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How a global manufacturing company built a data science capability from scratch

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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.

Published in: Data & Analytics
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How a global manufacturing company built a data science capability from scratch

  1. 1. How a global manufacturing company built a data science capability from scratch @carlotorniai Head of Data Science and Analytics Pirelli
  2. 2. Outline § Why Data Science and Analytics in Pirelli § What did we do differently § Lessons learned
  3. 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. 4. Why Data Science and Analytics in Pirelli? § Capitalize on the amount of data available § Build services around data § Drive a cultural change
  5. 5. Main clusters of activities Smart Manufacturing Integrated value Chain Demand forecasting Services built on top of Cyber Technologies
  6. 6. What didn’t work before? § Tech-centered approach within IT § Old approach: client - supplier relationship § Core competence outsourced
  7. 7. What did we do differently? § People - Org structure and team composition § Process - Agile to break silos § Technology - Right tools for the task
  8. 8. People: outside the company grid § Start up § Outside ICT § Reporting directly to the CTO
  9. 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. 10. Process: agile to break silos § Transparency and trust § Break the contract game § Dealing with uncertainty § Cross team and cross hierarch interaction
  11. 11. Process: how to stick around § Business driven § Have clear KPIs § Identify actionable items § Redefine the “idea” be data driven
  12. 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. 13. Tech stack and architecture evolution MES Local repo Hadoop Cluster ETL pipelines PirelliVPC AWS Factory
  14. 14. Tech stack and architecture evolution PirelliVPC AWS Factory MES Local repo Hadoop Cluster ETL pipelines Analytics Infrastructure
  15. 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. 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. 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. 18. Requirements § Top management commitment § Integration with the business § Relations with IT
  19. 19. Challenges § Expectations and portfolio management § Recruit and maintain talents § Resistance to change

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