February 1, 2017

Big Ideas in Big Data: The Hive & Verizon Ventures Host Women in Data Science Event


In the first in a series of events on Women in Big Data organized by The Hive and hosted by Verizon Ventures, remarkable women data scientists came together to share the impact of data analytics on many elements of our lives.

Events like these are great opportunities for women in tech to connect and network, as well as learn about how others are taking big data insights and applying them to real-world scenarios. We partnered with The Hive, a group that brings together one of the largest communities of data experts called ‘databees,’ which includes entrepreneurs, business and technology practitioners specializing in the use of AI and big data approaches.

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In a room of data scientists and software engineers, while the discussions were highly technical, real-world applications were the focus – using data for dog shelter reform and applying real-time analytics to autonomous driving.

Data For the Greater Good

The first speaker, Jill Dyche, VP of SAS, invited the audience to think of the good that could be done if we applied data science to initiatives outside of our day jobs. Data professionals are used to the idea of using data science to better understand customer behavior and preferences or to optimize business processes, but what if we applied these things to issues outside of our respective tech companies… such as animal shelters. Despite the digitization we’re seeing across almost every industry, animal shelters still use analog processes that are cumbersome and are vulnerable to human error. Shelters provide one page of information with a black and white photo and very basic data such as gender, age, size, breed, and health record all to be identified by the shelter employees. This is a cumbersome intake process for shelter employees or volunteers. More often than not, data points like breeds are misjudged, mislabeled, and may result in lower adoption rates. Shelter staff aren’t incentivized to improve their processes, as they are measured on how many dogs exit-- whether that be through euthanization or adoption.

Digitalization of Shelter System

Jill took it upon herself to visit some of the high-kill dog shelters, film short videos showing the dogs’ “data points” - personality, playfulness, etc. She found that after sharing these videos to her social media networks and receiving 40K+ views, the adoption interest started rolling in. The richer the data provided, the more relevant decisions people can make. So in this case, the more data available on each dog, the richer the customer experience and the faster the dogs are finding great homes. As Steve Jobs famously said, “You've got to start with the customer experience [in this case-- the dogs] and work back toward the technology - not the other way around.”

Deep Learning for Autonomous Driving

Our next speaker was Crystal Valentine, VP of Tech Strategy at MapR, a converged data platform that applies analytical insights to operational processes in real-time.  She kicked off the conversation by highlighting the different ways big data can be used in autonomous cars:

  • Real-time analytics, with cars serving as the new platforms

  • Advanced driver assistance systems (ADAS) that automate and enhance vehicle systems for safety and better driving

  • Computer aided driving, leveraging data from smart cities applications

  • Vehicle healthcare and predictive analytics, which are being used in the insurance and financial services industries

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Machine learning is not new, but Valentine went on to say how next generation tech is only now starting to leverage machine learning to be applied to real-time decision making - a key success factor in autonomous driving.

One of the most interesting parts of Crystal’s presentation was learning about MapR’s semantic segmentation, which allows them to segment and classify the items in each frame of video from on-vehicle cameras. With this technology, autonomous cars can essentially categorize surroundings in real-time - deciphering between humans, driving surfaces, stationary objects, and other vehicles-- reducing risk of accidents and collisions.

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What’s Next?

The final panel, a Q&A moderated by Pinterest Data Scientist June Andrews, brought forth a thoughtful discussion on data science, machine learning and some of the challenges that still exist in the field. The women discussed other sectors that would benefit from real-time decision making, such as government processes and fraud detection services. On growth in the industry, Dyche felt the opportunities were endless with more and more hyper-focused data companies emerging every day.

Closing questions covered issues of diversity and career advice for women in this and all industries. The panelists encouraged attendees, to be proactive, work hard, be smart, and also to be yourself. They reiterated how women can’t simply wait for opportunity to come to them-- and advised them to actively go after the things they want.

This event was the first of many this year featuring spectacular women in data science. Looking forward to the rest!


Tags: Big Data , AI , Autonomous Car , Data Science