Analytics is a core function in today’s businesses and corporations, enabling companies and organizations to get insights from raw data and make better business decisions and operations. In the era of big data, it is increasingly difficult to analyze the huge amount of data in an effective and scalable manner. LinkedIn has recently formed a company-wide analytics team with 150+ talented analysts and data scientists to work together to drive business impact at scale.
Michael Li and Chi-Yi Kuan offer an overview of the EOI (enable-optimize-innovate) framework for big data analytics. Michael and Chi-Yi explain how to leverage this framework to drive and grow business in key corporate functions, such as product, marketing, and sales, and explore use cases in these key functions, as well as lessons learned during LinkedIn’s journey to scalable big data analytics.
Michael Li is head of analytics at LinkedIn, where he helps define what big data means for LinkedIn’s business and how it can drive business value through the EOI analytics framework. Michael is passionate about solving complicated business problems with a combination of superb analytical skills and sharp business instincts. His specialties include building and leading high-performance teams to quickly meet the needs of fast-paced, growing companies. Michael has a number of years’ experience in big data innovation, business analytics, business intelligence, predictive analytics, fraud detection, analytics, operations, and statistical modeling across financial, ecommerce, and social networks.
Chi-Yi Kuan is director of business analytics at LinkedIn. He has over 15 years of extensive experience in applying big data analytics, business intelligence, risk and fraud management, data science, and marketing mix modeling across various business domains (social network, ecommerce, SaaS, and consulting) at both Fortune 500 firms and startups. Chi-Yi is dedicated to helping organizations become more data driven and profitable. He combines deep expertise in analytics and data science with business acumen and dynamic technology leadership.
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