Data Observability: Establishing Trusted Data Across the Enterprise Data Pipelines
Webinar Speaker: David Loshin, TDWI Research Fellow
Date: Thursday, September 24, 2026
Time: 9:00 a.m. PT / 12:00 p.m. ET
What happens when the data your organization trusts is wrong, but nobody knows it?
Organizations increasingly treat data as an organizational asset, yet much of the data flowing through our systems remains effectively unobserved. Small data failures can silently propagate through data pipelines as missing records, unexpected values, or subtle changes in data distribution. These can multiply unnoticed, corrupting dashboards, distorting AI model outputs, and influencing bad decisions. By the time the problem is discovered, its impact may extend far beyond the original failure. Operational costs rise, teams lose confidence in their data, and overall trust in the systems themselves begins to erode.
Data observability addresses this challenge by making the condition and behavior of data pipelines visible and actionable. In this presentation, we will examine the benefits of data observability: how data quality monitoring identifies problems before they propagate, ML-powered anomaly detection exposes silent failures and unexpected patterns, and end-to-end data lineage provides the context needed to trace problems to their source and assess their downstream impact. Job monitoring helps maintain pipeline reliability, while pipeline performance and cost optimization ensures that delivering trustworthy data does not come at an uncontrolled operational cost.
Attendees will learn:
- How to monitor data quality and detect anomalies before they impact downstream systems.
- How lineage and jobs monitoring accelerate root-cause analysis and keep pipelines healthy.
- How to optimize pipeline performance and costs while improving reliability.
- How data observability helps protect the integrity of AI models, BI dashboards, and the decisions that depend on them. /li>