**MD file:** `Metadata.md`  
**Estimated reading time:** 9–11 minutes

# Metadata

## Definition

**Metadata** is data that describes other data.

It provides context needed to understand, discover, govern, and operate datasets.

## Simple Mental Model

> **Data is the book. Metadata is the title, author, table of contents, publication date, and description on the back.**

## How It Works

Metadata can be grouped into several categories.

### Technical Metadata
Table names, columns, data types, keys, schemas.

### Business Metadata
Business definitions, owners, descriptions, certified use.

### Operational Metadata
Refresh time, pipeline duration, row counts, success/failure.

### Governance Metadata
Owner, steward, classification, retention, access rules.

## Example

Without metadata:

```text
tbl_vw_agg_final_v2
dt
uid
cnt
sec
src
```

With metadata:

```text
Name: Daily Viewing Aggregate
Purpose: Daily content consumption reporting
Source: JW Player viewing events
Grain: One row per user/content/day
Owner: Audience team
Refresh: Daily 05:00
sec: Total valid watch seconds
```

## How It Fits Into the Bigger Picture

[[Data Lineage]] uses metadata to understand relationships.

[[Data Governance]] uses metadata to record ownership and classification.

[[Data Quality]] uses metadata to record rules and results.

[[Data Catalog]] makes metadata searchable.

## My Company / Real-World Context

Useful metadata could identify which audience dataset is certified, who owns it, how often it refreshes, and which source systems feed it.

## CTO Perspective

A mature organization moves knowledge from:

```text
People's heads
      ↓
Documented metadata
      ↓
Searchable organizational knowledge
```

The useful question is:

> **Is the metadata complete, current, discoverable, and actually used?**

### Questions to Ask

- Who owns this dataset?
- What business purpose does it serve?
- What is its [[Grain]]?
- Where does it come from?
- How often is it refreshed?
- Is it certified for business use?
- Can users discover this information without asking the original developer?

## Meeting Scenario

**Situation:** A new analyst finds four datasets that appear to contain audience information.

**Possible response:**

> "If the only way to know which dataset is trusted is tribal knowledge, that's a scalability problem. Let's make the owner, purpose, status, grain, source, and refresh information discoverable as metadata."

## Key Takeaways

- Metadata is data about data.
- Technical metadata explains structure.
- Business metadata explains meaning.
- Operational metadata explains processing and freshness.
- Governance metadata explains ownership and controls.
- [[Data Catalog]] makes metadata discoverable.

## Related Concepts

- [[Data Catalog]]
- [[Data Governance]]
- [[Data Lineage]]
- [[Data Quality]]
- [[Business Glossary]]
- [[Data Owner]]
- [[Data Steward]]
- [[Grain]]