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Webinar Details

Using Designed Data to Correct for Errors in Big Data

Misty Saline, Meghan LePage Beeman, Kay Ricci
Friday, June 25, 2021
1:00pm Eastern Time

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About This Course:

For data to be usable in social research, it goes through multiple steps that can introduce bias or error. ‎The total survey error approach can be leveraged to understand and correct for errors in big data, but ‎some new challenges may also arise with organic data sources. This webinar will cover specific errors ‎found in big data and how panel data can help to correct for them. Nielsen data scientists Misty Saline, Meghan LePage Beeman, and Kay Ricci will leverage their experience with ‎big data sources that measure media as well as share examples from other researchers.‎

Learning Objectives:

By the end of the course, participants will:
  • ‎understand what is big data and the relationship between big data and designed data
  • ‎understand the properties of coverage error and measurement error and their applications to ‎big data sources
  • ‎know how to dimension the potential bias in big data sources and make big data usable for quality ‎measurement using designed data
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About the Instructors:

Misty-Saline-(1).jpgMisty Saline, VP, Data Science, joined Nielsen in 2002 with an initial focus on Demography and Universe Estimation. Over the years, she ‎has broadened her scope to cover television and digital measurement comprehensively, with a special ‎focus on client and strategic engagement. Misty is a graduate of Nielsen’s Data Science Women ‎Leadership program and also Nielsen’s Business Process Improvement program. Prior to joining ‎Nielsen, Misty attended Monmouth College where she earned a bachelor’s degree in Mathematics.‎

Kay-Ricci.jpegKay Ricci, Principal Data Scientist, joined Nielsen in 2014 with a focus on behavioral research methods for TV diary. Over the years, she has ‎led projects related to TV, radio, and digital measurement, and she has more recently concentrated on ‎the development, research, and socialization of new panel methodologies. Prior to joining Nielsen, Kay ‎attended the University of Nebraska where she earned a master’s degree in Survey Research and ‎Methodology with a minor in Statistics.‎

Meghan-Beeman.jpgMeghan LePage Beeman, Senior Data Scientist, began her Nielsen career in 2015 in client consulting. In that role, she helped manufacturing clients ‎leverage what is now NielsenIQ retail sales and buyer behavior data. After earning a master’s degree in ‎data science from Indiana University, she transitioned to work on product research and methods ‎development, specifically focused on incorporating big data into media measurement. Meghan recently ‎shifted focus again and now supports client and strategic engagement for data science initiatives‎.