data steward's guide to machine learning

Leading-edge consumer technology companies, such as Google, Amazon, and Netflix, have demonstrated the impact that machine learning can have on the customer experience. These brands have become some of the most valuable in the world by delivering experiences that feel magical to the end consumer by using machine learning to make helpful recommendations, tag pictures, and translate documents. They’ve also made machine learning top of mind among executives at enterprises across all industries who recognize the need to adopt it to avoid being disrupted.

Data steward machine learning

 

As consumers, we’re primarily aware of how machine learning impacts the ‘last mile’ aspects of the customer experience. But this technology is also readily applied to all areas of business operations. Data stewardship, an often ill-understood, but vital part of Data Operations, is one such area. The following white paper goes over 5 ways how machine learning can be used to increase the effectiveness of data stewards.

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About the author 

Matt Holzapfel

Matt Holzapfel leads Solutions at Tamr. He is responsible for delivering new offerings that combine Tamr’s software products with data and services to help customers realize more value from their data. Matt has worked directly with many of Tamr’s largest customers, enabling them to successfully deploy Tamr’s Solutions. Prior to joining Tamr, Matt held positions leading the strategy & development of new analytics capabilities at Dell in strategic sourcing and Sears Holdings. Matt has a BS in Mechanical Engineering from the University of Illinois at Urbana-Champaign and an MBA from Harvard Business School.

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