Gramener's Head of Analytics, Ganes Kesari conducted this webinar and discussed the following points : -Why do data analytics and visualization initiatives require teams to work in silos? -What are the best organizational structures for data science? -As your data journey progresses, how should the organizational structure evolve? -Best methods for encouraging team collaboration in data projects This is a unique webinar designed for Executives, Chief Analytics Officers, Heads of Analytics, Directors, Technology Leaders, and Managers that work with data science teams on a daily basis. To check out the full webinar visit: https://info.gramener.com/data-science-teams-structure-for-best-outcomes To contact us & book a free demo visit: https://gramener.com/demorequest/

1. How to Structure Your Data Science Teams
for Best Outcomes
Ganes Kesari Webinar, June 2020

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VALUE FROM DATA
SCIENCE
ORGANIZING DATA
TEAMS
PROMOTING
COLLABORATION

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INTRODUCTION
Ganes Kesari
Co-founder & Head of Analytics
“Simplify Data Science for all”
100+ Clients
Insights as Stories
Help start, apply and adopt Data Science
@kesaritweets
/gkesari

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VALUE FROM DATA
SCIENCE
PROMOTING
COLLABORATION
ORGANIZING DATA
TEAMS

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“ Only 30 percent of
Organizations align their
Analytics Strategy with
the Corporate Strategy.
- McKinsey
Reference: McKinsey report

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HERE’S WHERE YOU MUST START TO GET VALUE FROM DATA
Organizations improve in
data maturity over phases
For business value, they must
start with users & pain areas..
..and build a roadmap by
prioritizing on 3 factors
Every data science team
must have 5 key roles..
..which become important at
different stage of maturity
There are best practices to
get the right talent onboard
Here’s a quick recap from our last 2 webinars…
Recent Gramener Webinar recordings: Webinar 1: How to pick your data science projects? Webinar 2: How to build your data science
teams?

7. Poll #1
7
What are your top challenges
in getting value from data
science teams?
Here’s a short & simple poll to help you reflect.

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HOW DO YOU SCALE DATA SCIENCE: A TALE OF 2 ORGANIZATIONS
vs

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5 KEY CHALLENGES IN STRUCTURING DATA SCIENCE TEAMS
SOLUTIONS GOING
UNUSED
SILOED
OPERATION
DUPLICATED TECH
INVESTMENTS
POOR BUSINESS
ALIGNMENT
STAGNATING DATA
MATURITY

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PIC – THE GREAT DILEMMA
What’s the right way to organize your data science team?

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VALUE FROM DATA
SCIENCE
PROMOTING
COLLABORATION
ORGANIZING DATA
TEAMS

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Executives
Data
Science
Business
Units
Sub
Business
Sales &
Marketing
Sub
Groups
CENTRALIZED TEAMS START DATA SCIENCE TOP-DOWN
• Starts with the leadership’s belief in data
• Often housed within the IT organization
• Team priorities driven by broader org needs
• Knowledge retention and sharing
• Lower redundancy
• Best talent for most important projects
• Aligned with corporate priorities
What works?
What doesn’t?
• Suboptimal business alignment
• Often seen as slow-moving/bureaucratic
• Spread out too thin

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Executives
Sales &
Marketing
Data
Science
Business
Units
Data
Science
Finance
Data
Science
DECENTRALIZED TEAMS ARE DRIVEN BY BUSINESS UNIT PRIORITIES
• Aligned with the Business units
• Often seen in mid-to-large organizations
• Multiple parallel data teams scale up
• Controlled & prioritized by business
• Better to demonstrate quick-wins
• Teams gain domain knowledge
• Lesser bureaucracy
What doesn’t?
• Siloed talent & knowledge
• Could lead to conflicting ‘truths’
• Often lacks executive support
What works?

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HYBRID TEAMS BALANCE SPECIALIZATION WITH CONNECTION
• Often led by a data executive
• Teams and tools shared
• Dual reporting structures
• Best of both worlds. Can tailor based
on org size, know-how & maturity
• Talent has ready access to business and
a connect with data competency
• Balances control and efficiency
What doesn’t?
• Ambiguity in roles & ownership could
take away the gains
What works?
…here’s an effective Hybrid model..

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A HYBRID MODEL THAT WORKS: HUB-AND-SPOKE
Source: “Building the AI Powered Organization”, HBR, Aug 2019
Hub
Central group headed by a C-level analytics executive
Spoke
Market-facing Business unit to own & manage solutions
Gray area
Work with overlapping responsibilities
Execution teams
Dynamic teams assembled from hub, spoke & gray area

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HOW DO THE HUBS AND SPOKES SCALE?
Analytics
CoE
Sales
Legal
Sales
Legal
Reference: “Building the AI Powered Organization”, HBR, Aug 2019
Sales
Legal
Analytics
CoE
Analytics
CoE
Assets
Assets Assets
Corp
Corp Corp
Risk
Fin Mktg
HR Risk HR
Mktg
Fin
HR
Risk
Fin Mktg
Data Engineering Data Science
Data as
‘Culture’
Data
Collection
Data Storage
Data
Transformatio
n
Reporting Insights Consumption Decisions

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CASE STUDY: EVOLUTION OF THE GRAMENER ORG STRUCTURE
Media
Pharma
Tech
Public Emerging
Media
Pharma
Tech
Public Emerging
Analytics Design
Product Eng.
Analytics Design
Product Eng.
Analytics Design
Product Eng.
Labs Advisory
Early-stage Mature Execution
Verticalization
Specialization
• All rolled in one
• Generalist-heavy
• Create COEs
• Onboard specialists
• Align with 5 verticals
• Hub-and-spoke structure
• Org-wide processes
• Better processes, frameworks
• Deeper COEs
• Experiments – AI, Story labs
• Improve value thru Advisory

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“You can manage a large force the
same way you manage a small
one.
It is a matter of communication
and formations.
- Sun Tzu

19. Poll #2
19
What is your current
organizational structure?
Here’s a short & simple poll to help you reflect.

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VALUE FROM DATA
SCIENCE
PROMOTING
COLLABORATION
ORGANIZING DATA
TEAMS

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WHY FOCUS ON COLLABORATION WITH DATA?
Shared
Context
Scale
Organization
s
Ensure
Business ROI
…5 Steps to Promote Collaboration

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1. Form multi-functional Teams led by a Business specialist

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2. Ensure Accountability with full Decision-making rights

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3. Empower Users and keep them in the loop to build Trust

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4. Adopt Process Frameworks for Repeatability and Scale

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5. Invest in an Ecosystem of Tools for open Conversations

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#4: Adopt Process Frameworks for Repeatability and Scale
#2: Ensure Accountability with full Decision-making rights
#3: Empower Users and keep them in the loop to build Trust
#1: Form multi-functional Teams led by a Business specialist
#5: Invest in an Ecosystem of Tools for open Conversations

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VALUE FROM DATA SCIENCE: A STUDY IN CONTRASTS
Leadership
Initiatives
Roles
Org Structure
Collaboration
Data
Data-driven
Organization
Data-aware
Organization
• Used opportunistically
• Funds initiatives
• Technology-projects
• Not multi-functional
• Centralized or Decentralized
• Unstructured conversations
• Resists data
Culture
• Leveraged strategically
• Funds & owns initiatives
• Business-programs
• Staffs 5 key roles
• Hub-and-spoke
• Explicitly planned for
• Embraces data

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“ The war is not won with
bayonets, but with effective
organization.
- Anonymous

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TO LEARN MORE.. CHECK OUT OUR OTHER WEBINARS!
View recording Coming soon…
Register for updates!
How to promote a Culture
of Data in your
Organization
View recording

31. Poll #3
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Feedback Survey
Here’s a short & simple poll to help you reflect.

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Thank You!
@kesaritweets
/gkesari
gramener.com