data skills has been growing. Research labs, universities, and tech companies have all relied on number crunchers who can wade through mountains of data and say something sensible about it in the final reckoning. 

Historically, the opportunities for people who understand data and statistics have been relatively niche. The economy supported a few thousand such people, but it wasn’t seen as a “breakout career” choice, in the same way as, say, property management or investment banking, 

Many commentators, however, believe that with changes in technology, the time of the data scientist has finally arrived. It’s not just big tech or academic institutions that require data tools; it’s everyone. 

The reason for this change has to do with the new uses of data. Data is no longer just about researching scientific mysteries. Just like branding, it has become a tool for competitive advantage. Companies that understand how to deploy data effectively can lower their costs, save on labor, reduce supply chain expenses, control overheads, and better understand what their customers want. They can make their marketing more effective, improve their public relations, and develop new products that far outstrip the appeal of their closest rivals. In short, data helps them win. 

Firms, however, won’t be able to capitalize on the data that they collect without the right people. This means that over the next ten years, we’re going to see the demand for “data analyst” and “data scientist” roles explode. Senior executives will soon get wind of the power of data – if they haven’t already – and go on a hiring spree, paying massive wages to people with suitable skills. 

For millennials stuck in dead-end jobs, this is a massive opportunity. If you become a data analyst, you could be looking forward to a long and illustrious career, doing something interesting. 

The Elephant In the Room

But first, let’s address the elephant in the room: all that maths you’ll have to do. 
Becoming a data scientist isn’t something that happens overnight. Usually, it requires a degree in mathematics and statistics and then postgraduate training in some of the stats packages that companies use for managing data. (There are loads of them, so I won’t bore you with the details here).

That all might seem like bad news, but if you have an aptitude for numbers, then it can be a lot of fun. Once you get the basics of statistics, it isn’t as hard as you imagine. You soon find yourself performing feats that you never imagined you could, like working out regression coefficients and putting a story to them.

The elephant in the room is the amount of training that some of these careers require. There’s no getting around that, unfortunately. But the rewards are also enormous. If you can fund your education with part-time work on the side, then you can acquire skills that will generate massive returns in the future, and a stable income to boot. For decades into the future, companies will need real people to design their data collection systems, analyze income information, and make recommendations for how to use it. They’ll need people with unique human insight for how to make the best use of the data at their disposal to gain a competitive advantage. Getting to the point where you can offer that level of value is a long one, but also incredibly lucrative. 

How Hard Is Understanding Data, Really? 

The first time that many of us encounter the world of science and data is in school. One day, the teacher comes along and shows us a table of statistics or a bar chart and then explains what it shows. Over time, the concepts build on each other and become more complex. Eventually, you’re dealing with ideas like “parameter spaces” and “vector autoregressions.”

But how hard is it to understand data to the point where you can add value to private companies? The answer is that it depends. Firms still have a lot of low-hanging fruit they can pick when it comes to using data. Easy-to-reach opportunities are still available. Thus, over the next five years or so, graduates will find that they do not need a tremendous amount of training to earn their keep. 

In the more distant future, the most significant rewards will be for the people who can do the most complex work and are adaptable. The tools that fit one situation might not suit another. 

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One of the advantages that you have as a modern data scientist is high-quality streams of information. The reason traditional data science is so difficult is that people have had to develop tools that allow them to compensate for incomplete or messy data. Companies have researchers have learned a lot over the decades and now collect data of higher quality, making the work of crunching the numbers more manageable. 

As with anything technical, though, it depends on your personality and intellect. If you’re the sort of person who loves investigating problems and figuring out a solution, data science will suit you down to a tee. If you’re more of a creative or arty-type person, it might not be for you.

It’s worth pointing out that your experience in school or college is no guide to your future success. If you really do want to carve out a career for yourself in the data sector as an adult, there’s no reason why experiences of ten or twenty years ago should interfere with your judgment. Back then, you understood less about the world, and your career was a distant afterthought. Today, it’s the daily reality of your life. You have a massive incentive now to learn that wasn’t there in the past. The prospect of getting a much higher salary combines with the fact that you don’t like your current work is a powerful motivating combination. 

In conclusion, data science is going to take over the world over the next twenty years. Hopping on that bandwagon could see your career success multiply. No doubt, that’s something you want. 

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