LOCATIONS: STOCKHOLM | BANGALORE | MILAN

Session Outline

Most organisations have only scratched the surface of the potential value of AI, and we data people often blame corporate culture: “my stakeholders just don’t get it, the inertia is too powerful, people won’t give up Excel, etc…” But practitioners must reflect on how our behaviour contributes: if we treat models as pets to be carefully raised over months or years, is it any wonder data people rarely rise to lead whole organisations? And are our ways of working, maximising lines of code written and bleeding edges surfed, truly cut out for the monumental task of transforming organisations? This session will challenge how we traditionally select which models to build, and offer new ideas on how to become more valuable to your organisation.

Key Takeaways

  • Where did we receive our wisdom on which models to build?
  • How does this get data scientists “trapped” in local optima?
  • Why do we avoid time dimensions in our search for value?
  • What principles will lead us to better choices in what to build? (Hint: we must first recognise the true competition for AI)
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Speaker Bio

Shaun McGirr – AI Evangelist | Dataiku
Shaun McGirr is a data leader with experience across official statistics, academia, consulting, and data science in a large automotive services company. He recently achieved minor stardom in a documentary Data Science Pioneers, coining the phrase “things that happen 35% of the time, happen ALL the time” to explain why quite likely outcomes are often dismissed out of hand. Shaun believes the toughest part of doing data well is finding the right questions and ensuring the answers will actually push a lever to change the world, a theme developed further in his podcast Half Stack Data Science. At Dataiku, he helps customers and colleagues identify and articulate the value of putting data science in the hands of everyone.

October 14 @ 14:00
14:00 — 14:20 (20′)

Day 1 | M1 | Applied Innovation & AI Transformation Stage

Shaun McGirr – AI Evangelist | Dataiku