I spent five years working as a consulting data scientist for EMC/Pivotal. In that time, I have had a chance to help several organisations in Asia Pacific set up their data science practices. Most of these organisations are large traditional enterprises with entrenched corporate practices. In each case, I must admit I wasn’t as successful as I hoped I would be going into the project, but I think I managed to leave something useful behind each and every time. I certainly learnt a lot in the process; the battle scars serve as constant reminders of what to do and what not to do. The following presentation sums up some thinking around issues that need to be understood and handled for anyone seeking to build a data science practice.
Building A Data Science Practice: Technology and Organisational Considerations
Note: Some of the slides and lessons come from ex-colleagues at EMC/Pivotal, many of whom joined me in battles in challenging on-site projects in places like Mumbai, Pohang, and Hong Kong.
Mental Models 4 Life 