Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Executive Briefing: 5 things every executive should NOT know

Ellen Friedman (Independent)
16:3517:15 Wednesday, 1 May 2019
Average rating: ****.
(4.20, 5 ratings)



Prerequisite knowledge

  • Familiarity with large-scale systems and data-driven business (useful but not required)

What you'll learn

  • Understand key indicators that an organization is taking advantage of modern technologies and large-scale approaches
  • Explore best practices for designing effective architectures and leveraging emerging infrastructure
  • Learn how to connect data-intensive AI, machine learning, and analytics applications to practical business goals and build real multitenancy—and why that matters


For modern businesses to release the potential value in large-scale data and stay competitive, they need more than just a lot of data. They need new practices and evolving architectures that use emerging technologies appropriately. Strategies need to take into account not just which data-driven applications should be developed but also how to take business-based action on the insights they deliver, how to build and maintain infrastructure that supports reliability and flexibility, and how to manage the teams that implement all this.

To be efficient and practical but still take advantage of innovation, it’s essential to have an effective separation of concerns. The good news is that there’s a wealth of new options for highly effective infrastructure and corresponding architectures that make this possible.

One way to tell if your organization is doing this well is to consider certain things that executives should not know, either because they shouldn’t be bothered with them or because seeking them out is a flag that your organization isn’t really adapting to modern practice.

Ellen Friedman outlines five things that best practice with emerging technologies and new architectures can give us ways to not know—and why that’s important. Join in to learn some key indicators about whether or not your organization is on the right track and ways to make corrections if need be.

Photo of Ellen Friedman

Ellen Friedman


Ellen Friedman is a data technologist with a Ph.D. in biochemistry. She is a committer for Apache Drill and Apache Mahout projects and co-author of books including AI & Analytics in Production, Machine Learning Logistics, Streaming Architecture, the Practical Machine Learning series, and Introduction to Apache Flink, all published by O’Reilly Media. Ellen has been a keynote speaker at JFokus in Stockholm, Big Data London and NoSQL Matters Barcelona and an invited speaker at Strata Data conferences, Berlin Buzzwords, Nike Tech Talks, and the University of Sheffield Methods Institute.