TitleNormalizing and Comparing Addresses with TeamMate Analytics

There are a number of reasons auditors would like to compare addresses using data analytics tools. For instance, to look for duplicate vendors or compare employees to vendors. The problem is that the same address can be written a number of slightly different ways and still be a valid address. 123 N Main St. is the same address as 123 North Main Street but would fail a duplicates check. Fuzzy matching algorithms can also lead to many false positives. In this topical webinar, we'll provide you with strategies for normalizing your addresses and show how to implement those strategies using TeamMate Analytics.

Learning Objectives

  • Understand various challenges in comparing addresses
  • Learn strategies for normalizing and comparing addresses
  • Witness the detailed steps involved to normalize and compare addresses
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Ken Petersen PMP, MBA
Director, Client Development Initiatives, Wolters Kluwer TeamMate

Ken Petersen has over 25 years of experience developing and implementing systems and working with data in a variety of capacities while working for both Fortune 500 and entrepreneurial software development companies.  Since 2002 Ken’s focus has been on the Governance, Risk and Compliance space helping numerous customers across multiple industries implement software solutions to satisfy various compliance needs including audit and SOX.  Ken has been with Wolters Kluwer since 2009 and is currently a director and product manager of the TeamMate Analytics product focused on helping auditors perform data analytics.  In this role Ken communicates regularly with a variety of auditors and audit organizations about their audit analytics challenges, needs and successes.  Ken is PMP certified, holds an MBA from the University of Dayton and a BSBA in Information Systems from the Ohio State University.