Data Science and AI have become a key part of business success — and capitalizing on data depends to a large extent on building the right team, structuring it in the right way, and setting up the processes that enable open and efficient dialogue between Data Science and Business people.
Within the technology sector, numerous companies find it difficult to deliver effective data science programs at scale. Despite hiring best data scientists and providing a promising AI solution, many pilots often fail to evolve into real, actionable results. Very often the reasons for failure are to be searched in the way technology and business teams operate together. The start-up environment, which often combines the need to maintain long-term vision, even under the pressure of short-term goals, exacerbates these challenges even further.
There is no perfect way to structure the data science team, or best way to facilitate data science – business dialogue. However, in this series, Natalia will aim to give a few recommendations on how to balance various business interests, how to structure data science team efficiently and to ensure that this talent is also aligned with the business strategy.