Open Data Science A Strategy for Success in 2018

Portfolio Director – Data Analytics Speaker Introduction @V1Analytics Paul.Norris@version1.com ie.linkedin.com/in/norrispaul Paul Norris Portfolio Director – Data Analytics www.version1.com

End of a era 01

Compare to app. dev. evolution Standalone Code Desktop App Web App Mobile App API Microservices We are at least 10 Years Behind (DevOp’s 2008)

Where are we today? 02 01

Open Data Science Today Standalone scripts and desktop development environments the norm Reliance on key individuals - not teams & processes Tooling makes it difficult to collaborate Version, release and source control management processes are often not defined

A Vision for 2018 03

Open Data Science in 2018 R & Python usage overtakes proprietary systems Demand for data science as an application emerges in enterprises Industry data operations best practices start to be defined and used GDPR forces our profession to mature fast I.T.’s a team sport - lone data scientists are seen as a risk Desktop development moves server side Data lake hype ends, they doesn’t work for analytics Columnar storage databases make a come back in cloud as PaaS

Strategy For Success 04 01

3. Data Op’s 2. Platform 1. Team

Team Data Engineer Data Analyst Data Scientist

Open Data Science Platform jupyter.org dataiku.com

Data Platform druid.io snowflake.net

Analytics Portal superset.incubator.apache.org looker.com

The Data Op’s Manifesto Dev Op’s Process Engineering Data Management dataopsmanifesto.org

Thank You Any questions?