High cardinality fields are data fields that contain a large number of unique values. Examples include user IDs, email addresses, transaction IDs, device identifiers, or URLs.

In contrast, low cardinality fields have relatively few unique values, such as country, gender, or status.

High cardinality fields matter in BI and analytics because they impact:

• Query performance

• Storage efficiency

• Visualization usability

• Indexing strategies

For example, grouping data by “user_id” (millions of values) is far more expensive than grouping by “country” (dozens of values). High cardinality fields can slow down dashboards, increase memory usage, and make charts unreadable.

From a visualization standpoint, high cardinality fields are rarely suitable for charts like bar graphs or pie charts. Instead, they are often used for:

• Filtering

• Drill-through

• Detail tables

• Backend joins

Technically, analytics systems must handle high cardinality carefully. Columnar storage, compression, and indexing techniques help manage performance. Some BI tools limit the use of high cardinality fields in visuals to prevent performance issues.

High cardinality fields are still essential. They enable detailed analysis, personalization, and user-level tracking. The key is to use them appropriately — for detail-level exploration rather than high-level summaries.

Understanding cardinality helps analysts design better models, faster dashboards, and clearer insights.