• Save
Big Data Use-Cases across industries (Georg Polzer, Teralytics)
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

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

on

  • 2,989 views

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

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

Statistics

Views

Total Views
2,989
Views on SlideShare
2,988
Embed Views
1

Actions

Likes
4
Downloads
0
Comments
0

1 Embed 1

http://www.docseek.net 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

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

  • 1. Big Data Use-Casesacross industriesGeorg Polzer+41 79 308 97 23 – georg.polzer@teralytics.ch
  • 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. 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. 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. 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. 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. 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. 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. 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. What about you?