The document discusses strategies for building cross-functional teams to enhance data science production and automate decision-making. It emphasizes the importance of organizational structure, suggesting that teams should be domain-oriented to optimize speed and reduce dependencies. Various models and roles within these teams are explored, alongside the need for integration and best practices to overcome silos and improve collaboration.

1. Lightning Talk: How to build a
cross-functional team to
productionise data science and
deliver automated decision-making
at pace and scale

2. // Harvinder Atwal
MoneySuperMarket
// Web
dunnhumby
{"previous" : "Insight Director, Tesco Clubcard"}
Lloyds Banking
Group
{"previous" : "Senior Manager, Customer Strategy and Insight"}
{"Current" : "Chief Data Officer"}
@harvindersatwal
British Airways
{"previous" : "Senior Operational Research Analyst"}
{"about" : "me"}
@gmail.com

3. £2.1B
SAVINGS
2018 estimate total of UK savings
1993 24.9M 13 million £356M 989
We started life
as mortgages
2000
Adults choose
to share their
data with us
Average Active
users
2018
Revenue
2018
Product
Providers

4. Organise around the ideal data journey not skills or technology
Data storage and Databases
Cloud file storage, NoSQL DB,Distributed file system, RDBMS, Analytical DB
Compute infrastructure and Query execution engines
VMs, Container services, and Distributed compute frameworks
Distributed SQL execution engines
Development tools, workspaces and software libraries
Data Analytics
Data exploration, Data
visualization, Data analysis,
Data science, Machine
learning, Deep learning
Reproducibility, Deployment, Orchestration and Monitoring
Output files
BI Tools
Interactive
dashboards
Web Apps
APIs
Data
Ingestion
Data
Transformation
Data
Analytics
Data
Products
Product
creation,
Customer
experience
and Business
efficiency
Use Cases
Data integration and Data processing
pipelines
ETL/ELT tools, Stream processing
MDM, Data unification, and Data preparation
Data management
Production System
Product Development System

5. What does the data say?

6. Conway's Law isn't academic
Microsoft's research found organisational structure predicted code quality better than
other measurable factors such as Code Churn, Code Complexity, Dependencies, Code
Coverage or Pre-Release Bugs

8. Nearly 60 percent of breakaway organizations use
cross-functional teams, versus less than a third
of the remaining respondents that do so.

9. #1 Team Orientation

10. Functional teams are organized by technical expertise, e.g.
data scientists, data engineers, BI developers, DBAs.
Optimized for maximum resource utilisation of scarce talent. No duplication of expertise across teams.
Specialised talent is allocated to projects as soon as it is available.

11. Database
and data
storage
team
Source
data system
owners
IT security
and data
governance
team
Data
science
team
Data
engineering
team
Data
analyst
team
Compute
infrastructure
team
IT Operations
team
Stakeholders
Use cases
Functional teams are optimised for resource utilisation not
speed
Only works well if all functional teams share goals and objectives, or they
focus on delivering self-service for other teams.

12. Matrix
Good Film
But terrible
organisational
structure

13. Domain-orientated Teams are organized by
customer, product, value-stream, segment, service,
market, etc.
Optimised for speed. A cross-functional
team is self-sufficient and has few external
dependencies.

14. Connecting domain-orientated teams
Spotify Model
Hub and Spoke
Chapters and Guilds.
Chapters are people with similar skills,
for example, data engineers, who meet
regularly to discuss their area of
expertise and challenges.
Guilds are communities of interest or
communities of practice who want to
share knowledge, tools, code, and
practices.
Loose Connection Tighter Connection
Central team or Centre of
Excellence ensures best
practice and consistency
Coordinator
SME, e.g. solutions
architect keeps teams
aligned on best practice

15. #2 Reporting Lines

16. Centralised reporting lines

17. Decentralised Integrated - Netflix model
“Highly aligned, Loosely coupled”
Works well with very large teams

18. Hybrid Centralized/Embedded -
AirBnB
Corporate
Centre of Excellence Business unit Function
Analytics Group Analytics Group
Analytics Project Analytics Project

19. #3 Skills Personas

20. Data
Scientist
Data
Analyst
Data
Engineer
Not all roles fit in cross-functional teams
ETL
Quality Testing
Descriptive
Analytics
Advanced
Analytics
BI
Dev
Ops
IT
Ops
DBAs
Business
Stakeholders
Operations
Sys
admin
Developers
ML
Product Managers

21. Core
Personas
Data Engineer Data AnalystData Scientist Team Lead
Data Platform
Administration
ML
Engineer
Supporting
Personas
Solutions Architect
DBA
Security Expert
Specialist
Tester
Technical Lead Designer

22. There’s a problem with cross-
functional teams

23. Silos within teams

24. #4 Cross-skill

25. Dash-Shaped
(Generalist)
Capable in a lot of things
but not expert in any
No I (or -) in teams
I-Shaped
(Specialist)
Expert at one thing
Poor Better Good Best
Breadth of knowledge
Depth of knowledge
T-Shaped
(Generalising Specialist)
Capable in a lot of things
and expert in one.
Pi-Shaped
(Multi-skilled)
M-Shaped
(Poly-skilled)

26. Analytics
Specialists
and Centre of
Excellence
Source data
system
owners
Data Management and Platform teams
(Databases, data storage, compute infrastructure, analytical tools, data governance and security,
master data management, operations, etc.)
Domain
use cases
Cross-functional domain team
(Data engineers, Data scientists, Data analysts, Stakeholder,
etc.)
Cross-functional domain team
(Data engineers, Data scientists, Data analysts, Stakeholder,
etc.)
Cross-functional domain team
(Data engineers, Data scientists, Data analysts, Stakeholder,
etc.)
Domain
use cases
Domain
use cases
Self-service access Productionise
Domain-orientated Teams (optimised for speed)

27. "Perfection cannot be reached because nothing's
perfect. But things can change, and so can people"
John O'Callaghan
John O'Callaghan

28. SHAMELESS
SELF-PROMOTION
AHEAD

31. // Harvinder Atwal // Web
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