Most data catalogs are built, populated once, and then forgotten.
When your data lives in Alation (catalog), Snowflake (warehouse), BigQuery (warehouse), and Databricks (processing), you can build a data catalog that your team actually uses—not just populates.
The 4 data catalog features that matter
1. Discovery
Effective discovery starts with search functionality that helps users find the data they need quickly. Browse capabilities organized by domain let teams explore data relevant to their work. Filtering by tags helps narrow down results to exactly what’s needed.
2. Documentation
Good documentation includes table descriptions that explain what each table contains and its purpose. Column descriptions clarify what each field means and how it should be interpreted. Usage examples show how to actually use the data, making it accessible to team members who aren’t familiar with it.
3. Lineage
Data lineage answers critical questions: Where did the data come from originally? How was it transformed through various processes? Where is it currently being used? This visibility helps teams understand data dependencies and trust the data they’re using.
4. Quality
Quality indicators show whether data is fresh by displaying when it was last updated. They reveal if data is complete by showing row counts and expected volumes. Most importantly, they provide quality scores that indicate data accuracy and reliability.
What to build first (week 1)
Start with a simple data catalog that auto-discovers tables by scanning Snowflake, BigQuery, and Databricks. Add basic documentation including table and column descriptions that explain what each dataset contains. Implement basic lineage tracking to show data flow from source to consumption. Finally, add basic quality indicators showing freshness and completeness metrics.
Once you have these fundamentals, add rich documentation with examples and use cases that help team members understand how to use the data. Expand lineage to show detailed transformations and dependencies. Add quality monitoring with ongoing checks that alert when data quality degrades. Finally, implement usage analytics to see who’s using what data, helping prioritize documentation efforts.
Why most data catalogs fail
Most data catalogs fail because they’re not discoverable, making it hard to find the data you need even when it exists. They’re not documented, lacking descriptions that explain what data means and how to use it. They’re not maintained, leaving stale information that becomes untrustworthy over time. Most critically, they’re not used because teams don’t check them when they need data.
When you build a data catalog right, you can find data easily through good search functionality. You can understand data because good documentation explains what it means. You can trust data because quality scores indicate reliability. Most importantly, you can use data effectively because examples and lineage show how it’s been used before.
The hidden cost of no data catalog
When there’s no data catalog, time is wasted searching for data that may or may not exist. Mistakes are made when teams use the wrong data because they can’t find the right dataset. Duplication happens as teams recreate datasets that already exist because they don’t know about them. Most critically, trust erodes because teams don’t know what data means or whether it’s reliable.
A good data catalog means time is saved because you can find data fast through effective search. Mistakes are avoided because you use the right data with clear documentation. Duplication is reduced because teams can discover and reuse existing datasets. Most importantly, trust is high because you understand what data means and can verify its quality.