Remove Data Pipeline Remove High Quality Data Remove Metadata Remove Webinar
article thumbnail

Building An “Amazon.com” For Your Data Products

Monte Carlo

Second step: Creating data product SLOs A key part of data product thinking is keeping the consumers at the center and considering what provides the most value for them. You can think of SLOs as measures that remove uncertainty surrounding the data and serve as a primary way to define trustworthiness for its consumers.

article thumbnail

61 Data Observability Use Cases From Real Data Teams

Monte Carlo

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data 52
article thumbnail

61 Data Observability Use Cases That Aren’t Totally Made Up

Monte Carlo

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.