In less than a year, Pirelli, a global manufacturing company best known for high-performance tires and motorsports, grew an impactful data science capability from the ground up. I am sharing a how-to guide for doing the same in your organization, equipping you with arguments to marshal and concrete tips to follow, while calling out pitfalls to watch out for along the way.

1. How a global manufacturing
company built a data science
capability from scratch
@carlotorniai
Head of Data Science and Analytics
Pirelli

2. Outline
§ Why Data Science and Analytics in Pirelli
§ What did we do differently
§ Lessons learned

3. Pirelli
§ The 5th world’s largest tyre
manufacturer
§ Leader in the Premium and Prestige
market
§ Only supplier of Formula 1 tyres
§ The Calendar

4. Why Data Science and Analytics in Pirelli?
§ Capitalize on the amount of
data available
§ Build services around data
§ Drive a cultural change

5. Main clusters of activities
Smart Manufacturing Integrated value Chain
Demand forecasting
Services built on top of
Cyber Technologies

6. What didn’t work before?
§ Tech-centered approach
within IT
§ Old approach: client -
supplier relationship
§ Core competence
outsourced

7. What did we do differently?
§ People
- Org structure and team
composition
§ Process
- Agile to break silos
§ Technology
- Right tools for the task

8. People: outside the company grid
§ Start up
§ Outside ICT
§ Reporting directly to the
CTO

9. People: insource the right talents
§ Diversity of backgrounds
§ Small and flat organization
§ Be as much “independent”
as you can across the full
DS spectrum

10. Process: agile to break silos
§ Transparency and trust
§ Break the contract game
§ Dealing with uncertainty
§ Cross team and cross
hierarch interaction

11. Process: how to stick around
§ Business driven
§ Have clear KPIs
§ Identify actionable items
§ Redefine the “idea” be data
driven

12. Technology: right tools for the task
§ It’s never about the tools
(first)
§ Democratising data and
enable smart data
interaction at every level of
the organization
§ Choose the right tools at
the right time

13. Tech stack and architecture evolution
MES
Local repo
Hadoop
Cluster
ETL
pipelines
PirelliVPC AWS Factory

14. Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure

15. Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Local analytics
Infrastructure
Data Products
Dev & Deploy

16. Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Local analytics
Infrastructure
Data Products
Dev & Deploy
Issue tracking
Notification
Smart
Alerts

17. Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Local analytics
Infrastructure
Data Products
Dev & Deploy
Issue tracking
Notification
Smart
Alerts

18. Requirements
§ Top management
commitment
§ Integration with the
business
§ Relations with IT

19. Challenges
§ Expectations and portfolio
management
§ Recruit and maintain talents
§ Resistance to change