Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
Mark Madsen

Mark Madsen
Fellow, Teradata

Website | @markmadsen

Mark Madsen is a Fellow at Teradata, where he’s responsible for understanding, forecasting, and defining analytics ecosystems and architectures. Previously, he was CEO of Third Nature, where he advised companies on data strategy and technology planning, and vendors on product management. Mark has designed analysis, machine learning, data collection, and data management infrastructure for companies worldwide.


9:0012:30 Tuesday, 22 May 2018
Data engineering and architecture
Location: Capital Suite 14 Level: Intermediate
Secondary topics:  Data Platforms
Mark Madsen (Teradata), Todd Walter (Archimedata)
Average rating: ****.
(4.29, 7 ratings)
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multiuse data infrastructure that is not subject to past constraints. Mark Madsen and Todd Walter explore design assumptions and principles and walk you through a reference architecture to use as you work to unify your analytics infrastructure. Read more.
16:3517:15 Wednesday, 23 May 2018
Executive Briefing, Strata Business Summit
Location: Capital Suite 17 Level: Beginner
Mark Madsen (Teradata), Shant Hovsepian (Arcadia Data)
Average rating: ****.
(4.33, 6 ratings)
If your goal is to provide data to an analyst rather than a data scientist, what’s the best way to deliver analytics? There are 70+ BI tools in the market and a dozen or more SQL- or OLAP-on-Hadoop open source projects. Mark Madsen and Shant Hovsepian discuss the trade-offs between a number of architectures that provide self-service access to data. Read more.
12:0512:45 Thursday, 24 May 2018
Ask Me Anything
Location: Capital Suite 14
Mark Madsen (Teradata), Shant Hovsepian (Arcadia Data)
Average rating: ***..
(3.33, 6 ratings)
Join Mark Madsen and Shant Hovsepian to discuss analytics strategy and planning, data architecture, data management, and BI on big data. Read more.