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Big Data Use-Cases across industries (Georg Polzer, Teralytics)


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This talk was held at the third meeting of the Swiss Big Data User Group on September 17 at ETH Zürich.

Published in: Technology, Business

Big Data Use-Cases across industries (Georg Polzer, Teralytics)

  1. 1. Big Data Use-Casesacross industriesGeorg Polzer+41 79 308 97 23 –
  2. 2. Why Big Data Use-Cases? Why not? ‣  Today: Need to sell Big Data to Business ‣  What can it do for us? , answer with use-case ‣  Need to calculate business case ‣  Tomorrow: Data first, Business case later ‣  Requires data infrastructure (built today)
  3. 3. Pain vs. Lust ‣  Use data to solve immediate business pain ‣  E.g. Manufacturing line inefficient, return-rate high, computation takes too much time ‣  Explorative analysis for data-driven innovation ‣  You don t know what you will find ‣  Drivers: Curiosity and fun
  4. 4. Iterative vs. Disruptive ‣  Improve search results vs. self-driving car ‣  Try 5 different products simultaneously, collect data rigorously, fail fast, double down on success ‣  Long tail, fail in order to succeed (mindset!) ‣  Natural selection, try to push convergence rate ‣  Data over experience (reality changes fast) ‣  A/B Testing
  5. 5. Maturity levels of Big Data ‣  Level 1: Empower existing business models ‣  Understand customer, better service, better products ‣  Level 2: Enable data-driven, disruptive innovation ‣  Understand past better, start predicting future ‣  Level 3: Create data-driven business models ‣  Bank sells data about customer-group buying habits to retailers, advertisers ‣  Mobile network operator predicts traffic jams
  6. 6. Recap: Big Data Use-Cases Industry Data Processing Advanced AnalyticsWeb Clickstream Sessionization Social Network AnalysisMedia Clickstream Sessionization Content Optimization elco Mediation Network AnalyticsRetail Data Factory Loyalty & Promo inancial Trade Reconciliation Fraud Analysis ederal SIGINT Entity Analysis ioinformatics Genome Mapping Sequence Analysis
  7. 7. Recap: Use-Case Patterns ‣  Data Processing ‣  Data enrichment, data transformation ‣  Part of ETL Pipeline ‣  Complex Analysis ‣  Network Analysis (who interacts with whom, flow of goods) ‣  Correlation, Classification, Clustering
  8. 8. Big Data Use-Cases Checklist ‣  Thinking hard does not bring solution (Intelligence vs. Statistics) ‣  Large amounts of data available for analysis ‣  Think out of the box: where do we get data from outside the company to fill data gap? ‣  Difficult question ‣  How much ice-cream did we sell? vs How much ice-cream will we sell next week?
  9. 9. Caveats ‣  Targeted advertisement by browser Cookies threatened by EU legislation ‣  Judging reliability of external data sources in certain use-cases crucial (e.g. reputational risk assessments) ‣  Data privacy barriers very high in Europe
  10. 10. What about you?