Making data science a success is really hard with up to 85% of projects and initiatives around big data and data science failing, according to Gartner. The reasons are complex but often misunderstood. As project management begins to grapple with the opportunities presented by data science those responsible for implementation need to proceed with care.
What is so different about data that it needs new approaches? This talk will focus on the requirements for data science success and looks at a future after the hype:
- Motivation: Vanity project or aligned business strategy with senior leadership buy-in?
- Requirements and preparations: Solid foundations or duct taped data silos and constant fire fighting bad data?
- Hiring: Unicorns with the right skill sets to be a commercial data scientist or expensive mis-hires?
- Delivery: Models in production serving business needs or undocumented proof of concepts on laptops?
- Retention: Roadmap of game changing projects or abandoned team and expensive write-offs?
Head of Data Science
11:30AM - Day 2