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Bill Winkler, Global IDs - #MITCDOIQ - #theCUBE

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Published on Jul 13, 2016

01. Bill Winkler, Global ID's, Visits #theCUBE!. (00:19)
02. What Does Data Quality Have To Do With Customer Experience. (00:44)
03. How Are You Helping Customers Make Or Save Money With Data. (02:13)
04. How Do You Get Data Out Of Spreadsheets And Into A Shared Space. (03:32)
05. How Do You Seperate The High Quality Data Versus Low Quality. (04:53)
06. Where Are Your Customers Looking For Data Quality. (06:36)
07. How Hard Is It To Measure Customer Service Satisfaction. (07:57)
08. Can You Give Us An Idea Of How We're Making Progress. (09:01)

Track List created with http://www.vinjavideo.com.
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Managing data for great customer service | #MITCDOIQ
by Zoe Bernard | Jul 13, 2016

Data management is a forefront concern for businesses — especially when it comes to dealing with customer service. A glitch in data can yield any number of troubling issues, from an order
placement gone awry to a delivery shipped to the wrong address.

Bill Winkler, CTO of Global IDs, sat down with Stu Miniman (@stu) and Paul Gillin (@pgillin), cohosts of theCUBE, from the SiliconANGLE media team, today at MIT CDOIQ Symposium to discuss how customer service and data are interconnected.
“Service orders drive everything,” Winkler said. “If a customer orders equipment and finds a small defect in the order, the missing pieces of information will cause automated processes to fail.” This, in turn, results in inferior customer experiences.

Simplifying data complexity

Keeping these huge amounts of data organized and accurate is a primary concern for a business’s quality control. In an industry with vast amounts of information, it’s not uncommon to find skeins of corporate data tied up in dormant Excel spreadsheets. It’s up to Winkler to sieve through these datasets (most of which are rife with duplicate entries) and find ways to make this information useful.

And there’s plenty in store for this burgeoning field. “We’re finding new ways that we can identify how customer service problems relate to data,” Winkler said. “In the future, we hope to demonstrate the cost of those errors and continue to simplify data complexity.”

#MITCDIOIQ
#theCUBE

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