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Digital Marketing Class: Big data

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Slides for Big Data, covering Hadoop, Algorithms and Cloud computing, more.

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Digital Marketing Class: Big data

  1. 1. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Scope • Data produced every 2 days = all data prior to 2003 … Google’s Schmidt • Volume of Big Data, WW doubles every 1.2yrs • 90% of data was produced in last 2 years • 2012, everyday 2.5 exabytes of data created • Increased digitization of communications & transactions: mobile, IoTs Digital Marketing alexbr.brown@gmail.com University of Delaware
  2. 2. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Generally • Data versus actionable insights (Information) – Orgs typically use small portion of data • Good for increasing engagement with current customers • Increases lock-in • Helps identify new products Digital Marketing alexbr.brown@gmail.com University of Delaware
  3. 3. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Generally • Useful when past is good predictor of future • Provides real time insights (dashboards) • Is it a revolution? • Very buzzy, re: VCs etc. • Example: Google Flu Trends – Fall 2008 Digital Marketing alexbr.brown@gmail.com University of Delaware
  4. 4. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Data Sources • Internet of Things (Sensors) – Ex: Parking meters, Smart houses • Machine to Machine networks: 250m 2014 – APIs versus Open Standards • Data exhaust (ex. Social media) • Clickstream data Digital Marketing alexbr.brown@gmail.com University of Delaware
  5. 5. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Data Sources • Location data – In-store (iBeacon, sensors) and geo fencing • Loyalty program data (Target) • Transactional data (Krogers) • Structured versus Unstructured data • Primary to Secondary data (unimagined use) Digital Marketing alexbr.brown@gmail.com University of Delaware
  6. 6. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Three Aspects • Ingestion & Storage (cloud) – cost • Analytics – value • Visualization – communication Digital Marketing alexbr.brown@gmail.com University of Delaware
  7. 7. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Analytics • Continuous – Real-time reporting: Dashboards • Predictive – Netflix – Google and house sales • Sentiment – Sales down, twitter chatter Digital Marketing alexbr.brown@gmail.com University of Delaware
  8. 8. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Defining Big Data: 3 Vs • Volume • Velocity • Variety • Veracity (AP Hack “Obama hurt”) Digital Marketing alexbr.brown@gmail.com University of Delaware
  9. 9. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Technology: Hadoop MapReduce • Google Origins: MapReduce: indexing web, examining user behavior to improve algorithm • Yahoo! & Hadoop Open Source project • Cornerstone technology for Big Data – For now ? Google’s Cloud Dataflow • Makes Big Data more manageable Digital Marketing alexbr.brown@gmail.com University of Delaware
  10. 10. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Technology: Hadoop MapReduce • Cheap, scalable • Fast • Store, then figure out • Open Source – Cloudera – Hortonworks Digital Marketing alexbr.brown@gmail.com University of Delaware
  11. 11. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Cloud Computing • Business and consumer markets • Increasing volumes of data to manage & interpret • Moore’s law Digital Marketing alexbr.brown@gmail.com University of Delaware
  12. 12. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Cloud Computing • Virtualization • Variable cost • Scalability and agility Digital Marketing alexbr.brown@gmail.com University of Delaware
  13. 13. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Cloud Computing • Run apps from anywhere, from any device • Easier to upgrade apps • Makes IT more accessible • Software as a Service (SaaS) – Ex: Salesforce.com Digital Marketing alexbr.brown@gmail.com University of Delaware
  14. 14. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Cloud Computing • Very buzzy re: VCs • Key players: Google, Amazon, Windows Azure, Oracle, IBM, Rackspace, Salesforce.com • Privacy issues • Data ownership and security? Digital Marketing alexbr.brown@gmail.com University of Delaware
  15. 15. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Changes in Analysis • Data starved to data abundance – Too expensive, and not enough of it • Samples and statistical significance • Hypothesis driven versus Data driven: end of theory? • Correlations to design algorithms Digital Marketing alexbr.brown@gmail.com University of Delaware
  16. 16. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Changes in Analysis • Patterns in the data: relationships • What versus Why? – Amazon recommendations (risks ?) • Causation versus Correlation (good enough) • Limitation: we know what, but not why Digital Marketing alexbr.brown@gmail.com University of Delaware
  17. 17. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Criticism • Bias in data sources – Ex: Twitter and election results • Importance of human intuition • Filter bubble Digital Marketing alexbr.brown@gmail.com University of Delaware
  18. 18. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Algorithms • A repeating set of rules, series of instructions • Designed from the data, or purchased • Predicts future, based on the past (Big Data) • Input data unique, passes algorithm, output unique (creates probability) • Replaces human intuition: or mix of both Digital Marketing alexbr.brown@gmail.com University of Delaware
  19. 19. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Machine Learning & Algorithms • Increasing returns on data • Designed into algorithm (real-time) • Examples – Google (search) – Amazon (human editors versus algorithm) what, not why – Facebook (news feed) – Netflix (recommendations) – Zynga (data from game play) Digital Marketing alexbr.brown@gmail.com University of Delaware
  20. 20. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Old World, New World • Old World: Analogue, same content by vehicle, medium, retail store • New World: Digital, unique content driven by algorithms to increase engagement and lock-in Digital Marketing alexbr.brown@gmail.com University of Delaware
  21. 21. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Old World, New World • New World Examples: – Netflix – Amazon – In store retail experience w/ mobile app and iBeacon technology (smart retail) Macy’s ? – Target, pregnant teen Digital Marketing alexbr.brown@gmail.com University of Delaware
  22. 22. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Unintended Consequence: Filter Bubble effect • Human curated content versus serendipity • Editors versus wisdom of the crowd • Personal universe of online information • Past clickstreams determine future choice: information determinism – Netflix, Amazon etc. • Echo chamber effect Digital Marketing alexbr.brown@gmail.com University of Delaware
  23. 23. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Unintended Consequence: Filter Bubble effect • Echo chamber effect • What we want to see, versus what we should see – Amazon / Netflix recommendations (what, not why) – Good enough recs. Versus risks ? • Personalized: Google, Facebook etc. • Personalization drives ad revenue • Wikipedia is a standout Digital Marketing alexbr.brown@gmail.com University of Delaware
  24. 24. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Privacy • Law (and keeping up) • Terms of Service (bargain) Binary choice • Terms of Service: changes (for business model) • Scope of reach: Facebook / Google – EU: Right to be Forgotten ? – EU: Privacy rules change, Google • Tension, privacy versus personalization • Opt in, versus Opt out: by default – Org donations; Facebook Nearby Digital Marketing alexbr.brown@gmail.com University of Delaware
  25. 25. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Privacy • Knowledge of data collection: invisible • Knowledge of data use (secondary: sometimes unimagined) • Creep factor (how did they know?) – Ad retargeting – Snapchat ? Digital Marketing alexbr.brown@gmail.com University of Delaware
  26. 26. #buad477 follow @alexbrownracing http://www.udel.edu/alex/classes Big Data Privacy • Combining data can de-anonymize • Data brokers: reselling – Acxiom • Hacking, data theft (Target, Home Depot, Kmart, etc.) Digital Marketing alexbr.brown@gmail.com University of Delaware

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