Member

In depth

In business intelligence and data analytics, we organize data into dimensions—logical groupings such as Product, Region, or Customer. Each dimension contains many members.

At a technical level, dimensions often correspond to lookup tables in a data warehouse. Each row in that table is a member. For example, a Customer dimension table might list every customer by a unique identifier and a name. Every row (or member) in that table represents one customer that you can slice your metrics by.

Members serve two critical roles. First, they provide the context for your measures (metrics). When you view revenue by region, you’re really summing a revenue measure for each region member (like "North America" or "EMEA"). Second, members drive filtering, grouping, and drilling in dashboards. You filter on a member, and the dashboard dynamically updates to show only that slice of your data.

High-cardinality dimensions—those with thousands or millions of members—require thoughtful design. Too many members can slow down queries and overwhelm end users. That’s why dimension tables sometimes include surrogate keys, parent/child hierarchies, and aggregated members (for example, grouping “California,” “Texas,” and “New York” into the “USA” parent member).

Pro tip

When you have a dimension with hundreds or thousands of members, use hierarchies or tagging to group similar members. This reduces clutter in your dashboards and improves performance. Read more about best-practices here.

Why it matters

• Accurate slicing: Members define how you break down your metrics. Without the right members, your visualizations can mislead or hide critical insights.

• Consistency and trust: Standardized members ensure everyone in your organization refers to the same entities. This builds confidence in your reports.

• Performance: Well-designed dimensions with curated members help your analytics platform run faster and more efficiently.

Members - In practice

1. Filtering dashboards: In Klipfolio PowerMetrics, click a region member (for example, “EMEA”). Instantly, all charts and tables update to show only data for that region.

2. Building tables: Drag a Product dimension to the rows of a table. Each member (like “Blue T-Shirt”) appears as a row alongside its metrics (units sold, revenue, margin).

3. Drilling down: Start at a high-level member, such as “Europe.” Drill down into child members like “Germany” or “France” to uncover more detailed insights.

4. Custom views: Create a saved view that only includes starred members—say, your top 10 best-selling products—and share it with your team for focused discussions.

Product-specific notes

In PowerMetrics, individual member entries are exposed when segmenting on a dimension, or if a filter is applied to include or exclude certain members.