The most significant challenge in the enterprise today is messy and siloed data: Bad plumbing, which forces data scientists to spend 80-90% of their effort cleaning and preparing data instead of actually analyzing it. Pulling from first-hand experience, Eliot shares how Fortune 500 companies are fixing their plumbing and gaining $100s of millions of value by automating the collection, organization and preparation of enterprise-wide data (supplier, customer, product, financial, etc.) to drive key spend, cost and revenue outcomes.
Eliot Knudsen is Data Science Lead at Tamr where he works on technical implementation and deployment. He's worked with fortune 100 clients to dramatically reduce spend by unifying sourcing data and implementing procurement analytics. Prior to Tamr Eliot was a Data Scientist in Healthcare IT, applying machine learning to patient-provider matching algorithms. Eliot is a graduate of Carnegie Mellon University where he studied computational mathematics, statistics and machine learning.
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