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    JP Creedon
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    Uber has orchestrated more than 2 billion total rides in 450 cities in 72 countries around the world. So it’s safe to say the company has accumulated a serious amount of data. But what exactly does Uber do with it? Cory Kendrick – a data science manager at Uber who formerly worked at Apple and Google – shared what Uber can deduce from its data during the Hewlett Packard Enterprise’s Big Data Conference 2016 on Wednesday.

    BIG DATA ENABLE US TO NOT JUST SOLVE BIG PROBLEMS, BUT ALSO TO UNDERSTAND THE IMPACT.

    Kendrick, a Yarmouth native and Dartmouth alumna, said, “Big data enable us to not just solve big problems, but also to understand the impact.”

    For Uber, big data translates to trips. Lots of trips. Among the possible interpretations the company can make from its data, Kendrick said they look at the trips taken within an area and determine:

    1. When the bars close. It might sound like a joke, but it’s not. “You can make a good guess based on when ride requests spike at night,” Kendrick explained.

    2. How communities connect with each other. Uber has been making an impact on cities by filling in “transportation deserts” or areas not serviced by trains, buses or taxis, as Kendrick put it. Now, Uber can analyze areas formerly inaccessible if people didn’t have cars, trying to gain an understanding of how users interact within and between them.

    3. How city residents use all of the transportation available to them. Kendrick mentioned Uber riders use the service to “go the last mile” when connecting with public transportation. By tracking these last mile instances, Uber has a broader picture of general transportation in a city.

    As with any other organization leveraging big data, Uber isn’t analyzing this wealth of information in a vacuum. It’s used to identify how the company can improve life in cities.
    For example, Uber Pool is the product of data analytics. Kendrick explained that for every ride going down at any moment, there’s a “look-alike trip.” Analytics, as well as matching algorithms, are key to “get more people into fewer cars and make streets less congested,” she said.
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