The document discusses the importance of big data use-cases across various industries and emphasizes the need for businesses to leverage data for solving immediate problems and driving innovation. It outlines the maturity levels of big data applications, from improving existing models to creating new, data-driven business models. Additionally, it highlights the challenges of using external data sources and legal considerations regarding data privacy in Europe.

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?