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Data-Driven Product Innovation

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Authors: Xin Fu, Hernan Asorey Abstract: Data Science is an increasingly popular area of Knowledge Discovery and Data Mining. Leading consumer Web companies such as Amazon, Facebook, eBay, Google and LinkedIn, as well as B2B companies like Salesforce, possess Petabytes of data. Through effective mining of this data, they create products and services that benefit millions of users and generate tremendous amount of business value. It is widely acknowledged that Data Scientists play key roles in the creation of these products, from pattern identification, idea generation and product prototyping to experiment design and launch decisions. Nonetheless, they also face common challenges, such as the gap between creating a prototype and turning it into a scalable product, or the frustration of generating innovative product ideas that do not get adopted. Organizers of this tutorial have many years of experience leading Data Science teams in some of the most successful consumer Web companies. In this tutorial, we introduce the framework that we created to nurture data-driven product innovations. The core of this framework is the focus on scale and impact - we take the audience through a discussion on how to balance between velocity and scale, between product innovation and product operation, and between theoretical research and practical impact. We also share some guidelines for successful data-driven product innovation with real examples from our experiences. We end the tutorial by discussing the organizational perspective of data-driven product innovation: how to structure Data Science teams so Data Scientists collaborate effectively with other functions, and how to hire and grow talents into Data Scientist roles. ACM DL: http://dl.acm.org/citation.cfm?id=2789994 DOI: http://dx.doi.org/10.1145/2783258.2789994
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