There is no doubt that data science–and predictive analytics– are the next wave of investments aimed to create significant improvements to corporate bottom lines. With the advent of new capabilities, however, generally comes a commensurate amount of complexity and challenge. In the new era of analytics, many companies struggle to properly connect three key divisions: Data Science, IT, and Business Teams. Throughout these divisions time, money, and energy is invested into analytics and model-making with the hope of tangible operational improvement. And while success stories are many, there are many counter examples that show the challenges that new capabilities bring: some models never come to fruition and drive true business decisions, or worse are misinterpreted when passed through various hands to the point of uselessness or harm. I have witnessed such scenarios in my own career as well as in those of friends, colleagues, and others. With these experiences and the emerging importance of analytics, it’s becoming increasingly clear that a new role will be required in many organizations to ensure maximum ROI from data science investments: AnalyticOps.