How to build an organisational capability and set it up for success, not failure

1. Identifying and Prototyping
Data Science Use Cases
How to build an organizational capability
and set it up for success, not failure
Ambrus Vancso

2. 60%

3. The Problem
Data projects fail too often
page
03*Nick Heudecker is VP Analyst at Gartner
*

4. Where do Data Science
Projects go Wrong
page
04
No Clear Vision
On Business Goal
Models Not Understood
By Stakeholders
Lack Of Sponsorship And
Other Project Woes
False Assumptions
About Data
Results Never
Deployed
Not The Right Skill
Mix On Team
Challenges

5. Identify

6. page
06
EVALUATE
Clarify
Understand
Qualify
Identify Impact
(cost/revenue/capability)
Define Metric
PRIORITIZE
Value
Effort
Need
Visualize
Communicate
CAPTURE
Involve Key Stakeholders
Prepare
Questionnaire /
Interview / Workshop
Record
Organisation
Business Process
Persona
Data
Systems
Use Case Identification Process
Define Value for Organization

7. Capture
Key Questions to
Understand
page
07

8. Capture
Example One Pager
page
08

9. Evaluate
Clarify if BI or DS
page
09
Is it a Data Science Project at all?

10. Prioritize
Joint Decision
page
010
Effort
Value

11. The Cycle

12. The Scientific Method
As an Ongoing Process
page
012
Observe
Develop
General Theory
Build
Hypothesis
Testable
Predictions
Ask questionsGather Data to
Test Predictions
Iterate

13. Agile Process
As in Software Engineering
page
013
Multidisciplinary
team
Iteration
Increased Understanding
and Focus on Delivery of
Real Value
Product Backlog Release Backlog Sprint Backlog Product Release Retrospective

14. KNOWN UNKNOWN
Types of Data Science Projects
Engineering vs Science Paradigm
page
014
Question
DataKNOWN UNKNOWN
R&DExploration
DiscoveryIteration
Addressable space
R&D

15. An Agile Process
Best of Both Worlds
page
015
PrototypeUse Case
03
02
04
01
DELIVER tangible result in every sprint,
aim for end-to-end as much as possible
TIMEBOXING and continuous backlog
revision is the key to bridge approaches
DEFINITION OF DONE is essential and not
native to most Data Scientists
ITERATION is the most common
denominator between scientific and
agile engineering paradigm

16. Prototype

17. What is a Prototype?
Shared Understanding is Key
page
017
POH or POC
Proof of Hypothesis,
can be part of a POC
An experiment
basically
Something tangible coming
out of the project that
has the potential of
practical usage
and scaling
Prototype
Something ready for production
deployment without
significant rework
MVP
Definition of done

18. Use a Prototype to
Understand and Validate
page
018
Data Feasibility
TeamPlatformNeed
Value

19. page
019
Skills and Roles

20. Big Enough vs Big Data
Progress is much, much faster and
easier if tools and data fits into memory
page
020
VS.
Note: you can rent machines of up to TiB range on
the cloud. GPU instances also available.
A memory optimized node of 488 GiB is <5 $/hr.

21. Data Handling Paradigms
Optimize for Persona - can also Mix & Match
page
021
Code
Powerful, Open, Difficult
Expert Data Scientist, Developer
Engineering friendly
Jupyter, RStudio
Pipeline
Powerful, Specific, Learnable
Citizen Data Scientist, Analyst
Middle ground, “Lingua Franca”
Alteryx, KNIME, Rapidminer
Table
Limited, Narrow, Intuitive
Analyst, Office User
Everybody knows spreadsheets
Excel -> Power BI

22. Be Ready To Explain
Always Have a White Box Model (As Well)
page
022
VS.

23. Communicate, Visually
Tell the story – in light of your key Personae
page
023
0
200
400
600
800
1 000
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jan feb mar apr may jun jul aug sep oct nov dec
Approach
One
Approach
Two
Approach
Three

24. Operationalize

25. Operationalize and Scale
Infrastructure, Process,
Organizational Challenges
page
025* Source: Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-
systems.pdhttp:/martin.zinkevich.org/rules_of_ml/rules_of_ml
ML
Code

26. Operationalize and Scale
Data Science Platforms
Can Give a Head Start
page
026
Open Source
Fully open source, open core or business source
offerings.
Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms
Source: https://www.kdnuggets.com/2018/02/gartner-2018-mq-data-science-machine-learning-changes.html
Note: Gartner research methodology does not
include open source platforms like R and Python.
While these are powerful, a platform shines in its
comprehensiveness rather, especially when it
comes to ease of deployment.
Also most open platforms already allow to run
R/Py scripts and incorporate the results.

27. Thank you
@ambrusvancso
linkedin.com/in/ambrusvancso/
+44 7937 962 169