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Big Data

  Andrew Jeavons
     President
Survey Analytics LLC
Overview
• Coming out of the data desert.
• What do we mean by Big Data ?
• Behavior, Big Data and Market Research.
• Get a model to get ahead.....
Data Desert

• Data was scarce..
     • The hunt for completes.
     • Sampling ?
     • Limited data.
     • Limited potential...
The Deluge
 • The Data Tsunami.
 • What we get..
 • What we don’t get...
Forms of Data
• Metadata, Paradata, Data
• Big Data and Prediction
  of behavior.
• The central dogma:
   • Big Data is Marxist.
   • Black Swans - Taleb’s
      cautionary tale...
Machine Learning


• What is meant by this ?
• Learning and pattern recognition.
Nerves and Genes


      • Neural Networks
      • Genetic Algorithms
      • “Agnostic” algorithms...
Neural Networks
Originally sought to reproduce the way
the nervous system works.

Had the ability to learn complex
relationships with any assumptions.

Can learn complex non linear
relationships.
Back Propagation
Genetic Algorithms
Biological metaphor: encode problems as chromosomes.
Fitness function.
Evolve different mapping functions via principles of evolution.
Cross over and mutation (note: mutation rates not as powerful as
you may think...)




MR example: used GA’s to evolve panel sample balancing - rim balancing.
Social Media Data


•   We have a lot of text...that we didn’t have
    before...

•   Text has its own set of problems..unlike
    Gore Vidal we don’t speak in sentences.

•   “#itsucks phat”

•   Txt messages, social media messages.

•   Parsing and tokenizing
Sentiment Analysis

• One of the frontiers for MR and Big Data
• Can we tell the sentiment behind the
  comment ?
• Text is messy because language is messy.
     • Grammatical vs Stochastic
Surveys, Market Research
         and Big Data
• Fusion of data
• Survey and behavioral
• “Whole respondent” view
• Surveys can be “big data”
Prediction and Market Research

  • Statisticians are the new “rock stars” (Eric
    Schmidt)
  • MR has to embrace prediction and realize
    the limits of explanation. It’s great to know
    why, better to know what’s next ?
  • Theories of respondents and consumers...

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Big Data

  • 1. Big Data Andrew Jeavons President Survey Analytics LLC
  • 2. Overview • Coming out of the data desert. • What do we mean by Big Data ? • Behavior, Big Data and Market Research. • Get a model to get ahead.....
  • 3. Data Desert • Data was scarce.. • The hunt for completes. • Sampling ? • Limited data. • Limited potential...
  • 4. The Deluge • The Data Tsunami. • What we get.. • What we don’t get...
  • 5. Forms of Data • Metadata, Paradata, Data • Big Data and Prediction of behavior. • The central dogma: • Big Data is Marxist. • Black Swans - Taleb’s cautionary tale...
  • 6. Machine Learning • What is meant by this ? • Learning and pattern recognition.
  • 7. Nerves and Genes • Neural Networks • Genetic Algorithms • “Agnostic” algorithms...
  • 8. Neural Networks Originally sought to reproduce the way the nervous system works. Had the ability to learn complex relationships with any assumptions. Can learn complex non linear relationships.
  • 11. Biological metaphor: encode problems as chromosomes. Fitness function. Evolve different mapping functions via principles of evolution. Cross over and mutation (note: mutation rates not as powerful as you may think...) MR example: used GA’s to evolve panel sample balancing - rim balancing.
  • 12. Social Media Data • We have a lot of text...that we didn’t have before... • Text has its own set of problems..unlike Gore Vidal we don’t speak in sentences. • “#itsucks phat” • Txt messages, social media messages. • Parsing and tokenizing
  • 13. Sentiment Analysis • One of the frontiers for MR and Big Data • Can we tell the sentiment behind the comment ? • Text is messy because language is messy. • Grammatical vs Stochastic
  • 14. Surveys, Market Research and Big Data • Fusion of data • Survey and behavioral • “Whole respondent” view • Surveys can be “big data”
  • 15. Prediction and Market Research • Statisticians are the new “rock stars” (Eric Schmidt) • MR has to embrace prediction and realize the limits of explanation. It’s great to know why, better to know what’s next ? • Theories of respondents and consumers...