1. The document discusses how data science can provide value to business managers by focusing on creating value from data without requiring coding skills. 2. It explains that increased data availability and better algorithms, like deep learning, have enabled major advances in data science. 3. While traditionally only data-centric industries benefited from data science, the document argues that all companies now have untapped potential to innovate and gain competitive advantages through the use of data science.

1. 1
Data Science for Business Managers
Akın Osman Kazakçı
MINES ParisTech
Balazs Kégl
Ecole Polytechnique, CNRS

2. data science from
business perspective
no coding

3. data science from
business perspective
focus on creating value from data
=

4. 4
Plan
1. A paradigm change: the era of data-driven
2. Data science ecosystem
3. Some recent results and applications
4. The untapped potential of data for engineering
companies

5. 5

6. Kasparov vs. Deep Blue,
1997
Lee Seedol vs. AlphaGo,
2016

7. Complex task
Decision-making
and prediction
Large computational
resources
Complex task
Decision-making
and prediction
Large computational
resources
Top-down,
hard-coded
knowledge
Bottom-up,
learned
knowledge
+ data

8. 8
Why now?
1) More data
2) Better algorithms

9. 9
More data

10. 2,767 copies of the full text of
the Encyclopedia Britannica
=
1 terabyte
LHCb proton collider at CERN
(the smallest of the 4 detectors)
=
1 terabyte per second

11. Facebook vs LHCb/CERN
Image courtesy of Vladimir Gligorov, CERN

12. “more data” isn’t always “more value”
but data availability certainly gave a great push
to data science

13. 13
Better algorithms

14. 14
Deep learning: a significant breakthrough
“The model has 1 billion connections, the dataset
has 10 million 200x200 pixel images downloaded
from the Internet. We train this network […]with
1,000 machines (16,000 cores) for three days.”

15. 15
Deep learning: a significant breakthrough
Deep neural nets enable the
automated extraction of useful
features - beyond any level we
previously achieved
before after
We used to hand-craft
features that should be
recognised to identify
objects

16. 16

17. you don't always need “better algorithms”
but algorithmic advances certainly gave a great
push to data science

18. 1. A paradigm change: the era of data-driven
2. Data science ecosystem
3. Some recent results and applications
4. The untapped potential of data for engineering
companies
18
Plan

19. Various types of players
IndustryResearchStartups

21. 1. A paradigm change: the era of data-driven
2. Data science ecosystem
3. Some recent results and applications
4. The untapped potential of data for engineering
companies
21
Plan

22. 22

23. 23

24. 24

25. 25

26. 26

27. 1. A paradigm change: the era of data-driven
2. Data science ecosystem
3. Some recent results and applications
4. The untapped potential of data for
engineering companies
27
Plan

28. 28
Are the only companies
that can
benefit from
data science?

29. data-centric
industry
traditional
industry
The Divide
<4%
GDP
>96%
GDP

30. Discussion
• How the traditional industry will benefit from data?

32. INNOVATING THROUGH DATA
32
« You might find that the data is
more valuable than the shoe”
Van Alstyne,
Boston University

33. A %1 change in fuel burn for an airline
is worth hundreds of millions of
dollars.
The power of one percent:
« Every big company will
become a software company »
Jeff Immelt, GE CEO
Industry - A growing focus on data-transition
Massive impetus by GE
to Internet of Things

34. OPPORTUNITY OR RISK?
34

35. All companies will need to develop
organizational APIs for Big Data
35
INNOVATING THROUGH DATA: OUR VISION
API
Organisation
Department 1
Department 2
Department 3
Data science
ecosystem
Buffer Zone
Intermediation

36. INNOVATING THROUGH DATA
36
Data value
Data analytics
Exploration of value
• design theory
• data-based prospection
• innovation workshops
Problem formulation
Problem solving
• specialized teams
• RAMPs
• data challenges