Martin Szugat gave a guest lecture on developing a data strategy. He explained that a data strategy guides how a business uses data and analytics to make decisions and gain value. It involves understanding user and business needs, exploring available data sources, and developing analytics use cases and a roadmap to increase the maturity of data-driven processes, products, and business models over time. Szugat also discussed common reasons why many data projects and products fail, and emphasized the importance of designing data strategies using proven approaches like CRISP-DM to ensure business impact, user acceptance, and feasibility based on available data.

1. Martin Szugat, Guest Lecture at TUM, July 31st 2020
Space Data Strategy:
How to Gain Business Value
from Geospatial Data
Source: https://www.facebook.com/photo.php?fbid=4095642163810682&set=gm.3359180584133043&type=3&theater

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. What’sa data strategy?

5. Data is the new oil …
… but oil is worthless when its not used.

6. AnalyticsData
Datadrives your business.
Value

7. AnalyticsValue Data
A data strategy guides your business.
How?Why? What?

8. Data-DrivenDecision Making
ActionDecisionAnalytics
Measurement: Metrics & Performance Indicators
Infor-
mationData
Objec-
tive
Results

9. Data-DrivenEvolution
Data-Driven Decisions:
Users, managers and decision-makers use data & analytics to make better decisions.
Data-Driven Processes:
Sequences of data-driven decisions and actions are(semi-)automated & analytically optimized.
Data-Driven Products:
Collections of data-driven processes are bundled togetherto create
value for the customers.
Data-Driven Business Models:
all areas are data-driven.
Complexity ➔Costs, Risks&Value
Time Horizon
Data & AnalyticsStrategy

10. Products over Projects.

11. Data ProductAnalyticsData x =
What is a data product?

12. ~85% of data products fail!
https://designingforanalytics.com/resources/failure-rates-for-analytics-bi-iot-and-big-data-projects-85-yikes/

13. Reason #1: no business impact.

14. Reason #2: no user acceptance.

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

16. Why are (data)
products successful?
Feasibility Desirability
Viability
Useful(data)
productwith business
impact

17. How to define a successful
data (& analyticsstrategy)?
AccessibleData
Analytics
Use Cases
Company’s
Objectives
Successfuldata strategy

18. Before doing data science …

19. … think about data!

20. Data
Thinking
Data
Mining
Data Engineering
Data
Management
Data Strategy
Data Prototypes
Data Product
Data Sources
Data Product
Innovation Cycle
Data Assets

21. Data
Thinking
Data
Mining
Data Engineering
Data
Management
Data Strategy
Data Prototypes
Data Product
Data Assets
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→
Result → Objective)
Data Sources
(Collection,Acquisition, …)
ValuePipeline
InnovationPipeline

22. 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
(Desirability)
3. Evaluation
(Viability)
1. Data
Exploration &
Preparation
(Feasibility)
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

23. Data Strategy Design: combinationof approaches,processes & tools
Data
Thinking
(CRISP for)
Data Mining
Lean Manage-
ment
Design
Thinking
Canvas

27. Human InputAnalytics Decision Action
Data
Descriptive:
What happened?
Diagnostic:
Why did it happen?
Predictive:
What will happen?
Prescriptive:
What should I do?
Automated:
Do it.
Analytics Maturity Model
Informed
Validated
Simulated
Optimized
Automated

30. ✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓✓
✓
✓
✓

34. The Datentreiber Method: Data Strategy Design
Using the proven method of Data StrategyDesign, explore your data landscape, evaluate
relevant use cases, and develop a plan to increase your analytics maturity. For more
information, see www.datastrategydesign.com.

35. Follow on acceleran.do
Hinweis auf Accelerand.do

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