Solving hard data problems is only part of the mission given to data science teams. They also need to make sure that data science results get properly operationalized to deliver business value to the organization. Operationalizing data analytics is very much use case - dependent. It could mean, for example, creating a dashboard that synthesizes insights for decision makers or integrating a machine learning model, such as a recommendation engine, into a web application.
In most cases, this is where data science meets software engineering (or as some would say, where the rubber meets the road). Sustained collaboration between the teams—instead of a one-time handoff—is key to a successful completion of the task. More often than not, they also have to grapple with different languages and platforms, leading to significant code rewrites by the software engineering team.
We experienced it firsthand in our Sentiment analysis of...