Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining and programming skills. This topic came to us from Tech Target in their article, “How to structure and manage a data science team.”
In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. And like most endeavors, this is accomplished better with a team.
Data science teams typically include various analytics and data professionals and can be set up in different ways. Responsibilities for data collection, management and analysis once typically fell under the duties of the chief information officer (CIO). However, over the past two decades, more organizations separated the data function into its own department as the amount of internal data stores grew, supporting technologies evolved and data-related tasks became more differentiated and specialized.
Executives and team leaders who are seeking to build and mature their data science programs should consider designing their teams for the highest synergy opportunities.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.