Big Data
 

Big Data

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Big Data Big Data Presentation Transcript

  • Big Data Andrew Jeavons PresidentSurvey 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 NetworksOriginally sought to reproduce the waythe nervous system works.Had the ability to learn complexrelationships with any assumptions.Can learn complex non linearrelationships.
  • 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 asyou 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...