Matt VanLandeghem, Nielsen
How Nielsen Utilized
Databricks for Large-scale
Research and Development
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About Nielsen
• Founded 1923
• Buy & Watch
– Buy: Market Research
– Watch: Audience Measurement
• Not just TV!
• Also Radio and Digital, including PC, Mobile, Connected Devices, Digital
Audio, Digital TV
• Digital Ad spending now meeting/exceeding TV Ad spending
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What is Nielsen Digital Ad Ratings (DAR)?
• Measurement of computer, mobile, and over-the-top device audience
– Comparable to TV ratings
– Who is behind the screen?
• Advertising campaigns
– Primary focus is age/gender demographic breaks
– On-Target Delivery (%) is a key metric
• Global product
– 25 countries
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How does DAR work?
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4.3
2.4
2.0
0
1
2
3
4
5
Third-party
Demographics
Report to ClientAd Impression
“Big Data”Mobile, computer, over-the-
top
Overnight daily reporting of:
Unique audience, Ad
impressions, On-Target %
Nielsen
Bias-Correction
Adjustment
Focus of today’s
presentation…

Nielsen Adjustments
• “Big Data” is not perfect
– Needs bias correction
– Where the value of Nielsen’s high-quality panels really
shines
– Nielsen’s panels provide a “truth set” that can be used to
develop models that adjust big data
• 3 sources of bias
– Misrepresentation
– Misattribution
– Non-coverage
• Nielsen adjustments are an active area of Research
and Development
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Nielsen Adjustments
• Metered home PC behavior
– Representative sample of U.S. homes
– “Medium” data
• Production impression data
– Big data
• What is the best way to create Nielsen adjustments
AND test them in a production environment?
• Foundation for Nielsen’s Databricks Use Case
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Nielsen Business Case
• Recently created new DAR adjustment
methodologies
– Small-scale testing showed the new methodologies are an
enhancement over current methodologies
• Business requirement: test new methodologies on a
large # of campaigns
– Need to understand client impact
– Large-scale testing could identify corner or edge cases where new
methodologies could break down and cause a data quality issue
– Small scale testing: ~20 campaigns
– Large scale testing: ~4000 campaigns
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Databricks
• Cluster management
• Provide a friendly interface to Spark for our
Data Scientists
– Multiple programming languages
– Create adjustment factors
• Uses an algorithm not available in SQL
– Link to production databases
– Apply adjustment factors to production-level data
– Analyze data with new adjustment factors applied
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Nielsen Business Case
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Aggregated panel data
Netezza
Cloud
-Combine small and large data
-Run all analyses in one place
using PySpark/Spark SQL
Data Lake
Oracle
Aggregated production data

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Nielsen Business Case
• Performance gains:
– What would have taken 36 hours with standalone
Python only took 1.5 hours in Spark/Databricks
– Edge-cases identified
• Advantages of one methodology over another also
identified
– Short turn-around if any revisions to
methodology
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Nielsen Business Case
• Other benefits
– Reduced time from idea to deployment
– Enhanced support/investigation once deployed
• Client data inquiries and issues addressed quicker
– Collaboration
• Application Development teams
• International data science teams
• These new methodologies being tested in other
products
– Enhanced skillsets of data scientists
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Summary
• At the end of the day, the Databricks/Spark
technology allowed us to solve this business
use case
• The reduced R&D timeline plus extensive
testing will allow enhanced methodologies to be
available to our clients sooner
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Copyright © 2017 The Nielsen Company. Confidential and proprietary.
Special thanks: Mala Sivarajan, Anil Singh