Keynotes from Strata + Hadoop World in London 2016

Watch full keynotes covering AI, data science, data tools, and more. From Strata + Hadoop World in London 2016.

By Mac Slocum
June 3, 2016
Strata + Hadoop World keynote stage Strata + Hadoop World keynote stage (source: O'Reilly Media Conferences via Flickr)

Experts from across the data world came together for Strata + Hadoop World in London 2016. Below you’ll find links to full keynote presentations from the event.

Modern data strategy and CERN

Cloudera’s Mike Olson and CERN’s Manuel Martin Marquez discuss how CERN is using Hadoop to help drive operational efficiency for the Large Hadron Collider.

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The Internet of Things: It’s the (sensor) data, stupid

Martin Willcox explains why data management, data integration, and multigenre analytics are essential for driving business value from IoT initiatives.

Data relativism and the rise of context services

Joe Hellerstein explains why we now take a relativistic view data, where the meaning of data depends on the context in which it is used.

Saving whales with deep learning

Broad inter-domain awareness about which problems can be solved using deep learning techniques plays a key role in data analytics development.

Data wants to be shareable

Mona Vernon outlines a framework for thinking about data shareability and data monetization.

Analytics innovation in cancer research

Learn how federated analytics is helping cancer research teams analyze large genomics and patient data sets, while preserving patient data privacy and intellectual property.

The future of (artificial) intelligence

Stuart Russell argues for a fundamental reorientation of the field artificial intelligence.

Apache Hadoop meets cybersecurity

Tom Reilly and Alan Ross explain why organizations are turning toward the open source ecosystem to detect a new breed of sophisticated attacks.

The curious case of the data scientist

David Selby shares some of the data challenges he’s faced and explains why he’s particularly enthusiastic for the latest technological developments in the field.

Drawing insights from imperfection: A year of Dear Data

Stefanie Posavec discusses the insights she gained spending a year on her intensive Dear Data project.

Big data at Google: Solving problems at scale

Jordan Tigani shares what big data means for Google, and he announces several new BigQuery features.

Prophecies and predictive models: How a 3D approach to data transforms your business

Tricia Wang explores the application of “thick data” gathered through qualitative methods.

Bringing big data and design to policymaking

Cat Drew explores how the UK’s Policy Lab and GDS data teams are bringing an approach to policymaking that combines data, digital, and design.

Machine learning for human rights advocacy: Big benefits, serious consequences

Megan Price explains why machine-learning methods can be crucial to understanding and addressing patterns of violence.

Post topics: AI & ML, Data

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