Understanding how to evaluate and manage algorithmic performance could be the difference between success and failure. This article outlines a six-step approach for defining what to measure and monitor. Central to this approach is to work out where the waste is by measuring failure states. These are critical to monitoring
Tag: manage
Data-Product Manager: A New Distinct Role Requires The Ability To Manage A Cross-Functional Product Development And Deployment Process
As companies have struggled to make use of datasets and AI, many have started to create data products – reusable datasets that can be analyzed in different ways by different users over time to solve a particular business problem. Data products can be a powerful tool, especially for large, legacy
The Best Way To Manage No-Code Analytics Reports: An Old School Approach – 80:20 Rule
No-code tools can be helpful in democratizing data science and providing fast results for users, but IT’s old school 80:20 rule definitely applies. No-code applications are expanding because users are frustrated with IT bottlenecks and they want to get their reports and apps faster. It’s also worth noting that most
