Putting predictive models into production demands a streamlined workflow, and highly skilled data scientists and engineers. For the past few years, organizations have focused on developing strong proof of concepts and initial use cases. Now they must successfully introduce data-driven concepts into their daily operations. This year, data-driven companies will make productionizing data projects the priority and focus. Productionizing will increase exposure in other parts of the company, so data science teams will turn to proven methods, like microservices and APIs, to streamline their process. Data governance will become a standard part of any data project. Big Data technology has matured and is mostly enterprise-ready, increasing the pressure on and demand for data scientists. Major tech players like Google, LinkedIn, and Facebook will continue to open-source their product innovations in an early stage. In 2017, the gap between textbook data scientists and seasoned practitioners will widen further. Companies that retain, foster, and attract people who can apply data science in production will gain a significant competitive advantage.
