Everyone is talking about Big Data, Deep Learning and Artificial Intelligence. But the reality in some companies looks different, especially when developing new products: (the relevant) data is missing. Without predictive models and recommendation systems cannot be trained and the value is consequently low. This so called cold-start problem is especially concerning startups, since without own data treasure the companies are missing a defendable unique value proposition. Successful startups solve this problem with the help of „Data Traps“ and develop products with „Data Network Effects“. What exactly stands behind these terms and how companies design their own successful and data-driven products, will be demonstrated by Martin Szugat based on samples from his occupation as Data Strategy Consultant.

1. Using Data Strategy Design to
Build Data-Driven Products
Martin Szugat at Product Tank Munich on 26.06.2017
datentreiber.de

2. Driving your business forward.
Consulting, workshops and seminars on data strategy design.

3. 1996-2008
Software developer, consultant,
trainer and author
Study & research of bioinformatics
(Data Science)
2001-2008
Managing director & shareholder of
SnipClip GmbH
2008-2013
Program director of Predictive
Analytics World conferences in
Germany
2014-dato
Managing director & owner of
Datentreiber GmbH
2014-dato
Founder and director of
Datengipfel academy
2017-dato

6. Drivers for AI spring:
 Improved methods & open source tools
 Processing power (i.e. GPU) & memory
 Availability and quality of data

7. Breakthroughs in AI are mainly driven by data.
Source: http://www.kdnuggets.com/2016/05/datasets-over-algorithms.html

8. How machine programs work.
Data Algorithm
Infor-
mation

9. How machine learning works.
Past
Data
Training
Algorithm
Trained
Model
Future
Data
Trained
Model
Infor
mation

10. http://www.r2d3.u
s/visual-intro-
to-machine-
learning-part-1/

11. What makes data valuable?
Quantity (i.e instances)
Density (i.e. features)
Quality
 Completeness
 Currentness
 Correctness
 Reliableness
 Representativity
 …

12. More data means better predictions (in general).
Source: https://medium.com/mmc-writes/the-fourth-industrial-revolution-a-primer-on-artificial-intelligence-ai-ff5e7fffcae1

14. „Data is the new oil.“
So what?

15. The startup founder ...
… looking for data.

16. “Accessing and owning a large, domain-specific dataset to build high
accuracy models can be the single hardest problem that founders need to
solve in the beginning.”
Source: https://medium.com/@muellerfreitag/10-data-acquisition-strategies-for-startups-47166580ee48

17. “Owning a large, domain-specific dataset can therefore become a significant
source of competitive advantage […]”
Source: https://medium.com/@muellerfreitag/10-data-acquisition-strategies-for-startups-47166580ee48

18. The Chief Data Officer …
… is drowning in data ...

19. … but his company is …
… thirsty for information.

21. Most data lakes are …
… data swamps.

23. Data exploitation

24. Data refinement

25. Data utilization

26. Big Data Ansatz
Big data approach:
finding the needle in the haystack …
… by accumulating more hay.

27. The alternative:
data strategy design

28. Data utilization

29. Data refinement

30. Data exploitation

31. Data-driven business is lead by a value-oriented strategy.
Exploitation Refinement Utilization

32. What‘s a „data product“?
Data Algorithm
Infor-
mation
„Data Product“
[2]:
“A data product is a
computer application
that takes data
inputs and generates
outputs, feeding
them back into the
environment.”
„Data Product“
[1]:
“Customers buy the
data 'product' once,
and continue to use
it as is.”
„Data Product“
[3]:
“A data product is
digital information
that can be
purchased.”

33. What‘s a data-driven product?
Data Algorithm
Infor-
mation
It‘s valueable.It‘s unique.
It‘s an unique value proposition. (model)
It‘s a driven-driven product.
It‘s customer needs.

34. Predicting blockbusters.
Source: http://dataconomy.com/using-wikipedia-activity-data-forecast-movie-success/

35. Data Strategy Design Kit by Datentreiber

36. Data-driven Business Model Generation
https://www.amazon.com/Business-Model-Generation-Visionaries-Challengers/dp/0470876417/ref=sr_1_1

37. Cinema
Operator
Prediction of
percentage of
seats sold
License cost
reduction
Website
SaaS
Email
Predictive
Modeling
Marketing &
Sales
Data
Scientists
M&S
Manager
HR
Software
Marketing
Budget
Monthly Pay per
Cinema Hall

38. ???
Prediction of
percentage of
seats sold

39. Prediction of
percentage of
seats sold
Predictive
Model: Total
Revenue
Predictive
Model: Seats
Sold
Wikipedia
Movie Data Movie Total
Revenue
Figures
Seats Sold
per Movie

40. Wikipedia
Movie Data
Movie Total
Revenue
Figures
Seats Sold
per Movie
Movie Name
Mapping
Table
???

41. Data Traps

42. Wikipedia
Movie Data
Movie Total
Revenue
Figures
Seats Sold per
Movie
Movie Name
Mapping Table
Prediction
Game

43. Prediction of
percentage of
seats sold
Predictive
Model: Total
Revenue
Predictive
Model: Seats
Sold
Wikipedia
Movie Data Movie Total
Revenue
Figures
Seats Sold per
Movie
Movie Name
Mapping Table
Prediction
Game
Data
WarehouseETL Tool Predictive
Modeling Tool
Website
Development
Tools
Data
Engineers
Data
Scientists
Web
Developers
Seller: Movie
Total Revenue
Figures

44. Cinema
Operator
Prediction of
percentage of
seats sold
License cost
reduction
Website
SaaS
Email
Predictive
Modeling
Marketing &
Sales
Seats Sold
per Movie
HR
Software
Marketing
Budget
Monthly Pay per
Cinema Hall
Prediction
Game
Movie Name
Mapping
Table
Prediction
Game
Seller: Movie
Total Revenue
Figures

45. Data Network Effects

46. More Data
(Seats
Sold)
Smarter
Algorithms
Better
Product
More
Clients

47. Get started.
Go to www.datenstrategiedesign.de.

48. May the data be with you.

49. datentreiber.deWir treiben Ihr Unternehmen voran.
Martin Szugat
Managing director
Telefon: +49 [0]881 12 88 46 53
E-Mail: ms@datentreiber.de
Web: www.datentreiber.de
Blog: www.datenstrategiedesign.de