Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK
John Akred

John Akred
CTO, Silicon Valley Data Science

Website | @BigDataAnalysis

With over 15 years in advanced analytical applications and architecture, John Akred is dedicated to helping organizations become more data driven. As CTO of Silicon Valley Data Science, John combines deep expertise in analytics and data science with business acumen and dynamic engineering leadership.

Sessions

9:00–12:30 Wednesday, 1/06/2016
Data-driven business
Location: London Suite 2&3 Level: Non-technical
Scott Kurth (Silicon Valley Data Science), John Akred (Silicon Valley Data Science)
Average rating: ***..
(3.83, 6 ratings)
Big data and data science have great potential to accelerate business, but how do you reconcile the opportunity with the sea of possible technologies? Conventional data strategy offers little to guide us, focusing more on governance than on creating new value. Scott Kurth and John Akred explain how to create a modern data strategy that powers data-driven business. Read more.
11:30–12:00 Wednesday, 1/06/2016
Data 101
Location: Capital Suite 15 Level: Non-technical
John Akred (Silicon Valley Data Science)
Average rating: ***..
(3.25, 4 ratings)
Spark is white-hot, but why does it matter? Some technologies cause more excitement than others, and at first the only people who understand why are the developers who use them. John Akred offers a tour through the hottest emerging data technologies of 2016 and explains why they’re exciting, in the context of the new capabilities and economies they bring. Read more.
13:30–17:00 Wednesday, 1/06/2016
Enterprise adoption
Location: Capital Suite 12 Level: Intermediate
John Akred (Silicon Valley Data Science), Stephen O'Sullivan (Data Whisperers)
Average rating: ***..
(3.39, 18 ratings)
What are the essential components of a data platform? John Akred and Stephen O'Sullivan explain how the various parts of the Hadoop and big data ecosystems fit together in production to create a data platform supporting batch, interactive, and real-time analytical workloads. Read more.