The document discusses the potential of big data in creating disruptive business models, emphasizing the need for trust and partnerships in leveraging data. It explores various data sources and their applications in mobility, predictive maintenance, and product development, alongside methodologies like design thinking and rapid prototyping. The importance of complying with data privacy regulations while finding creative business models around data sharing is also highlighted.

1. © Catalysts GmbH
Big Data Gold Mining
Dominik Rockenschaub
Scrum Master, Agile & IoT Cluster Project Lead
dominik.rockenschaub@catalysts.cc

2. 2
⇒ It’s more likely that Trust &
Partnership (sharing ideas)
leads to better and disruptive
business ideas
“The electric light did not come from the
continuous improvement of candles.”
-Oren Harari

3. Big Data
Gold Mining
How?
Value?
Technical Solutions?
Costs?
Competition (Dis-)Advantage?
Investors | Venture Capital | Public Sponsorships (FFG, …)
Creativity Techniques (Design Thinking, Brainstorming, Real
World Problem Analysis, ….)
IoT Cluster | Machine Learning | Big Data Analysis | Data
Science | Artificial Intelligence | Predictor | Classifier
Trust + Partnership
Statistics never lie (!) - It’s all about
numbers/patterns/similarities…..

4. Where do I find “data”?
What is (valuable) “data”?
How can one “increase value” of data?
Is there a standardized data format?
….
4
Data?
⇒ Join the
Rapid Prototyping Train

5. 5
Data Value Chain
Identify
Data
Offer
Data
Sell
Data
(optional)
Mashup
Data
Data
Science
Data
Privacy
Use
Data
Use creative
techniques to identify
data
Think of Business
Model
Check regulations
and law before
offering data (EU
privacy law,
Company Terms of
Service, …)
Defined Interface
Defined Update
Interval
Marketing & Sales
required
chicken-and-egg
problem between
data provider and
data consumer
Combine data
products
Data Science and
Algorithm Experts
required
Increase value of
data
Disruptive Business
Models
Don’t fear Data Sharing!
It’s key for innovation and successful Joint Ventures.

6. 6
Mobility Data Products 1/2
Data Source Data Artifacts Value/Product
ÖBB Schedule
Train departure and arrival
time, train delays
Intermodal Travel Route Planning
Linz Bus Schedule
Live GPS position of public
transport
Qando mobile App
Car Accident Distribution
(Statistik Austria 2016)
GPS position, accident
frequency
Safety Products (Collision Detection, Self Driving Cars)
Meinewege.at (Floating Data of
School Children)
GPS position (live, planned) Safety Products (Collision Detection, Self Driving Cars)
Engine Manufacturing Company
“Health status of engine”,
engine speed, ...
Predictive Maintenance
Hyperloop Simulator
Flights, Cars, Boats, Taxis,
Trucks, Trains
Intermodal Travel Route Planning
Specific
data/product
More
visionary
data/product

7. 7
More
visionary
data/product
Even More
visionary
data/product
Mobility Data Products 2/2
Data Source Data Artifacts Value/Product
Car Fleet Data (VW/POI)
GPS Routes
Driving Behaviour
(acceleration, breaking, …)
Driving Logbook, Cross- & Upselling for Car Dealer, OBU products (DiBox, Automatic,
…), Predicted Life: “we recommend your dream-car even before you know”
“Routenexakte
Wettervorhersage”
Wiper frequency,
GPS position, ABS/ESP
frequency, weather data
Self Driving Cars
Safe Trip App for Car Driver
Crash Avoidance for Insurance Companies
“Carlytics”
(CROSSNG, Carfin,...)
“Health state” of car parts
Predictive Maintenance (“Repair today and save 100€”)
On-Time Parts Production and On-Time Parts Stock Management
“PremiumCar”
GPS Route, calendar
appointments, user
identification
Enable Premium Car Features (additional speed, hourse power, ….)
“I don’t always need 300hp, but when I do, I enjoy it!”
Electricity Power Supplier
e-car power consumption,
charging frequency
Life-Long Energy Products at fixed product price (sell car with higher price, includes
lifetime energy demand)

8. Process
for (Data) Product Development
Idea!
and
/ or
Less costly
Expensive
High risk
Low risk
Identified Problem
Expected Efficiency Improvement
Hypothesis defined

9. Idea!
and
/ or
Less costly
Expensive
High risk
Low risk
AlgorithmSpecify Solution
Process
for (Data) Product Development

10. Idea!
and
/ or
Less costly
Expensive
High risk
Low risk
How? Data missing!
=> Test Driven Development required!
Specify Solution
Testing
Process
for (Data) Product Development

11. Idea!
and
/ or
Less costly
Expensive
High risk
Low risk
Catalysts Examples:
Hyperloop
VW/POI
Steyr Motors
ZAMG
4.0 Companies
And many more….
Specify Solution
Testing
With DMA Data With Simulator censored
Process
for (Data) Product Development

12. Idea!
and
/ or
Less costly
Expensive
High risk
Low risk
Specify Solution
Testing
With DMA Data With Simulator
Validate Hypothesis
Rapid Prototyping Train
Innovation
Design Thinking
Brainstorming
Lean Canvas
….
⇒ Innovation happens right here ;-)
Process
for (Data) Product Development

13. Idea!
Specify Solution
Testing
With DMA Data
Validate Hypothesis
Real World Market
Product Development
GoLive
With Simulatorand
/ or
Less costly
Expensive
High risk
Low risk
Process
for (Data) Product Development

14. Building Products based on Data
Floating Car
Data
ESA Satellite
Data
ZAMG Weather
Data
€
Movement
Data
Engine Data
ÖBB Traffic
Data
Your Data
Routenexakte
Wettervorhersage
EODC
Hyperloop
Travel Portal
Steyr Motors
Predictive Maintenance
Your ProductYour Data Mashup May we help you? € €
DATA MARKET
PLATFORM

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Start now:
Big Data Gold Mining
with
Rapid Prototyping Train
Image Source: http://honister.com/inside-the-mine/