WHILE DATA HAS BECOME THE TOP CEO TOPIC STILL MOST PROJECTS FAIL

1. BIG DATA
is DEAD
1
Marc Weimer-Hablitzel
marcwh@etventure.com Nov 2018

2. 2
Marc Weimer-Hablitzel
Principal - Data Innovations
● Corporate & Startup
experience
● 15+ years Data Science
Tech & Business

3. 3
THREE SIGNS THAT BIG DATA IS
THESE TWO JOIN FORCES
FAMOUS BEHAVIORAL
ECONOMICS SCIENTIST
JOKES ABOUT IT
Prof. Dan Ariely
COO TERADATA THINKS
O. Ratzesberger

4. 4
IS DATA THE NEW OIL?
Gather Store Get Rich

5. WHILE DATA HAS BECOME THE TOP CEO TOPIC
STILL MOST PROJECTS FAIL
5
TOP 3 REASONS
1. Bad data quality
2. Not enough data
3. No talents
understood
data potential ¹
successfully
using data ²
1) etventure: “Studie Digitale Transformation 2018”, 4-2018
2) I. Swanson: “Three Ways To Boost Your ROI In Data Science”, www.forbes.com, 6-2-2018

6. WHILE DATA HAS BECOME THE TOP CEO TOPIC
STILL MOST PROJECTS FAIL
6
TOP 3 REAL REASONS
1. Wrong focus (technology instead of business
impact)
2. Poor data strategy
3. Unattractive for talents
understood
data potential ¹
successfully
using data ²
1) etventure: “Studie Digitale Transformation 2018”, 4-2018
2) I. Swanson: “Three Ways To Boost Your ROI In Data Science”, www.forbes.com, 6-2-2018

7. 7
...IT’S ALL ABOUT THE USE CASES
Gather Store Refine
Use Cases Value
Today

8. THE MOST SUCCESSFUL COMPANIES ARE...
8
● Focus on customer needs
● Design of customer experience
● continuous improvement with
qualitative and quantitative
Feedback
CUSTOMER CENTRIC
TO THE OUTSIDE
DATA CENTRIC
TO THE INSIDE
● Focus on use case
● Design of data generation
● continuous improvement towards
measurable baseline and human
acceptance criteria
Data
Thinking

9. 9
ITERATE TO SUCCESS

10. ● premium car
manufacturer
increasing
volatility and
complexity in
special
equipment
Automotive
They all talk AI but we still do
our planing like it’s 1999
Prove that machine learning can improve the
shortcomings of current demand prediction
> 1 month
> working prototype
Head of
Planning

11. 11
Prove in one month that machine learning can improve
the shortcomings of current demand prediction
IDENTIFICATION (8 days)
● 7 Use Cases
● > 10 Mio € / p.a. business impact
● Baseline from historic data
PROOF (14 days)
● PoC: Early detection of peak demand using
external factors
● 2 Mio € / p.a. business impact
● 3 iterations / 100% client commitment

12. 12
TOOLS OF
DATA THINKING

13. 13
DATA POTENTIAL - WORKSHOP
Business
Understanding
Ideation Definition Prioritization

14. Problem (Describe the problem in one sentence)
USE CASE CANVAS
Impact (Describe the impact of the problem)
14
Baseline (What’s the baseline value? What’s the
desired target value?)
Accessibility of Data (List relevant data sets and
their accessibilty)
Contact Person (Who’s the ‘go to’ person for data?)
Use Case Description (e.g. classification) Current Solution (Describe the current solution)
Cause (Describe the cause)
Acceptance Criterion (Describe the conditions
under which an improvement is expected)
Frequency (How often does the problem occur?)
© etventure GmbH

15. 15
THE 5 PRINCIPLES OF
DATA THINKING

16. 16
IMPACT FIRST,
DATA SECOND

17. 17
START SMALL
TEST EARLY
ITERATE FAST

18. 18
DESIGN ON YOUR
NEEDS, NOT ON
OUR HAVES

19. 19
BETTER
FAKE DATA,
THAN
NO DATA

20. 20
BE SOLUTION
AGNOSTIC

21. KEY TAKEAWAYS
21
1. Data value can be found everywhere
2. A structured approach helps!
3. Stop wasting your time on BIG Data

22. 22
Marc Weimer-Hablitzel
Principal - Data Innovations
Mobil: +49 151 57539361
marcwh@etventure.com
Ritterstr 24-27
10969 Berlin
www.etventure.com
KONTAKT
THANK YOU!
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