What Does It Take To Set The Business Up For Data Science Success?
Many businesses are stuck at step 1 when it comes to monetizing Machine Learning. In order to move past prototypes, there's work to be done.
There’s a difference between businesses using Data Science and businesses creating competitive advantages from Data Science. Moving from prototypes or proofs of concept to top and bottom line impacts requires:
Well built teams comprised of people with complementary capabilities instead of solo unicorns.
Teams need Data Engineering, Machine Learning Engineering, Model Quality Assurance, and Data Science capabilities. They also need access to domain expertise and front line implementation teams. Unicorns exist and having one is nice. However, teams are built around capabilities and diverse backgrounds not unicorns.
Supporting tools and infrastructure.
Tools make teams more efficient and help establish a cadence to project delivery. Data Science needs automation to deliver complex projects in business timelines. Infrastructure creates a path to production. It’s difficult to handoff models and have another team try to productionize when there is a gap in model development infrastructure. Reliable models do not come off a laptop and go straight into production.
Strategy and business leadership who create a unified vision for the technology and why it will help the business grow.