> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/consume/understand/quality.md).

# Trust & Freshness

Trust and freshness shows whether a data product can be relied on. It covers three signals: data quality checks, freshness, and AI-readiness. Use it before querying, reporting, integrating, or building on the product.

## Open it

From the data product overview, click **Quality** in the Quick access bar, or scroll to the **Trust and freshness** section and click **View all quality rules** or **View full run history**.

The page has two tabs: **Summary** and **Details**.

## Summary tab

A consolidated view of all three trust signals so you can decide whether to rely on the product without opening every rule. The summary line at the top shows how many dimensions need attention, the rules-passed count, and the timestamp the summary is current as of.

![Trust and freshness summary tab showing quality gauge and freshness chart](/files/y0kKmRsCHbmxoCddfsB7)

### Data quality

How much of the product's data is verified, in one glance:

<table><thead><tr><th width="245.56390380859375">Signal</th><th>What it tells you</th></tr></thead><tbody><tr><td>Rules passed gauge</td><td>Share of rules passing out of those evaluated. One number, overall trust.</td></tr><tr><td>Dimensions need attention</td><td>Number of quality dimensions with open issues.</td></tr><tr><td>As of</td><td>How current the verdict is.</td></tr><tr><td>Dimension breakdown</td><td>Passed and total counts per dimension, with a warning icon flagging the ones with issues.</td></tr></tbody></table>

The breakdown shows the dimensions evaluated for the product (**Validity**, **Uniqueness**, **Completeness**), each with a passed-out-of-total count, so you can tell whether the data is well-formed, free of duplicates, and complete.

Scroll down for the **Data quality trend** chart. It plots dimension scores over time, so you can tell whether quality is holding steady or slipping, instead of judging on a single snapshot.

![Data quality trend chart plotting dimension scores over time](/files/EIlZojUCphQUnXQ8pWcw)

Click **View all data quality rules** to open the Details tab.

### Data freshness

Whether the data is recent enough to rely on, and whether anyone is keeping it alive:

<table><thead><tr><th width="192.9715576171875">Signal</th><th>What it tells you</th></tr></thead><tbody><tr><td>Time since last run</td><td>Whether the data is current enough for your use, e.g. <code>10m since last run</code>.</td></tr><tr><td>Run duration</td><td>How long a refresh takes. Lets you anticipate when newly added data shows up.</td></tr><tr><td>Run trend chart</td><td>Whether the product runs reliably. Green bars are successful runs, red bars are failures.</td></tr><tr><td>Failure summary</td><td>Whether recent refreshes have been failing, with an <strong>Issues detected</strong> label.</td></tr></tbody></table>

Click **View full run history** to open the run history in the [Activity](/consume/understand/activity.md) tab.

### AI-readiness

Whether you can safely point an AI agent or MCP-aware tool at the product. Tiers indicate readiness:

<table><thead><tr><th width="212.68988037109375">Tier</th><th>What it tells you</th></tr></thead><tbody><tr><td>Tier A: production-ready</td><td>Connect AI agents and MCP-aware tools with confidence. The product is fully configured.</td></tr><tr><td>Lower tiers</td><td>Expect gaps if you consume through AI. The label and checklist show what's missing.</td></tr></tbody></table>

The checklist below the tier label shows which requirements are met:

* MCP server.
* Validated prompts.
* Rich descriptions.
* Semantic layer.

Click **Know more** for more on AI-readiness requirements.

## Details tab

When the summary raises a concern, the Details tab pinpoints which checks are responsible, with filtering and grouping to narrow in.

![Trust and freshness details tab showing quality rules grouped by dimension](/files/ces6zN8FC2oHHTNgxVVz)

### Coverage summary

The top of the Details tab tells you how much of the product is actually protected by rules:

<table><thead><tr><th width="204.451416015625">Stat</th><th>What it tells you</th></tr></thead><tbody><tr><td>Dimensions covered</td><td>Number of dimensions with at least one rule.</td></tr><tr><td>Models covered</td><td>Models backed by rules. Lets you spot unverified models.</td></tr><tr><td>Columns covered</td><td>Columns backed by rules. Shows how deep coverage goes.</td></tr></tbody></table>

### Quality rules table

Each row is one check:

<table><thead><tr><th width="189.43743896484375">Column</th><th>What it tells you</th></tr></thead><tbody><tr><td>Column</td><td>Which field the check protects, or <code>table-level rule</code> for model-wide checks.</td></tr><tr><td>Rule</td><td>What the check verifies, in human-readable form.</td></tr><tr><td>Dimension / Model</td><td>Where the rule fits: its dimension (grouped by Models) or its model and layer (grouped by Dimensions).</td></tr><tr><td>Status</td><td>Green check is pass; amber triangle is a warning or failure.</td></tr><tr><td>Last 5 runs</td><td>Heatmap of the last five runs (green pass, amber failure). Tells you whether the check is stable or flaky.</td></tr></tbody></table>

### Group and filter

Use the **Group** dropdown to organize rules by **Models** or **Dimensions**.

![Group dropdown showing the Models and Dimensions options](/files/HPPqjkAKztAik5xQ8Nrr)

Grouped by Models, rules sit under each model.

![Trust and freshness details tab showing quality rules grouped by model](/files/mQ2tdweNLYx5s8et2s1m)

Use the filter icons on the **Dimension** and **Status** columns to narrow the table. Use the **Search** field to find a column or rule by name.

### With rules vs without rules

Use the dropdown at the top right to switch:

![With rules and Without rules dropdown above the quality rules table](/files/bWSx8EaBr1kzlGNWEfCM)

* **With rules** - The default. Every column and model that has at least one rule, with each rule's dimension, status, and recent runs.
* **Without rules** - Columns and models with no rules assigned, each shown with its layer (e.g. `bronze` or `postgres / public`). Use this view to spot coverage gaps.

![Without rules view listing columns and models with no quality rules assigned](/files/SyDmXcsjNUV4E1CFjj0E)

### Reading a group

Grouped by **Dimensions**, each group shows the dimension name and an amber badge with the count of issues. Expand to see the rules.

Grouped by **Models**, each group shows the model name, schema path, and an issue badge. Expand to see the rules, the dimension each belongs to, and current status.

### When to use Details

| Question                                | Where to look                                 |
| --------------------------------------- | --------------------------------------------- |
| Which rules are failing?                | Filter Status column to warnings or failures. |
| Which dimension has the most issues?    | Group by Dimensions and check issue badges.   |
| Which model is causing quality issues?  | Group by Models and check issue badges.       |
| Is a specific column covered by a rule? | Search by column name.                        |
| Which columns or models have no rules?  | Switch the table to Without rules.            |
| Has a check been stable over time?      | Last 5 runs column.                           |

## When to use Trust and freshness

| Question                            | Where to look                       |
| ----------------------------------- | ----------------------------------- |
| Are quality checks passing overall? | Summary tab: quality gauge          |
| Which dimensions have issues?       | Summary tab: dimension breakdown    |
| Is the data recent?                 | Summary tab: Data freshness section |
| When did the last run succeed?      | Summary tab: run trend chart        |
| Is this product ready for AI use?   | Summary tab: AI-readiness section   |
| Which specific rules are failing?   | Details tab: quality rules table    |
| Which models are covered?           | Details tab: Models covered stat    |


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