Data science is having an increasing impact on the professional world, thanks to the increasing availability and increasing importance of data. Data science-related positions are in high demand, and even professionals in basic, non-data-intensive roles are required to pay more attention to how data relates to their job. In fact, demand for data scientists is expected to increase by 28 percent by 2020.
To eager professionals looking to take on more responsibilities, engage more high-level thinking, or make more money, acquiring data science skills seems lucrative, but there’s also a steep cost: the increased stress associated with data science.
Why are data science jobs so stressful, and what can we do to compensate for that stress?
First, data scientists typically work in stressful environments. They may be part of a team, but it’s more frequent that they spend time working alone. Long hours are frequent, especially when you’re pushing to solve a big problem or finish a project, and expectations for your performance are high. Fortunately, there are many easy ways to improve a work environment, from adopting more flexible hours to physically changing your environment with better lighting and window coverings.
Your conclusions and calculations are only going to be as accurate as the quality of the data your team is able to gather. For example, you could spend days perfecting a report on the behavior of your company’s customers, but if your sources of data are unreliable, or if your data are muddied with duplicates and old records, your report won’t be very effective. This is stressful in part because it has the potential to undermine your efforts, and in part because you may be able to exercise very little control over the sources of data your company uses. You can compensate for this by making your teammates more aware of the importance of quality data, and by improving the data in your own systems as much as possible.
Working on big data-related problems means you’re going to run into major hiccups—with no discernable source or root cause. For example, depending on your responsibilities, you might have an algorithm that doesn’t quite work the way you want it to, or you might be unable to find a conclusion that’s consistent across the board. Either way, you’ll need to spend hours debugging or investigating, and even then, there’s no guarantee that you’ll find a convenient solution.
Data scientists aren’t usually entry-level positions. Instead, you’ll be dealing with data for an entire company, looking at thousands of customers or millions of transactions at once. The conclusions you come to have the potential to influence the entire course of a business, which imbues those decisions with more significance. This can make any job more stressful; all you can do is accept the nature of the job and make the most objective decision you can.
If you’re a data scientist in the job market, you might also be stressed about the sheer level of competition. Data science is an increasingly popular field thanks to its high demand, and that means lots of candidates will be shooting for the same open positions. In time, open positions will outnumber applicants, and this won’t be as strong a source of stress.
Whether you’re interested in a career in data science or not, it’s important to prepare for a future that will demand your data science skills. Get used to data-related tools, like SAP’s customer data software, and learn how to compensate for the increased stress that can come with such data-intensive work.