How To Become A Large-Scale Data Innovator

Scaling AI Lynn Heidmann

Today, almost all companies in a wide range of industries call themselves data driven. After all, not being data driven in this day and age is hardly an option — those that don’t embrace it are edged out.

While they do likely use data at some level to drive decisions, how many of these organizations are practicing data science at scale and have a data culture throughout? Or are regularly deploying data projects into production for business-impacting results? The number of companies at this level is much smaller.

Kurt Muehmel, EMEA VP of Sales and Partnerships here at Dataiku, recently spoke at the Data Innovation Summit 2017 about what it takes to become a large-scale data innovator. He focuses on the criteria required to build a data culture, pitfalls to avoid on the path to data team scalability, and real-life use cases on successful deployment of data projects at scale.


You May Also Like

Data Science & AI Operationalization: Keys for Execution

Read More

The Coming AI Wave: Insights From IDC

Read More