Nielsen used Databricks to test new digital advertising rating methodologies on a large scale. Databricks allowed Nielsen to run analyses on thousands of advertising campaigns using both small panel data and large production data. This identified edge cases and performance gains faster than traditional methods. Using Databricks reduced the time required to test and deploy improved rating methodologies to benefit Nielsen's clients.

1. Matt VanLandeghem, Nielsen
How Nielsen Utilized
Databricks for Large-scale
Research and Development
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2. 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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3. 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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4. How does DAR work?
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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…

5. 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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6. 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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7. 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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8. 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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9. 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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12. 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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13. 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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14. 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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15. Copyright © 2017 The Nielsen Company. Confidential and proprietary.
Special thanks: Mala Sivarajan, Anil Singh