The Five Dimensions of Data Observability
Data teams are not flying blind because they lack tools. They are flying blind because their tools only cover part of the picture. This session maps the five dimensions of complete data visibility and what it actually takes to close every gap.
-
Thursday, June 11, 2026
-
11:00 a.m. PT / 2:00 p.m. ET
-
30 minutes + live 15 minute Q&A
-
Live virtual presentation + Q&A
What You'll Learn
DataRadar™ is the only Native App that covers all five dimensions—quality + cost—within a unified solution, deployed natively inside your Snowflake environment in under 30 minutes, with the option to pay with existing Snowflake credits.
Data Observability
What It Monitors
Ensure accuracy, completeness, and consistency. Detect anomalies early. Deliver AI-ready data always.
Why It Matters for AI
Garbage in, garbage out—AI amplifies data issues
Pipeline Health
What It Monitors
Track lineage, volume, freshness, and health. Know where data comes from and why it broke. anomalies early. Deliver AI-ready data always.
Why It Matters for AI
Broken pipelines = stale models = bad predictions
Performance Optimization
What It Monitors
Monitor and optimize warehouse, query efficiency, and resource use. Prevent bottlenecks before they hit. AI-ready data always.
Why It Matters for AI
Bottlenecks delay model training and inference
Usage Intelligence
What It Monitors
Gain visibility into utilization at the user, role, and department level, identify top consumers, and optimize.
Why It Matters for AI
Identifies high-value data and compliance risks
Cost Visibility
What It Monitors
Track spend, spot inefficiencies, and get real-time alerts on cost spikes before they become problems.
Why It Matters for AI
AI workloads consume massive compute—costs kill projects
1. Marelas, P. (2024, October 22). The role of observability within organizations is changing: Key findings from the 2024 Observability Forecast. New Relic. https://newrelic.com/blog/news/insights-2024-observability-forecast
What Are The DataRadar™ Five Dimensions of Data Observability?
Complete data observability requires visibility across five dimensions. Data Quality checks the accuracy, completeness, consistency, and freshness of data assets. Pipeline Health tracks lineage, freshness, latency, and throughput to ensure data flows correctly.
Performance monitors warehouse health, query efficiency, and resource use to prevent AI workload bottlenecks. Usage Intelligence shows who accesses data, when, and how — enabling governance and compliance. Cost Visibility tracks Snowflake credit consumption, identifies waste, and forecasts spending. Most vendors address only one or two, forcing organizations to assemble point solutions that do not communicate with each other.
Speaker
Ken Kasee
Brand DirectorKen Kasee is a three-time Telly Award-winning content marketer with over 25 years of experience translating complex SaaS technology into clear, engaging stories.
At DataRadar™, he leads the educational content strategy, helping data and analytics leaders understand modern data observability. Previously, he led the US go-to-market campaign strategy at IQVIA and helped grow the Insurtech startup Ensurem by 20 times before its acquisition by Health Plan One. He earned a bachelor’s degree in economics and art history from the University of Toledo and an MBA from the University of Illinois.