The times when data science teams could focus only on building and optimizing models is long past. Across sectors, data science has become a critical component of corporate strategy, and an increasing number of business decisions are being informed (and, in some cases, automatically taken) by its fruits. The major challenge is thus no longer simply building the right models but rather on companies’ ability to scale: scale their infrastructure, scale their data prototypes, scale the number of prototypes deployed to production, and, above all else, to scale the their people, both in terms of numbers and in terms of capabilities.
- Necessary criteria for building a sustainable data culture and practice
- Major pitfalls to be avoided when attempting to scale data and analytics practices
- Use cases from successful deployment of advanced analytics at scale