From Zero to Tensorflow: Building an Analytics Dept.
YOW! Data 2019
Day 1: one engineer vs. a heap of time-series data on a 1990s-era database
Four years on, there's 8 of us, we run TensorFlow analytics on a Hadoop cluster to detect subtle signs of a potential breakdown on earthmoving equipment. We've prevented million-dollar component failures, and reduced a lot of "parasite" stoppages.
This talk details the strategy and lessons learned from building an analytics department from scratch, in particular:
- Many analytics depts. were created as a "Flavour of the month". How do you approach this perception, survive and go beyond?
- Choosing the right projects to create a credible and sellable offering as quickly as possible to build your reputation.
- Expectation management, and choosing projects: Dealing with those who think "it won't work", and those who think you can solve all problems,
- Growing from a "start-up in a large company" to a more mature group. Change management, scaling, velocity, etc.
- Approach to R&D and launching new projects, dealing with the "shiny toys"
Hi, I'm Antoine!
I started as an electronics engineer, and after doing a PhD on Neural Networks, I caught the Data Science bug. I returned to Komatsu Australia in 2014 to start an analytics department the performs RHM (Remote Health Monitoring) on Komatsu's earthmoving equipment.
Working in the industry with an academic background, I worked on a blend of "tried-and-tested" solutions, as well as some of the bleeding edge techniques of analytics.
I like to communicate: I have some blog posts on LinkedIn, podcasts, and have published papers at 3 research conferences last year - at my last presentation I was told it was both entertaining and enlightening!