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As one of the most lucrative jobs in the tech field, data scientists don’t have much room for error. Here are the biggest mistakes they can make.
Data scientists are responsible for organizing and analyzing data for a business. With companies generating more data than ever before, these professionals are in high demand, placing first on Glassdoor’s Best Jobs in America list for the past four consecutive years.
Those working in data science are familiar with big data analysis, machine learning, coding languages, algorithms, and problem assessment, reported TechRepublic’s Alison DeNisco Rayome. However, technical skills alone won’t cut it.
Communication, collaboration, and constant learning are also necessary components for success in data science. Without both technical and interpersonal skills, data scientists will be let go, and easily replaced as their numbers rise.
“Being a successful data scientist requires a mix of technical skills, higher order thinking, and down-and-dirty problem solving,” said Roger Yarbrough, principal and cofounder of marketing consulting firm Stratistry. “Given that this mix of talent isn’t necessarily part of standard college curriculum, you’ll find many data scientists without the necessary real world experience to fully understand the potential pitfalls you can encounter when working with data.”
Data scientists can succumb to many pitfalls, as with any profession. Here are the biggest mistakes data scientists make that ultimately cause them to fail:
1. Focusing only on the solution
Data scientists are called in to solve business problems, as well as implement analytics, said Ganes Kesari, head of analytics at Gramener. “This is the holy grail of data science,” Kesari said. “One needs to frame the right business questions, and evolve a sequence of steps to solve them. But, this is where most data scientists falter.”
Focusing solely on the solution could create problems along the way; data scientists must remember the context with which the problem was posted, said Keith Williams, data scientist at Red Ventures.
“You have to understand how those systems typically work and how they interact with the solution,” he said. “Failure to do this legwork often manifests as a downstream bug, leaving you holding the bag with only a vague notion of what’s going wrong and where it’s happening.”
2. Forgetting the basics
While understanding how artificial intelligence (AI) and machine learning systems work is vital to a career in data science, these professionals often overlook the basics, said Kesari.
“Candidates flaunt 90% accuracy levels of AI models in projects. But it’s a tragedy when they struggle to explain what a p-value is, or how to use excel to extract simple patterns from data,” Kesari said. “A data scientist who has model building skills without fundamentals is like a pilot who can fly an airplane without knowing what the cockpit dials mean.”
“Simple tools like linear regression can actually be quite powerful when paired with well-curated data and integrated into a system where the outputs are actionable,” Williams added. “A techno-optmimistic data scientist will labor attempting to get the latest deep neural network applied to their problem only to find that some upstream process needs to be addressed before anything else can occur. By using simple solutions first, such issues will be quickly identified without burning credibility.”
3. Ineffectively communicating
Finding analytical results is important, but successful data scientists know how to productively communicate those results, said Kesari.
“The utility of analytical results is directly proportional to the decisions that can be taken using it. Data scientists assume that the users understand analytics,” Kesari said. “They don’t take the time to translate the results into a format that users can act upon. Business interpretation and data visualization are invaluable skills that often get sidelined.”
The best data scientists become aware of these mistakes and take measures to limit them, Yarbrough said, and they are able to do this because they have both technical and interpersonal skills.
“It’s one thing to understand and apply concepts in an academic environment, but another thing entirely to do so in the real world with all its pressures,” Yarbrough said. “Those who work hard to protect the integrity of their data and take the right steps to ensure its accuracy will find their work to be valuable to both themselves and to those who rely on it as well.”
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