1. Open Data Science
A Strategy for Success in 2018

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

3. End of a era 01

4. 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)

5. Where are we today? 02 01

6. 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

7. A Vision for 2018 03

8. 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

9. Strategy For Success 04 01

10. 3. Data Op’s
2. Platform
1. Team

11. Team
Data Engineer
Data Analyst
Data Scientist

12. Open Data Science Platform
jupyter.org
dataiku.com

13. Data Platform
druid.io
snowflake.net

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

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

16. Thank You Any questions?