The document outlines a framework for building effective data science teams that deliver business value, emphasizing the importance of starting with a central team and aligning projects with strategic objectives. It describes the three phases of organizational data science maturity: 'experiment', 'expand', and 'integrate', highlighting necessary skills, tools, and the need for a data-driven culture. Additionally, it warns against over-specialization and promotes continuous change and experimentation within analytics practices.

1. 1
Photo by Anna Samoylova on Unsplash
How to Build Data Science Teams that
Deliver Business Value
Ganes Kesari
Gramener

2. 2
INTRODUCTION
Ganes Kesari
Co-founder & Head
of Analytics
“Simplify Data
Science for all”
100+ Clients
Insights as Stories
Help apply & adopt
Analytics
Our data science platform,
Gramex is now open-sourced!

3. 3
“ 80% of analytics insights will not deliver business
outcomes through 2022
- Gartner, Jan 2019
TODAY, WE ARE IN THE AGE OF AI, BUT..

4. 4
LET’S DO A QUICK POLL

5. 5
FINDINGS FROM A RECENT O’REILLY SURVEY ON AI ADOPTION
https://www.oreilly.com/data/free/ai-adoption-in-the-enterprise.csp

6. 6
HOW NOT TO START A DATA SCIENCE JOURNEY
Photo by Sean Patrick Murphy on Unsplash

7. 7
ANALYTICS HIERARCHY
https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

8. 8
THE 3 STAGES TO ORGANIZATIONAL DATA SCIENCE MATURITY
Kazia89636 on Flickr

9. 9
• Start from the top
• Align with strategic priorities
• Start as a central team
• Build an initial data science
roadmap
OrgSizeSpecialization&
“Experiment”
PHASE 1: “EXPERIMENT”

10. 10
IDENTIFYING YOUR FIRST PROJECT
Stakeholder
groups
Objectives Initiatives Questions Data
have a set of that can be met by which answer specific using
for that meet that can address suggests
Business driven approach
Data driven approach
Importance
Ease
Quick wins
Strategic
Deferred
Revenue impact
Breadth of usage
Effort reduction
Data availability
Technology feasibility
Prioritised initiatives
Output
Gap in current reports
Addressed by current reports
1
2

11. 11
WHAT TOOLS DO YOU NEED?
Alteryx
Amazon EC2
Azure ML
BigQuery
Birst
Caffe
Cassandra
Cloud Compute
Cloudera
Cognos
CouchDB
D3
Decision tree
ElasticSearch
Excel
Gephi
ggplot2
Hadoop
HP Vertica
IBM Watson
Impala
Julia
Jupyter Notebook
Kafka
Kibana
Kinesis
Lambda
Leaflet
Logstash
MapR
MapReduce
Matplotlib
Microstrategy
MongoDB
NodeXL
Pandas
Pentaho
Pivotal
PowerPoint
Power BI
Qlikview
R
R Studio
Random Forest
Redis
Redshift
Regression
Revolution R
S3
SAP Hana
SAS
Spark
Spotfire
SPSS
SQL Server
Stanford NLP
Storm
SVM
Tableau
TensorFlow
Teradata
Theano
Thrift
Torch
Weka
Word2Vec
The tool does not matter. A person’s skill with the tool does.
Pick the person. Let them pick the tool.

12. 12
WHAT SKILLS DO YOU NEED?
X
• Start with Generalists
• Breadth, more than depth
• Strong business alignment
• Curiosity
• Learn on the go
https://hackernoon.com/how-not-to-hire-your-first-data-scientist-34f0f56f81ae

13. 13
“ Start where you are. Use what you have. Do what
you can
- Arthur Ashe

14. 14
CASE STUDY
Deep Learning at Google
https://hbr.org/2019/02/how-to-choose-your-first-ai-project

15. 15
PHASE 2: “EXPAND”
• Transition from pilots to
projects
• Map deeper with chosen
Business units
• Extend relationship with
sponsors
• Expand your data footprint
Time
OrgSizeSpecialization&
- Generalists with many broad skills
- Teams organized by initiatives
- “Just get it done”
“Experiment” “Expand”

16. 16
WHAT’S HOLDING BACK COMPANIES AT THIS STAGE?
https://www.oreilly.com/data/free/ai-adoption-in-the-enterprise.csp

17. 17
WHAT’S THE SKILL NEEDED?
Deep
Domain Expertise
Information
Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Data Bootstrap
Skills
Passion for data Data handling
Design basics
Domain awareness

18. 18
Deep
Domain
Expertise
Info
Design &
Presentatio
n
Deep
Programming
Stats &
Machine
Learning
Data
Bootstrap
Skills
WHAT’S THE SKILL NEEDED?

19. 19
Deep
Domain
Expertise
Info
Design &
Presentatio
n
Deep
Programming
Stats &
Machine
Learning
Data
Bootstrap
Skills
WHAT’S THE SKILL NEEDED?
ML Engineer

20. 20
Deep
Domain
Expertise
Info
Design &
Presentatio
n
Deep
Programming
Stats &
Machine
Learning
Data
Bootstrap
Skills
WHAT’S THE SKILL NEEDED?
Information Designer

21. 21
Deep
Domain
Expertise
Info
Design &
Presentatio
n
Deep
Programming
Stats &
Machine
Learning
Data
Bootstrap
Skills
WHAT’S THE SKILL NEEDED?
Functional Consultant

22. 22
Deep
Domain
Expertise
Info
Design &
Presentatio
n
Deep
Programming
Stats &
Machine
Learning
Data
Bootstrap
Skills
WHAT’S THE SKILL NEEDED?
Data Analyst

23. 23
Deep
Domain
Expertise
Info
Design &
Presentatio
n
Deep
Programming
Stats &
Machine
Learning
Data
Bootstrap
Skills
WHAT’S THE SKILL NEEDED?
..but watch out for over-specialization and siloed operation

24. 24
PHASE 2: “EXPAND”
• Start planning skill-based
teams
• Evolve your execution model
• “Introduce processes, but
stay nimble”
Time
OrgSizeSpecialization&
- Generalists with many broad skills
- Teams organized by initiatives
- “Just get it done”
“Experiment” “Expand”
• Map deeper with chosen
Business units
• Extend relationship with
sponsors
• Expand your data footprint
• Bring in the specialists

25. 25
“If you only do things where you know the answer
in advance, your company goes away.
- Jeff Bezos, Amazon

26. 26
CASE STUDY
Customer Experience
Analytics at a
Technology Player

27. 27
PHASE 3: “INTEGRATE”
“Expand” “Integrate”
Time
OrgSizeSpecialization&
- Start adding few specialists
- Sprouting of skill-based teams
- “Introduce process, but stay nimble”
(Pic/Icons Credits: José-Manuel Benito/wiki, OpenClipart-Vectors/27440 images, pngtree.com, ClipartPanda.com, macrovector/Freepik)
• Analytics business-as-usual
• Diffused org-wide
• Enable continuous change
• Experimentation labs
• ”Excel at mature execution”

28. 28Source: Peter Skomoroch – Why machine learning is harder than you think, Strata London 2019
AMAZON, GOOGLE, MICROSOFT ARE REORGANIZING THEMSELVES AROUND AI

29. 29
UNCOVER YOUR DARK DATA
Source: http://www.patrickcheesman.com/dark-data-problems-and-solutions/
• INACCESSIBLE data (e.g. technology is outdated)
• FORGOTTEN data (e.g. collected, but not actively used)
• UNCOLLECTED data (e.g. information exists, not digitized)
• SINGLE PURPOSE data (e.g. used for a specific purpose)

30. 30
Data is the product
IT is the key enabler
Tools are the differentiator
Insight is the product
Analysis is the key enabler
Skills are the differentiator
Action is the product
Culture is the key enabler
Process is the differentiator
INCULCATE A DATA CULTURE
Data Science for Business, by Foster Provost & Tom Fawcett, O’Reilly

31. 31
“ Perfection is not attainable, but if we chase
perfection, we can catch excellence
- Vince Lombardi

32. 32
CASE STUDY
Analytics Culture at
Netflix

33. 33
WRAP-UP
“Experiment” “Expand” “Integrate”
Time
OrgSizeSpecialization&
- Generalists with many broad skills
- Teams organized by initiatives
- “Just get it done”
- Start adding few specialists
- Sprouting of skill-based teams
- “Introduce process, but stay nimble”
- Mature business units with specialists
- Data Culture
- Data science diffused org-wide
- “Excel at mature execution”

34. 34
@kesaritweetsgramener.com @kesari
Thank You!
Presentation deck with references at
gkesari.com/DataSummit