The Operations tab provides visibility into how a dataset is monitored and maintained over time. It captures operational context such as downstream dependencies, review cadence, and any active monitoring mechanisms in place.
Purpose
This tab helps governance, data engineering, and compliance teams understand how the dataset is operationally managed and how it fits into broader AI or analytics pipelines. Recording these operational details supports traceability, oversight, and lifecycle management of datasets used in AI systems.
Key Sections
Downstream Dependencies
Displays any documented downstream systems, models, or processes that rely on this dataset. This is useful for understanding the potential impact of data quality issues or changes.Review Frequency
Indicates how often the dataset is scheduled for review. This may reflect organizational policy, sensitivity of the data, or regulatory requirements. If not specified, no scheduled review is currently in place.Monitoring in Place
Summarizes any technical or procedural monitoring applied to the dataset. This may include anomaly detection, data drift monitoring, or human oversight processes. If left blank, no monitoring details have been recorded.
Notes
Use this tab to confirm whether the dataset is actively monitored or subject to regular review.
Documenting downstream dependencies helps identify and mitigate risk from data changes or disruptions.
Operational metadata captured here supports compliance efforts, especially where continuous oversight is expected under governance frameworks.