Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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Executive Briefing: How organizations scale along the data and AI maturity curve

Michael Li (The Data Incubator)
4:40pm5:20pm Thursday, March 28, 2019
Average rating: ***..
(3.75, 4 ratings)



As their data and AI teams scale from one to thousands of employees and the maturity of their analytics capabilities evolve, companies find that the analytics journey is not always smooth. Drawing on experiences gleaned from dozens of clients, Michael Li discusses organizational growing pains and the best practices that successful executives have adopted to scale and grow their team.

Topics include:

  • As organizations move from early POC quick wins to deploying them, they need to start considering productionizability, reproducibility, and scalability. Suddenly, the early “move fast and break things” mentality has its drawbacks. Michael shares techniques to migrate away from this and begin building a more rigorous data science organization.
  • As they grow, companies need to decide between a centralized versus decentralized model for data science. Michael gives the pros and cons of each and when it may be appropriate for managers to switch from one model to another.
  • Different companies have implemented different models for data science, including an R&D model, a service model, and project-based consultative models. Michael explores the relative merits of each and which business environments may be better suited for which models.
  • As organizations increase in maturity, they move from descriptive, to predictive, to prescriptive intelligence. Michael discusses the accompanying evolution in the talent needed to supply the changing analytical offerings.
  • AI and data teams aren’t silos, and they need to work with other nondata stakeholders. While early on, teams can rely on just collaborating with early adopters, as they grow, they need to engage in more data evangelism and education. Michael details successful strategies executives have employed to do this.
  • As organizations scale in number, organizations benefit from greater standardization and articulated best practices, from technical requirements like data formats and computational platforms to nontechnical requirements like organizational culture and reporting standards. Michael outlines ways to implement standardization without destroying independence and creativity.
Photo of Michael Li

Michael Li

The Data Incubator

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, JPMorgan, and D.E. Shaw. He’s a regular contributor to the Wall Street JournalTechCrunchWiredFast CompanyHarvard Business ReviewMIT Sloan Management ReviewEntrepreneurVentureBeat, TechTarget, and O’Reilly. Michael was a postdoc at Cornell, a PhD at Princeton, and a Marshall Scholar in Cambridge.