What is a data catalog?

A searchable inventory of data assets with metadata. Enables: finding data, understanding what it contains, knowing who owns it, and assessing if it's fit for purpose.

How do I collect metadata?

Automated: extract technical metadata from databases, data tools. Manual: add business context, descriptions, classifications. Combination is typical - automate what you can, curate what you must.

What metadata should I capture?

Minimum: schema, descriptions, ownership. Important: quality metrics, lineage, classifications. Nice to have: usage stats, ratings, relationships. Prioritise based on governance needs.

Who should maintain metadata?

Shared responsibility: technical metadata from data engineering, business metadata from domain experts, governance metadata from data stewards. Clear ownership prevents neglect.

Book Free Assessment Calculator

Metadata

Data that describes other data, providing context about structure, meaning, origin, and usage.

In-Depth Explanation

Metadata is "data about data" - information that describes characteristics of other data. It helps users and systems understand, find, and use data effectively.

Types of metadata:

Technical: Schema, data types, constraints
Operational: Created date, last updated, row counts
Business: Definitions, ownership, classifications
Social: Ratings, comments, usage frequency

Metadata examples:

Column names and data types
Table descriptions and purposes
Data owner and steward
Data quality scores
Access permissions
Transformation logic

Metadata management:

Data catalogs (central repository)
Metadata standards (naming conventions)
Automated collection
Manual curation for business context
Search and discovery

Why metadata matters:

Find relevant data (discovery)
Understand data meaning (context)
Ensure proper use (governance)
Assess data quality (trust)

Business Context

Good metadata makes data findable and usable. Without it, data becomes a liability rather than an asset.

How Clever Ops Uses This

We help Australian businesses implement metadata management, making data assets discoverable and trustworthy for AI applications.

Example Use Case

"A data catalog with metadata showing: table purpose, column definitions, data owner, update frequency, and quality score - helping analysts find and trust data."