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

1. Big Data Use-Cases
across industries
Georg 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 Analytics
Web Clickstream Sessionization Social Network Analysis
Media Clickstream Sessionization Content Optimization
elco Mediation Network Analytics
Retail 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?