> 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/about.md).

# Overview

Understanding a data product is the second step in the consumer journey. Once you've found one that looks relevant, you check whether it's actually fit for what you need.

This step answers what's in the product, who owns it, how it's built, whether quality checks pass, and whether anyone is keeping it alive.

## Open the data product page

From the Hub, click a data product card or row to open its full page.

![Data product overview page with description, stats, and navigation](/files/96hsLHLPYQMOuZBDCQKP)

## Data product header

The data product header gives you a fast read on what the product is, what it covers, who owns it, and whether it looks trustworthy, all before opening any section. Use it to decide in seconds whether the product is worth a closer look.

![Data product header showing title, type, description, metadata chips, quality dimensions, and quick access bar](/files/dc0lGU5lZFw8DvdIIj6n)

<table><thead><tr><th width="180">Element</th><th>How it helps</th></tr></thead><tbody><tr><td>Name and type</td><td>Confirms you opened the right product and what kind of asset it is. Tags under <strong>+ N more</strong> tell you the business topics it covers (<code>orders</code>, <code>revenue</code>, <code>customer_segmentation</code>) so you can judge relevance.</td></tr><tr><td>Description</td><td>What the product is for and which systems it runs on, in plain language.</td></tr><tr><td>Metadata chips</td><td>Source, domain, documentation, and collection. Place the product in context. Copy the identifier to reference it in queries, code, or support requests.</td></tr><tr><td>Reference links</td><td>Direct links to external docs and dashboards.</td></tr><tr><td>Version</td><td>Which iteration you're looking at. Matters when behavior or schema has changed between versions.</td></tr><tr><td>Owner</td><td>Who to contact for questions, access, or issues. Hover the avatar and click <strong>View contact section</strong> to reach them without scrolling.</td></tr><tr><td>Quality dimensions</td><td>Whether <strong>Completeness</strong>, <strong>Validity</strong>, or <strong>Uniqueness</strong> need attention.</td></tr><tr><td>Follow / Unfollow</td><td>Track a product you rely on and stay aware of changes. Following also signals adoption to the owning team.</td></tr><tr><td>Explore</td><td>Start querying once you've decided it fits.</td></tr></tbody></table>

A few data product header actions worth pointing out:

* Click the identifier chip to copy the product identifier when you need it in a query, API call, or support request.

  ![Identifier chip showing the Click to copy identifier tooltip](/files/zlkTJol4DcGP0QK9qWr4)
* Hover the owner avatar to see who's accountable and jump to their contact details.

  ![Owner popover showing the owner name, role, and View contact section link](/files/evbMawj5CEktWbpYBwx5)
* Hover **+ N more** to see the full set of business tags.

  ![Tags popover showing orders, revenue, and customer\_segmentation](/files/oY5pn5CeVaoSklee0Sjw)

## Quick access

Below the description, the **Quick access** bar links to the most-used sections:

<table><thead><tr><th width="161.55487060546875">Link</th><th>What it opens</th></tr></thead><tbody><tr><td><a href="/pages/F4tq8ivZReEU5N7q4ddI">Assets</a></td><td>Inputs, models, semantic models, metrics, and lineage.</td></tr><tr><td><a href="/pages/w9fldZ8JVE82fbDzUkdH">Quality</a></td><td>Quality checks across the product's models and dimensions.</td></tr><tr><td><a href="/pages/tb7v7dP3H7s9LdTOty9t">Runs</a></td><td>Run history.</td></tr><tr><td><a href="/pages/86QhosHWjS4RGKbKHxaG">Metrics</a></td><td>Metrics defined on the product.</td></tr><tr><td><a href="/pages/dvr3ZebSNpVvi6SlQntF">Perspectives</a></td><td>Saved query views built on the product.</td></tr><tr><td>Apps</td><td>Data applications linked to or deployed on the product.</td></tr><tr><td><a href="/pages/OlvkX9wZ6Gm8cmlKjxdl">Activate</a></td><td>Connection options for MCP, BI tools, APIs, and database clients.</td></tr></tbody></table>

## Stats bar

Four live signals at a glance:

<table><thead><tr><th width="165.83056640625">Signal</th><th>What it shows</th></tr></thead><tbody><tr><td>DATA QUALITY</td><td>Quality score: rules passed out of total.</td></tr><tr><td>FRESHNESS</td><td>How long ago the data was refreshed.</td></tr><tr><td>QUERIES</td><td>Total queries run against the product.</td></tr><tr><td>FOLLOWERS</td><td>Number of users following it.</td></tr></tbody></table>

## Sections

The left navigation breaks the page into the following sections.

### Overview

A holistic view of the data product. The diagram maps how data flows from inputs through models, semantic models, and metrics, all the way to where you connect. It lets you grasp scope and end-to-end shape without opening every asset, and jump straight to a way to consume.

![Holistic view of a data product showing the flow from Inputs through Models, Semantic Models, Metrics, and Connect](/files/RLdOWwgIblyhyDSrBSC3)

The view follows data left to right. Each column has a count, so you can gauge size and complexity before diving in:

<table><thead><tr><th width="190">Column</th><th>What it tells you</th></tr></thead><tbody><tr><td>Inputs</td><td>Where the data originates. Lets you judge source coverage and trust.</td></tr><tr><td>Models</td><td>How raw data is shaped into curated tables across <code>bronze</code>, <code>silver</code>, and <code>gold</code> layers. Shows how much transformation stands behind the product.</td></tr><tr><td>Semantic Models</td><td>The business-friendly entities (<code>customer_profile</code>) you actually query. Shows whether the concepts you need are exposed.</td></tr><tr><td>Metrics</td><td>Ready-made measures (<code>customer_lifetime_value</code>) you can reuse instead of recomputing.</td></tr><tr><td>Connect</td><td>The supported ways to consume the product (MCP, BI tools, APIs, DB clients). Confirm it fits your tooling before going further.</td></tr></tbody></table>

The **Last updated** timestamp tells you how current the picture is. Zoom and fit controls help with bigger products.

![Holistic view columns listing Inputs, Models, Semantic Models, Metrics, and Connect with counts](/files/kq4TBc42IhIbocg6Ghpi)

The Overview also has an **Is it the right fit for me?** panel:

<table><thead><tr><th width="134.38470458984375">Tab</th><th>What it contains</th></tr></thead><tbody><tr><td>Good for</td><td>Business use cases the product is suited for.</td></tr><tr><td>Not good for</td><td>Scenarios where it's the wrong choice.</td></tr><tr><td>Caveats</td><td>Known limitations.</td></tr></tbody></table>

### What it answers

The business outcomes the product was built for, presented as metric cards with short explanations.

![What it answers section showing business outcomes the product is built for](/files/sjlMc9IiwaiFQ4m6HLij)

### Trust and freshness

The three signals used to judge whether the product can be relied on:

<table><thead><tr><th width="161.65032958984375">Signal</th><th>What it shows</th></tr></thead><tbody><tr><td>Quality</td><td>Quality rules passed across Validity, Uniqueness, and Completeness. Shows <strong>Needs attention</strong> when there are issues.</td></tr><tr><td>Data freshness</td><td>Time since the last run, last run duration, and a bar chart of recent runs (success and failure). Shows <strong>Issues detected</strong> when recent runs failed.</td></tr><tr><td>AI-readiness</td><td>Whether the product is configured for AI consumption. Includes a tier label and a state like <strong>Production-ready</strong>, with the underlying requirements (MCP server, validated prompts, rich descriptions, semantic layer).</td></tr></tbody></table>

![Trust and freshness summary showing Quality, Data Freshness, and AI-readiness](/files/dzt9JxmcAfK3xEAFZYy7)

Click **View all quality rules**, **View full run history**, or **Know more** to open the full trust and freshness page.

### How to activate

The supported consumption paths:

<table><thead><tr><th width="171.6046142578125">Option</th><th>Use when</th></tr></thead><tbody><tr><td>Connect via MCP</td><td>You're plugging the product into AI agents and workflows.</td></tr><tr><td>Connect BI tools</td><td>You're using outputs in Power BI, Tableau, or another BI tool.</td></tr><tr><td>Build on APIs</td><td>You're building a custom application or workflow on the product's APIs.</td></tr><tr><td>Query via DB clients</td><td>You want to explore the product from a SQL client.</td></tr></tbody></table>

![How to activate section showing four consumption path options](/files/felpFTaoPoPZtAs9sV7y)

### How it's being used

Live adoption signals:

<table><thead><tr><th width="159.80535888671875">Signal</th><th>What it shows</th></tr></thead><tbody><tr><td>Usage</td><td>Active users, new users, repeat percentage.</td></tr><tr><td>Performance</td><td>Query count and average query time, with trends.</td></tr><tr><td>Top users</td><td>The users running the most queries.</td></tr><tr><td>Perspectives</td><td>Saved query views built on the product, with authors.</td></tr></tbody></table>

### Contacts

Who's responsible for the product and how to reach them.

![Contact with section showing owner and team members with email addresses](/files/VBK7iaxEUYszACtyfLBV)

## Checklist

| Area                    | What to check                                                 | Why                                                                    |
| ----------------------- | ------------------------------------------------------------- | ---------------------------------------------------------------------- |
| Purpose and description | Read the summary and intended use.                            | Confirms the product matches your goal.                                |
| Right fit               | Review Good for, Not good for, and Caveats.                   | Rules the product in or out fast.                                      |
| Ownership               | Check owner, maintainers, and members in Contacts.            | Tells you who to contact.                                              |
| Assets                  | Review inputs, models, semantic models, metrics, and lineage. | Shows how the product is built and what it exposes.                    |
| Trust and freshness     | Check quality score, freshness, and AI-readiness.             | Confirms the data can be trusted.                                      |
| Activity                | Review recent runs and plan history.                          | Shows whether the product is actively maintained and recently changed. |

## You're done when

You know what the data product provides, you trust the signals it shows, and you can decide whether to query or consume it.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://v2.dataos.info/consume/understand/about.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.