Companies have adopted data into their DNA using a variety of methods, including data driven, data enabled, and data informed, but many implementations have fallen short of the promised ROI, the result of a gap between the cost of investing in people and infrastructure and the business value delivered.
June Andrews takes a structured look at how to strategically invest in data to maximize the benefit gained from incorporating data, highlighting situations when investment in experimental platforms doesn’t make sense and others when building a custom analysis platform does. Along the way, June explains how best practices have evolved over time. The result is to grow the mindset from creating data-driven organizations to creating data-competitive organizations that can adapt and deliver in the rapidly changing landscape between data science, machine learning, and artificial intelligence.
June Andrews is a principal data scientist at GE, where she’s building a machine learning platform used for monitoring the health of airplanes and power plants around the world. Previously, she spearheaded the Data Trustworthiness and Signals Program at Pinterest aimed at creating a healthy data ecosystem for machine learning and led efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.
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