Data Scientists Are More Than Just Scientists

As a growing area within business, data science can broadly impact many areas within a company. Not only can data scientists help explain and quantify risk, but they can also analyze the consequences of major decisions. However, being a good scientist isn’t always enough for professionals within this role. A more developed skillset is being looked for now as opportunity in this area continues to grow. Breaking though the idea that data scientists should function in isolation, this new importance being placed on general business acumen for data scientists is gaining momentum.

Growing Opportunity in Data Science

According to The U.S. Bureau of Labor Statistics, positions for analysts like data scientists continue to be plentiful. Between 2016-2026, job growth is projected at 27 percent, well above the national average for all occupations in the U.S. Directly influencing this growth are general technological advances which make it easier to gather data, and the desire by companies to have efficient and affordable solutions grounded in evidence. Key stakeholders in companies across industries are realizing that data and decision-making go hand-in-hand and business planning can be positively impacted through careful interpretation of data.

Simultaneous to this job growth, employers are coming to a better understanding of what a data scientist does and what they could do to improve operations. It’s assumed that a data scientist would have an educational background in math, statistics, computer programming, and even engineering. All of this helps make a data scientist a better, logical thinker and problem solver, which gives them a unique perspective within a company where most people are trained in business or a related field like marketing, finance, or economics.

Expanding the Skillset of a Data Scientist

With all the practical skills a data scientist develops within their formal education, the other component not being looked for, business acumen often comes from simple work experience. Background within the industry where the data is applied is quickly becoming as essential as being able to interpret the data itself. This calls for data scientists to shift their professional viewpoint, realizing they can apply a desire to understand a problem from top to bottom, from the business side of things as well as from analyzing data solutions.

The secret is identifying as a business person as well as a scientist. According to Thomas C. Redman, sharing his professional experiences with The Harvard Business Review, “The biggest successes [for data scientists] stemmed not simply from technical excellence but from softer factors…” These included a:

• Deeper understanding of business problems.
• Complete trust between data scientists and decision makers.
• Presentation of results in simple, powerful language.
• Willingness to work patiently with everyone impacted by the data.

Data science and business successfully converge, says Redman, when connections are made with the right people, at the right times, in the right ways. This often can’t happen if data scientists see themselves as working within a silo of statistics.

Integrating into the Business Side

Even if you don’t immediately see the connection between the data you’re analyzing and the business principles concerning stakeholders, examining both can help you relate to your colleagues working outside of data science. You discover a way in to the areas of your company that focus on daily operations, learning how they communicate with each other. This provides a better understanding of how to present findings in ways more easily digested. It also helps you draw relevant conclusions within your data. Reviewing data in a vacuum could lead to a variety of conclusions, yet some may be completely incorrect when applied within the greater environment. Understanding the business within which your data functions enables you to extrapolate only relevant conclusions.

Ways in which a data scientist can broaden their skillset and gain a deeper perspective on the actual business they’re working within include:

• Immersing yourself in the day-to-day work of your organization:

In addition to analyzing incoming data, try to have a curiosity about the business and an eagerness to find ways to make it better. Connect with stakeholders by stepping away from the data and spending time observing the other work going on around you. Get out into the office. Don’t fall into a data science silo.