Martin Szugat presented on designing a data strategy using an open source toolbox and method for data thinking. He discussed why a data strategy is important given that around 85% of data and analytics projects fail due to a lack of business impact, user acceptance, or having the right data. A data strategy involves understanding the business objectives, accessible data, potential use cases, and roadmap for data-driven business cases. It is designed through an interdisciplinary team that understands the business, users, and data. The presentation provided an overview of the key components and considerations for developing a successful data strategy.

1. Martin Szugat @ Data Brain Meetup on 10/08/2020
Data Strategy Design:
An Open Source Toolbox &
Method for Data Thinking

2. 1996-2008
Consultant, Author
and Software Developer
Study and Research of
Bioinformatics (Data Science)
2001-2008
Managing Director & Shareholder of
SnipClip GmbH
2008-2013
Program Director of the Predictive
Analytics World & Deep Learning World
Conferences
2014-dato
Managing Director & Founder of
Datentreiber GmbH
2014-dato
Chief Data Officer & Shareholder of
42AI GmbH
2018-dato
Martin Szugat

3. Why think about data …

4. … before analyzing data?

5. Because ~85% of data analytics & AI projects fail!
https://designingforanalytics.com/resources/failure-rates-for-analytics-bi-iot-and-big-data-projects-85-yikes/

6. Reason #1: no business impact.

7. Reason #2: no user acceptance.

8. Reason #3: no (right) data.

9. Viability
Feasibility
Why do we need a data strategy?
Desirability
Successful data innovations

10. Company
Objectives
Accessible Data
What is a data strategy?
Analytics
Use Cases
Data products with
a business case

11. Collection of Analytics
Use Cases
(Problem, Solution, Benefit)
Roadmap for Data-Driven
Business Cases
(Costs, Risks, Profits)
Assumptions
(Analytical, Economical, …)
Learnings
(Data, Business, User, …)
Business Value
(Information → Decision → Action →
Impact → Objective)
Data Sources
(Collection, Acquisition, …)
Data
Thinking
Data
Mining
Data Engineering
Data
Management
Data Strategy
Data Prototypes
Data Product
Data SourcesData AssetsData Product
Innovation Cycle

12. Data
Management
Data Engineering
Data
Mining
Data
Thinking
Data, Model &
Product
Management
Data, Software &
UI Engineering
Data
Mining & User
Experiments
Data & Design
Thinking
2. User Under-
standing
(Desirability)
3. Data Under-
standing
(Feasibility)
1. Business
Under-
standing
(Viability)
2. Modelling &
Visualization
3. Evaluation
1. Data
Exploration &
Preparation
3. Learn
1. Build
2. Measure
3. Monitor
1. Deploy
2. Orchestrate
CRISP-DM
Design
Thinking
Proof of Concept (PoC)?
Proof of Value (PoV)?
Lean
Develop-
ment
DataOps

13. Understand
Business
Understand Data
How do we design a data strategy?
Understand Users
Data Strategy Design for Data
Thinking

14. Business
Unit
IT
Unit
Who do we need for data thinking?
Data Analytics
Unit
Interdisciplinary team

15. What tools do we need?

16. Our heads.

17. And pen & paper.

18. 1st Day: Overview of Actual
Status & Outlook on Target
Status.
2nd Day: In-depth Look &
Check into the Details.

20. … and many more Data Thinkers.

21. Get started.
• Designkit: http://dtbr.de/designkit
• LinkedIn Group: http://dtbr.de/data-thinker
• Video training: http://dtbr.de/ddm
• Interactive trainings: http://dtbr.de/training
• News: http://dtbr.de/twitter
• Presentations: http://dtbr.de/slideshare
• More: https://www.datentreiber.de

22. datentreiber.deWir treiben Ihr Unternehmen voran.
Web: www.datentreiber.de
Blog: www.datentreiber.de/blog/
Martin Szugat
Geschäftsführer
Telefon: +49 [0]881 12 88 46 53
Email: ms@datentreiber.de

23. Data Strategy Workshop
Example
Martijn Bakker

24. Martijn: My Life Is All About Transformation
Personal
Transformation
• 26 year’s Yoga
• 23 year’s Tai Chi
• Yoga teacher:
• 7 Courses in India
• Tai Chi teacher
• Life long student
• Traditional, rational
Projects
• 22 year’s projects
• 14 year’s project/
program manager
• 11 year’s PMO
• 1½ year’s audit lead
• 1+ year Agile rollout
• Growth Mindset
Digital
Transformation
• Past 2½ years:
• PM & Consultancy
• Data Strategy
Design Method:
• Certified Consultant
• Hosted 3 workshops
• Hosted 2 trainings
• Design Thinking
24LinkedIn profile

25. Introduction
• Fictitious example
• 2 days in 45 minutes
• Iterative versus sequential

26. Gearbox Key Components
• Shafts
• Gear wheels
• Clutch
• Housing
• Gear lever
• Electronics
• Software
• Shift paddles
Manual
gearbox
Automatic
gearbox

27. Car, truck
manufac-
turers
Manual
gearbox
(Spare)
parts
Wholesale
distribu-
tors
Fixed
assets
Materials,
inventory,
freight
SG&A,
overhead
Labor &
burden
Consumer
Profes-
sional
driver
Garage
Gearbox
manufac-
turer
Martijn
Bakker
08-Oct-
2020
Automatic
gearbox,
DCT, CVT
Electric
vehicle
gearbox
Conve-
nience /
comfort
Expe-
rience
Efficiency
Speed
Sales
team
Technical
sales
Trade
shows &
marketing
Long
term
relations
Engineer
to cust.
spec.
Supply
chain
Suppliers
OSP
Investors
Logistic
providers
Factories Test labs Technol-
ogy / IPs
Employees €
Sales ;
engineer-
ing
Supply
chain
planning
Manufac-
turing ;
quality
Distribu-
tion
Finance ;
warranty
Warranty
Potential
recalls
Units sold
R&D
Financing
costs

28. Gearbox
Compo-
nents
(spares)
Steel Supply
chain
Compo-
nents
Car, truck
manufac-
turers
Wholesale
distribu-
tors
OutSide
Processing
suppliers
Manufac-
turing
scheduling
Opera-
tions
Supply
chain
(shipping)
Receiving,
inspection
> stock
Sub-
assembly,
machining
Final
assembly
Sequence
Stock
Supply
chain
planning
Pick
compo-
nents
Load
truck,
paperwrk.
Opera-
tions
Logistic
providers
Forecast,
firm sales
orders
Supply
chain
Schedule
transport
Tier 2
automo-
tive suppl.
Steel mills
Machine /
line
outages
Material
shortages
Quality
issues
Quality
issues
Excess &
obsolete
inventory
Unwanted
handling
(waste)
Re-se-
quencing
(waste)
Sub-
optimal
dist. plan
Gearbox
manufac-
turer
Martijn
Bakker
08-Oct-
2020
Supply
chain/Op
-erations

29. Transfer
stock
Materials
handling
analyst
Minimized
handling
(waste)
Metric:
receiving
handling
time by
receiving
volume
Optimize
default
locations
Gather
inventory
transfers
Handling
is reduced
Based on
history >
behind
ABC
inventory
analysis
Increased
through-
put
Reduced
costs
Metric:
produc-
tion han-
dling time
by prod.
volume
Maxed
handling
reduction
Maxed
cost
reduction
Maxed
through-
put
Gearbox
manufac-
turer
Martijn
Bakker
08-Oct-
2020
Prescrip-
tive stock
location
optimiza-
tion
Based on
history >
behind
Handling
is reduced
OK
Prescribe
default
stock
location
changes

30. Calculate
expected
demand
Simulate
inventory
transfers
Prescrip-
tive
Gearbox
manufac-
turer
Martijn
Bakker
08-Oct-
2020
Based on
expected
demand
ABC
inventory
analysis
Explode
Bills Of
Materials
Customer
forecast
data
Historical
demand
data
Firm sales
orders
data
Bills Of
Materials
data
Optimize
handling
time metr
Routing
data
Prescriptive
stock location
optimization
Locator
data
Gather
forecast
Gather
(future)
firm sales
Gather
historical
sales
Prescribe
default
stock
location
changesSimulate
ABC inv.
prediction
Inventory
transfer
data

31. Gearbox
manufac-
turer
Martijn
Bakker
08-Oct-
2020
Customer
forecast
(EDI/ERP)
Historical
demand
(ERP)
Firm sales
orders
(ERP)
Bills Of
Materials
(ERP)
Routing
data
(ERP)
Add des-
tination
location?
Default
locators
(WMS)
Prescriptive
stock location
optimization
Historical
inv. transf
(WMS)
Utilize to
identify
dest.?
Routing
machine/
line loc.

32. Gearbox
manufac-
turer
Martijn
Bakker
08-Oct-
2020
Prescriptive
stock location
optimization
Data lake
Functional
ERP
consultant
Functional
WMS
consultant
Data
scientist
Data
engineer
Data
science
consultant
Predictive
modelling
tool
Dashbrd.
Visualiza-
tion tool
Calculate
expected
demand
Simulate
inventory
transfers
Prescrip-
tive
Based on
expected
demand
ABC
inventory
analysis
Explode
Bills Of
Materials
Optimize
handling
time metr
Gather
forecast
Gather
(future)
firm sales
Gather
historical
sales
Prescribe
default
stock
location
changesSimulate
ABC inv.
prediction
Customer
forecast
data
Historical
demand
data
Firm sales
orders
data
Bills Of
Materials
data
Routing
data
Locator
data
Inventory
transfer
data
Business
key-user

33. Thank You for Your Attention
Feel free to contact me if you have questions:
LinkedIn profile
E-mail: martijnbakker@excite.com
Tel.: +31 (0)6 14505759

34. Questions?

35. Backup Slides