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All Upcoming Training

Data Quality for Financial Professionals

Virtual Seminar

Educational

Tuesdays, July 24 - August 7, 2018
11:00 AM - 12:00 PM Eastern Standard Time

$375 | $475
3.6
3.6
3.6
Finance
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Financial professionals are dependent on high-quality data to perform their responsibilities successfully. At times, they must go through time consuming processes to verify numbers, resolve discrepancies between systems, and track down errors.  It can be frustrating, exhausting work, and worst of all, one can never feel certain that everything is correct.

This three-part series aims to demonstrate how financial professionals can attack data quality proactively, with special emphasis on the first steps they can take. Beginning with a case study, this course illustrates the benefits that finance departments can reap by attacking data quality proactively. Participants will explore competing approaches and summarize why data quality is a management issue. This course also introduces the Friday Afternoon Measurement as a quick means to address “Do We Have a Data Quality Problem?” and strategies to eliminate issues. 

Learning Objectives


  • Make a simple data quality measurement, aimed at answering the questions “do I have to worry about data quality?”

  • Learn a simple method for finding and eliminating root causes of error.

  • Learn enough background to start a quality program within one’s span of influence:

    - the competing approaches to managing data quality

    - what data quality means and

    - why data creators and data customers, roles financial professionals play everyday, are so important

Pre-Requisites

None
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Speaker

Thomas Redman resize Dr. Thomas C. Redman
Dr. Thomas C. Redman, President, Data Quality Solutions
Dr. Thomas C. Redman, “the Data Doc,” President of Data Quality Solutions, helps start-ups and multinationals; senior executives, Chief Data Officers, and leaders buried deep in their organizations, chart their courses to data-driven futures, with special emphasis on quality and analytics. Tom’s most important article is “Data’s Credibility Problem” (Harvard Business Review, December 2013) He has a Ph.D. in Statistics and two patents.