Event: Dataconomy Düsseldorf, 19.10.2017 Speaker: Dr. Christoph Tempich Weitere Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/ Tech-Artikel im inovex-Blog: https://www.inovex.de/blog/

1. Data product discovery
The product perspective on digital
transformation
Dr. Christoph Tempich
Chief Data Economist
Köln, www.inovex.de 19.10.2017
#Datenprodukte #dataproducts @ctempich

2. 2Source: http://fortune.com/2012/07/30/amazons-recommendation-secret/
What are Amazon recommendations worth?
„The company reported a
29% sales increase to
$12.83 billion [...]
Amazon has integrated
recommendations into
nearly every part of the
purchasing process from
product discovery to
checkout.“

3. 3G Linden, B Smith, J York: Amazon. com recommendations: Item-to-item collaborative filtering - IEEE Int. Comp., 2003
How did they calculate them at the beginning?
2003 2017
Item-to-item
collaborative filtering
Hybrid recommenders
Deep learning
Random

4. 4Quelle: inovex Case Study Recommendations bei mobile.de
Recommendations @ inovex
Germany’s biggest vehicle market place
User Benefits
› engagement
› inspiration
› relevance
Business Benefits
› high click-through-rate
› small exit- & bounce-rates

5. 6
A recommendation system is
one type of data product

6. 7
Data products: types
Data as a Service
Data-enhanced
Products
Data as Insights
Type 1 Type 2 Type 3
› Autonomous driving
› Recommendations
› Weather data › Marketing planning
#Datenprodukte @ctempich @thomasleiterman

7. 8#Datenprodukte @ctempich @thomasleiterman
Growing market for data products

8. 9
How to discover
a data product?

9. 10Bildnachweis: https://www.colourbox.de
Value Chain Analyse
Start with the customer!
Deciding to run
Selecting a
runner
Workout plan
Course
selection
Run Recover
Customer Jobs

10. 11
Variance

11. 12Von Watzmann - Eigenes Werk, Gemeinfrei, https://commons.wikimedia.org/w/index.php?curid=8391805
Variance = Uncertainty
A measure for the value of information

12. 13
Stakeholder uncertainty
What is the problem and can data solve it?
Stakeholder:
Person who runs
• Increased
stamina
• Weight mgmt.
• Increased
performance
• Fun
• Continuous
improvement
• Motivating • Supports
performance
gains
• Supports
performance
gains
• Heart attack • Broken joints • No improvement
• Too exhausting
• Demotivating • Demotivating • No training success
Customer Jobs
Deciding to run
Selecting a
runner
Workout plan
Course
selection
Run Recover
Best
case
Worst
case

13. 14
Value Proposition

14. 15Quelle: Laura Dorfer: Datenzentrische Geschäftsmodelle als neuer Geschäftsmodelltypus …, 2016.
Value proposition and readiness to pay
Data provider and consumer may differ!
User Buyer
Social interaction
Entertainment
Curiosity
Decision support
Transparency
Information
Readiness to payValue proposition

15. 16
Looking into the future
Example: Training support
• Social interaction
• Which course have I taken?
• Information
• How fast am I running currently?
• Decision
• When should I run again?
BasedontransactiondataBasedon
masterdata
Value Proposition Service
• Information
• Shoes
Future
Present
Past
Data item
Information about

16. 17
Data Value Chain &
Feedback Loop

17. 18Quelle: https://www.inovex.de/blog/feedback-loop/
The Feedback Loop
A must-have for good training data …
… and to create a USP

18. 19
Recognition of house numbers for
Google Street View,
Feedback loop: Which house number is this?
by means of involving users in
recognizing the correct number.
Bsp.: https://www.google.com/recaptcha/intro/android.html
Quelle: http://stadt-bremerhaven.de/google-streetview-und-
captchas-beide-profitieren-voneinander/

19. 20Quelle: Data Value Matrix
Data Value Matrix
Improves
same
service
Improves
other
service
Machine learning
Aggregation
Tesla fleet learning
Improves autonomous driving Mobile.de User Profile
Show appropriate ads
Google reCAPTCHA
Improves Google Street View
STRAVA
Training course highlighting
nike plus
Music selection
Mobile.de Recommendation
Recommend similar cars

20. 21
Takeaway

21. 1. Start with the customer!
2. What is the problem and can data solve it?
3. Test your hypothesis using the simplest algorithm
4. Data provider and consumer often differ
5. Close the feedback loop
22#Datenprodukte #dataproducts @ctempich

22. Questions?
inovex GmbH
Dr. Christoph Tempich
Chief Data Economist
@ctempich
www.datenprodukte.de
blog.inovex.de
www.inovex.de