Data science teams need the right skills and solid processes
For data scientists, big data systems and AI-enabled advanced analytics technologies open up new possibilities to help drive better business decision-making. "Like never before, we have access to data, computing power and rapidly evolving tools," Forrester Research analyst Kjell Carlsson wrote in a July 2017 blog post.
The downside, Carlsson added, is that many organizations "are only just beginning to crack the code on how to unleash this potential." Often, that isn't due to a lack of internal data science skills, he said in a June 2018 blog; it's because companies treat data science as "an artisanal craft" instead of a well-coordinated process that involves analytics teams, IT and business units.
Of course, possessing the right data science skills is a predicate to making such processes work. The list of skills that LinkedIn's analytics and data science team wants in job candidates includes the ability to manipulate data, design experiments with it and build statistical and machine learning models, according to Michael Li, who heads the team.
But softer skills are equally important, Li said in an April 2018 blog. He cited communication, project management, critical thinking and problem-solving skills as key attributes. Being able to influence decision-makers is also an important part of "the art of being a data scientist," he wrote.
The problem is that such skills requirements are often "completely out of reach for a single person," Miriam Friedel wrote in a September 2017 blog when she was director and senior scientist at consulting services provider Elder Research. Friedel, who has since moved on to software vendor Metis Machine as data science director, suggested in the blog that instead of looking for the proverbial individual unicorn, companies should build "a team unicorn."
This handbook more closely examines that team-building approach as well as critical data science skills for the big data and AI era.
