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Breaking Data Science Open by Christine Doig, Michele Chambers, Ian Stokes-Rees

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Chapter 8. Data Science in the Enterprise

While we’ve discussed the challenges and rewards of Open Data Science in general, we’ll now turn our attention to the specific needs of enterprise users. Enterprises are turning to new rich data sources to augment their data science efforts, and are using advanced techniques (including machine learning and artificial intelligence) to increase the accuracy and precision of their work. The combination of enterprise-scale data sources and the technology to make the most of them means their results can be more robust and drive new business value. One such avenue is personalization, which demands not just batch-processed aggregated market analysis but real-time individualized results. To truly understand the customer, product, market, employees, network, and systems, enterprises are using the most granular level of data. From customer clicks and transactions, to individual DNA data and facial data, to geolocation and sensors, the list of new data sources appears unending. This data is being used with the latest innovative and advanced analytics techniques available—typically open-sourced immediately by academic researchers worldwide—by teams that collaborate across groups, departments, and geographies to create better models that yield significantly better results. And perhaps most exciting is that the tools your teams can leverage today are simultaneously being improved upon and released by researchers, academics, and members of the Open Data ...

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