For the past few years, big data has been all the rage, especially as businesses across every enterprise sector realize the benefits that it can bring to their organizations. However, in order to reap these advantages, your business must be armed with the tools and know-how necessary to mine information and analyze it correctly. This requires specialized skills, namely those that a data scientist can provide.

Since the emergence of big data strategies, the role of data scientists has been quickly growing in importance and popularity. In fact, a study from Indeed.com discovered that companies posted 57 percent more data scientist job openings during the first quarter of 2015 compared to the same time in 2014, and more than 73 percent more postings the following quarter, according to Fortune.

This has left many enterprise decision-makers wondering if it might be time to bring a data scientist on board within their organization. There are a few important considerations to make here, though, before your business goes on the hunt for a new employee.

Understanding the role: What is a data scientist?

First and foremost, it’s critical that hiring executives and company stakeholders fully understand what the role of data scientists will mean for the business.

“A data scientist can be described as ‘part analyst, part artist.'”

IBM explained that the data scientist position is typically tied to big data projects, and that this role evolved from the data analyst position. However, a data scientist usually examines a much larger quantity of data sets compared to the work that a data analyst does.

IBM vice president Anjul Bhambhri noted that a data scientist can be described as “part analyst, part artist.” In other words, it takes a very special individual to fill this role.

“A data scientist is somebody who is inquisitive, who can stare at data and spot trends,” Bhambhri noted. “It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”

Defining the role

At the same time, however, because the role of data scientist is relatively new, there is no real hard and fast definition of the overall position. While this person typically spearheads and directs a company’s big data analysis, he or she may come from a different background than another professional in the data scientist industry.

For instance, Harvard Business Review’s Michael Li noted that individuals in this position usually come from either a computations or statistics background.

“Candidates with a strong science or math background usually have had rigorous statistical training in distinguishing between signal and noise and can tell when they are ‘overfitting’ a complex model,” Li explained. “Those with a computer science background frequently have the software engineering chops to handle large amounts of data by taking advantage of parallel and distributed computing. While all data scientists need to be functional in both, we’ve found that people coming from each of these backgrounds have quite different strengths and weaknesses.”

It’s important that companies consider candidates’ different studies and expertises, as these should match up with what the business is looking to accomplish with its analysis initiatives.

Is there enough work to be done?

It’s also essential to ensure that once a data scientist is brought on board, there is enough data for them to work with. After all, what’s the use in hiring a new employee if there isn’t a sufficient number of tasks to keep them busy?

Organizations that have large repositories of data will likely need a person to help them comb through and identify the valuable insights that this information might hold. Businesses that are still in the early stages of their analytics initiatives, though, may simply not have the volume of data required to support the data scientist position. What’s more, analyzing incomplete data could provide skewed results that aren’t actionable for the company.

“Not enough data is every data scientist’s nightmare,” big data expert Bilal Mahmood wrote for KDnuggets. “Data scientists thrive when they are able to work with large data sets and event volumes. But more importantly, the specialized skillsets that data scientists employ – linear regressions, bayesian modeling, etcetera, simply don’t work on smaller data sets.”

In addition, it’s also helpful if the company has an idea of what they’re looking to get out of their big data projects. Hiring a data scientist without the necessary data or direction prepared can make the job incredibly difficult for the new employee. What’s more, the business likely won’t see the valuable results they are seeking. For this reason, it’s important that the enterprise is prepared to support this role to the fullest before doing any hiring.

Laptop with graph

Does your company need a dedicated data scientist?

Is it worth the investment?

Smaller companies that deal with smaller data sets, or those that are just starting out with their big data initiatives, may find that the high cost of hiring a data scientist simply isn’t worth it for their business. Fortune’s Barb Darrow pointed out that data scientists are not only incredibly in-demand positions, but are also very high-paying roles as well. Some of the top-paying offers currently are for data scientist roles – even software engineers fall second here.

In many cases, it simply makes more sense for businesses to hire an external consultant to assist them with their big data needs. This ensures that the company gets its big data questions answered without wasting a large portion of the budget on a full-time position that the organization isn’t ready to support.

“Not having a data scientist in the early stages doesn’t mean the data is being ignored – it just means that it doesn’t require the attention of a full-time data scientist,” Yanir Seroussi wrote for KDnuggets. “If your product is at an early stage and you are still concerned, you’re better off hiring a data science consultant for a few days to help lay out the long-term vision for data-driven capabilities. This would be cheaper and less time-consuming than hiring a full-timer.”

If your business falls into this category, contact the experts at Aunalytics. Our data experts can help you gather the right information to address your company’s big data goals. To find out more, contact us today.