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Description:

Procurement generates billions of pieces of data about spend annually. This data is spread throughout the organization, often difficult to locate, categorize and maintain. As a result, sourcing managers find themselves spending more time preparing the data than working on the actual analysis.

The best procurement organizations have made it a strategic priority to streamline how they locate, manage and maintain this data, so they can generate insights and savings faster than their competitors. Organizations that are able to prepare for business-critical discussions in days instead of weeks benefit from better decision making and lower supply chain costs.

In this webcast, you'll learn about the:

• Breakthrough methods in data preparation for gaining accurate procurement insight in days instead of months
• Value created in answering business questions, including, "What is my spend by commodity per vendor?"
• State of procurement analytics from direct user survey results focusing on maturity of analytics capabilities

About Matthew Holzapfel, Consultant — Tamr

Matt Holzapfel is an independent procurement consultant at Tamr Inc. Prior to consulting with Tamr, Matt held positions in Strategy at Sears Holdings and Strategic Sourcing at Dell, where he led the implementation of new sourcing techniques to significantly lower procurement costs. Matt has a BS in Mechanical Engineering from the University of Illinois at Urbana-Champaign and is a recent graduate of Harvard Business School.

About Eliot Knudsen, Data Scientist & Field Engineer — Tamr

Eliot Knudsen is a Field Architect 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.