Businesses can't rely on chance breakthroughs to power ongoing innovation. The stakes are simply too high.
So how do you put the right people, processes, and products in place to consistently drive market-moving innovations? Increasingly, the answer involves unleashing data science at scale.
At the Rev 3 Enterprise MLOps Conference, leaders shared their recipes for scaling innovation—in the enterprise and beyond.
Watch this recording featuring those leaders and their breakthrough insights.
Jim Swanson is a global business and technology leader and currently Chief Information Officer of Johnson & Johnson, the world's premier healthcare company. Jim is responsible for amplifying Johnson & Johnson's business impact and shaping its direction through the strategic use of technology.
Jim joined Johnson & Johnson from Bayer Crop Science, a $20 billion division of Bayer, where he served as a member of the Executive Leadership Team and as CIO and Head of Digital Transformation. In this role, he inspired teams across the world to use digital innovation and data science to transform and deliver world-class products and services sustainably. Jim and the Information Technology organizations he has led have received industry accolades for their contributions in leadership, application of technology to deliver substantial business value, best places to work in IT, and support of STEM for emerging talent.
Nick Elprin is the CEO and co-founder of Domino Data Lab, provider of the open data science platform that powers model-driven enterprises such as Allstate, Bristol Myers Squibb, Dell and Lockheed Martin.
Before starting Domino, Nick built tools for quantitative researchers at Bridgewater, one of the world's largest hedge funds. He has over a decade of experience working with data scientists at advanced enterprises. He holds a BA and MS in computer science from Harvard.
Linda is the strategic voice and visionary for data and analytics at Verizon, focused on making Verizon an AI powerhouse. Linda joined Verizon in September 2019.
Prior to this role, Linda was the first Chief Data Officer at the Federal Reserve Bank of New York. She shaped the Central Bank's data strategy, establishing a new Data Science organization from the ground up, and cultivating the use of AI by economists and central bankers in monetary and supervisory policy and research. Previously, Linda had a 20 year career at Goldman Sachs, where she was a Managing Director of Technology.