Sep 23–26, 2019

Driving adoption of data

Moderated by:
David Boyle (Audience Strategies)
Panelists:
Richard Evans (Statistics Canada), Leah Xu (Spotify), Victoriya Kalmanovich (Navy), Moise Convolbo (Rakuten)
2:30pm3:00pm Tuesday, September 24, 2019
Location: 1A 06

Often, the difference between a successful data initiative and failed one isn’t the data or the technology, but rather its adoption by the wider business. With every business wanting the magic of data but many failing to properly embrace and harness it, we will explore what factors our panelists have seen that led to successes and failures in getting companies to use data products.

Photo of David Boyle

David Boyle

Audience Strategies

David Boyle is passionate about helping businesses to build analytics-driven decision making to help them make quicker, smarter, and bolder decisions. Previously, he built global analytics and insight capabilities for a number of leading global entertainment businesses covering television (the BBC), book publishing (HarperCollins Publishers), and the music industry (EMI Music), helping to drive each organization’s decision making at all levels. He builds on experiences working to build analytics for global retailers as well as political campaigns in the US and UK, in philanthropy, and in strategy consulting.

Photo of Richard Evans

Richard Evans

Statistics Canada

Richard Evans is a 28-year veteran at Statistics Canada, Institut national de la statistique et des études économiques (Insee). He’s an expert in high-frequency economic indicators, a transformative leader, and an architect and project executive of the CPI Enhancement Initiative. Richard is passionate about using data science and AI to create user-centric data products from big data sources and is a recruiter of tomorrow’s statistical leaders.

Photo of Leah Xu

Leah Xu

Spotify

Leah Xu is a software engineer at Spotify, where she works on analytics for marketers, real-time streaming infrastructure, and Spotify Wrapped. Previously, Leah worked on data infrastructure and secure deployments in the cloud at Bridgewater and Nest.

Photo of Victoriya Kalmanovich

Victoriya Kalmanovich

Navy

Victoriya Kalmanovich is an R&D group lead at a large maritime corporation in Israel. She specializes in healing work environments by addressing them as startup companies and promotes and leads innovative and broad processes throughout the organization. In her day-to-day experience, she deals with all technological issues, product management, budgets, and client handling of her group. She’s an education enthusiast and often uses educational directives as a part of her management strategies, especially guiding group members and leadership. She’s also a firm believer in deploying data science where there’s a great value for data. She’s organized a successful data science hackathon and is forming a data science community within her organization. She also gives talks about management, leadership, and workplace challenges.

Photo of Moise Convolbo

Moise Convolbo

Rakuten

Moise Convolbo is a data scientist and research scientist at Rakuten, where he’s harnessing the potential of customer data in reaching “zero customer dissatisfaction.” He built a platform called the Rakuten PathFinder, which empowers product stakeholders such as PDMs, managers, and test engineers to focus on specific struggles along the users’ journeys in order to improve the company’s products and measure their business impact. It’s currently used by Rakuten Gora (the #1 golf course reservation site in Japan), Rakuten toto (the #1 lottery betting site), Rakuten O-net (the #1 match-making web service), and Rakuten Keiba (a horse racing betting service). Moise has had a long experience working with data, the cloud, and geodistributed data centers. He’s always been fascinated by what comes next, in terms of utilizing data from strategic data-informed business decisions. He’s active i academia and has spent time as a reviewer for major big data, cloud optimization, and data science journals from ACM, Elsevier, Springer, and the IEEE.

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