Subject archive for "perspective," page 7

Today we launched ‘Data Science Leaders,’ a new podcast series dedicated to helping teams scale the booming practice of data science by documenting and sharing real stories, breakthrough strategies, and critical insights into building a model of enterprise data science success, from those who have done it.

By Brian Loyal, Cloud Analytics Lead, and Naveen Singla, Vice President of Data Science, Bayer Crop Science

If there’s one thing that data science leaders likely agree on, it’s that there’s no “right” way to organize data science teams as you build out your enterprise strategy to accelerate model velocity.

This week Gartner published their latest Magic Quadrant for Data Science and Machine Learning (DSML) Platforms. Meanwhile, Forrester has two separate Waves for Predictive Analytics and Machine Learning (PAML) platforms — one notebook-based and one multi-modal — plus a third New Wave for automation-focused machine learning solutions. And Matt Turck’s 2020 Data & AI Landscape lists hundreds of companies in dozens of sub-categories.

At Domino, we’re fortunate to be able to work with the leaders of the world’s largest data science organizations. One thing that’s really stood out is that even the most sophisticated organizations struggle to understand and manage the depth and breadth of their data science investments. This situation gets even worse when you expand “data science” to include the broader big data, BI, and analytics capabilities that organizations are investing in. The complexity stems from the explosion in new tools, capabilities, and processes over the last five years, and many CIOs and CDAOs don’t actually know which systems are driving which business processes, and which teams are getting stuck vs. delivering value.