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BIG DATA
is DEAD
1
Marc Weimer-Hablitzel
marcwh@etventure.com Nov 2018
2
Marc Weimer-Hablitzel
Principal - Data Innovations
● Corporate & Startup
experience
● 15+ years Data Science
Tech & Business
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
IS DATA THE NEW OIL?
Gather Store Get Rich
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
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
...IT’S ALL ABOUT THE USE CASES
Gather Store Refine
Use Cases Value
Today
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
ITERATE TO SUCCESS
● 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
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
TOOLS OF
DATA THINKING
13
DATA POTENTIAL - WORKSHOP
Business
Understanding
Ideation Definition Prioritization
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
THE 5 PRINCIPLES OF
DATA THINKING
16
IMPACT FIRST,
DATA SECOND
17
START SMALL
TEST EARLY
ITERATE FAST
18
DESIGN ON YOUR
NEEDS, NOT ON
OUR HAVES
19
BETTER
FAKE DATA,
THAN
NO DATA
20
BE SOLUTION
AGNOSTIC
KEY TAKEAWAYS
21
1. Data value can be found everywhere
2. A structured approach helps!
3. Stop wasting your time on BIG Data
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!
Want a copy of this presentation?:
Post your feedback on LinkedIn and add
linkedin.com/in/marcwh #DataThinking
www.etventure.com
etventure.de © All Rights Reserved
changing the game
23

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BIG DATA is DEAD | Marc Weimer-Hablitzel, Etventure | DN18

  • 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
  • 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
  • 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
  • 18. 18 DESIGN ON YOUR NEEDS, NOT ON OUR HAVES
  • 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! Want a copy of this presentation?: Post your feedback on LinkedIn and add linkedin.com/in/marcwh #DataThinking
  • 23. www.etventure.com etventure.de © All Rights Reserved changing the game 23