Observability is a separate signal from Quality: Quality scores correctness (DQ measures), Observability tracks reliability (did data arrive on time, in the expected volume, with the expected schema). An asset can be observability-healthy and still fail quality checks, or vice versa.
AI Summary
An AI-generated summary states the headline reliability figures for the current date range. In validation, this summary did not always refresh immediately when switching between the Catalog, Data, Pipeline, and Report tabs — it can briefly keep showing the previous tab’s text. If the summary’s wording doesn’t match the tab you’re looking at, give it a moment or treat the tiles and widgets below as authoritative.KPI cards
Uptime tiles
Each uptime tile compares against a 100.00% prior 90d baseline — the previous period is treated as a perfect baseline for comparison purposes rather than its own measured value.
Count tiles
Widgets
Alert heatmap
A heatmap of alert counts with Domain as rows and time buckets (controlled by Slice By) as columns, with sub-tabs for Volume, Freshness, and Schema. The legend runs 0-20 green → 80+ red, because for alerts higher is worse — this is the inverse of the Quality Dashboard’s score heatmaps, where green means a high (good) score.Issue heatmap
The same structure as the Alert heatmap — Domain rows, Slice-By time-bucket columns, Volume/Freshness/Schema sub-tabs, 0-20 green → 80+ red — but for issue counts instead of alert counts.Recent schema changes
A table of individual schema-change events at the column level (finer-grained than the Catalog Dashboard’s asset-level Schema Changes widget). Columns: Asset, Change Type (e.g. Column Added), Changed Field, Impact (e.g. Additive), Changed (timestamp).Unhealthy assets
A table of assets with at least one reliability problem. Columns: Asset, Data Updated (last update timestamp), Freshness / Volume / Schema (per-dimension status, e.g. Stale / Normal / Stable), Impacted Assets, Reports, Dashboards (downstream BI impact counts via lineage).Filters
Same filter bar as the other Analytics dashboards: Domain, Application, Product, Tag, Source, Asset Type, Asset, Date Range, Slice By, Previous Period. See Catalog Dashboard for the full list of options; the mechanism is identical here.Slice By: time-bucket granularity for the heatmaps
Slice By offers the same six options as elsewhere (Day, Week, Month, Quarter, Half-year, Year; defaults to Week). On this dashboard it rebuckets the column headers of the Alert heatmap and Issue heatmap — it does not change any KPI tile. Worked example, no other filters applied:
Only the heatmaps’ column buckets changed — every KPI tile stayed identical, the same pattern documented for the Quality Dashboard’s trend chart and heatmaps.
With filter vs. without filter: a worked example
Example captured during validation, Last 90 days, no Slice By change:
Narrowing to a single asset type reduces every count tile (fewer matching assets/events) and, in this snapshot, also reduced the uptime percentages slightly — tables in this environment had a somewhat worse reliability record than the full asset population over the period. The same mechanism applies to every other filter field and to combinations of them.
The exact figures above are an illustrative example and will not match your environment. The relationship they demonstrate — filtering narrows the population, Slice By only rebuckets time — is what to rely on, not the specific numbers.
Related pages
Catalog Dashboard
The Catalog tab of Analytics — asset discovery, classification coverage, and governance health
Quality Dashboard
DQ score, alerts, and issues — the correctness counterpart to this reliability dashboard