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Data analysts (and their close cousins, data scientists) are in high demand, and it’s only going to grow from here. By 2020, it’s estimated that there will be 700,000 new recruitments of data scientists, analysts, developers, and engineers.

So how is it that this position has become so valuable, and how could demand for this profession continue to grow from here?

The Increasing Demand for Data

There are a few big reasons why so many companies are pushing for more data analysts:

  • Objective findings. Data is becoming more in-demand because of how objective it is. Conducting interviews with customers and brainstorming in meetings can help you subjectively understand your audience, but objective data points spell out inarguable conclusions—so long as you have the expertise to interpret those data.
  • Data availability. Each day, online users generate more than 2.5 quintillion bytes of data, and that number is increasing steadily. Couple that with the fact that we have more sophisticated data extraction tools than ever before, and it’s easy to see how we’ve landed in an era with unprecedented data availability. Companies need analysts and scientists to take advantage of that surplus.
  • Defensive competitiveness. If you don’t harness the potential of the data that’s publicly available, one of your competitors will. This fact alone is making companies more eager to headhunt talented data specialists, and gain the competitive advantage (or at least prevent being at a disadvantage).

How Accessibility and Convenience Could Reshape Demand

However, this growth spurt may not continue indefinitely. As data analytics platforms become more sophisticated and more approachable, we could see this demand level off.

Consider:

  • Time reduction. There’s a reason 70 percent of tech workers admit to sleeping on the job. Technology is all about making things simpler and easier, but the easier things get, the less time it takes to maintain them. Eventually, our platforms will be so sophisticated that data analysts will spend fewer hours on each project, ultimately reducing the sheer demand for expert work.
  • Visuals and customization. Data visualization is a feature that’s increasingly being represented in data analytics platforms. Visuals make complex data sets easier to interpret and process, no matter how much experience you have. Accordingly, they’re increasingly being put in the hands of non-experts, who can then handle at least some tasks that were previously only available to experts.
  • Simplicity and learning curve. New platforms are also easier to learn than ever before, with simple interfaces, and WYSIWYG customization tools. Rather than needing an advanced degree in statistical analysis, all you need is an afternoon watching tutorial videos to grasp the fundamentals.
  • Data in every job. Data analysis may soon be a skill that’s needed by all roles, rather than a specialty role available to only one person. Already, you can see this need forming; think about all the times you’ve needed data analysis in your own

There will always be a need for data scientists and engineers to come up with new platforms and new solutions to better collect, organize, and process data. But after the next few years, we’re going to see a radical transformation within enterprises that allows every employee to be a part of the data revolution, and that may predict a plateau in the demand for data specialists.

In the meantime, make sure you have the right software available to your data specialists and non-expert employees alike. Try SAP’s enterprise resource planning (ERP) software, and see how in-depth data analytics can transform your company for the better.

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