As organizations seek to mine their datasets for ultra-valuable insights, the demand for data scientists, data engineers, and data analysts is stronger than ever. Data-related budgets will likely increase for the next several years as executives across the country attempt to win this “talent race” against competitors.

What worries organizations about their current data-analysis setups? Hiring the right talent at the right time is always something that keeps executives and HR managers up at night. But as a new study by Unsupervised.com highlights, there are also concerns about whether companies can handle specific parts of the data-analytics process. 

Unsupervised.com surveyed 223 data professionals and 226 business leaders, so it’s a pretty small sample size, but nonetheless we have a taste of what intimidates organizations the most about working with massive datasets:

Analysis clearly worries organizations, but so does processing, as well as putting insights into action. As the following chart illustrates, executives are also deeply concerned about the massive volume and constantly shifting nature of data, along with potential inaccuracies:

For data scientists and analysts, the job isn’t limited to crunching datasets for crucial insights; at every stage of the process, you must use “soft skills” such as communication and empathy to ease executives’ and team members’ concerns about the analytics process. 

That’s a challenging position for any technologist to find themselves, and it’s a key reason why compensation is correspondingly high. A late 2021 study by executive recruiter Burtch Works suggested that, for data scientists and engineers, some 51 percent of those who changed jobs between the second and fourth quarters of the year received a base salary increase of at least 20 percent. According to Emsi Burning Glass, which analyzes millions of job postings across the country, data scientists can easily make six-figure salaries—and that’s in addition to other perks and benefits such as equity.

In order to secure these roles, though, you’ll also need the right combination of skills and experience, and keep up-to-date on the latest tools and techniques. Depending on the organization and seniority of the role, for example, data scientists might have to master everything from Python and SQL to machine learning. Given the complexity of modern data projects, a full portfolio of abilities is often a must when applying to a particular position.