Measures¶

Measures are the numerical values you want to calculate from your data. They always involve aggregation—counting, summing, averaging, or finding min/max values. When someone asks "How many customers?" or "What's total revenue?", they're asking for measures.

How Measures Work¶

Unlike dimensions (which you filter or group by), measures require an aggregation function. You can't just display a measure—you calculate it across a set of records.

Common aggregation functions:

• COUNT() - Count records
• COUNT(DISTINCT) - Count unique values
• SUM() - Add up values
• AVG() - Calculate average
• MIN() / MAX() - Find minimum or maximum

Creating a Measure¶

When you add a measure to a model, you'll configure these fields:

Measure Name¶

A clear identifier that describes what this measure calculates.

Examples: total_customers, total_revenue, average_order_value

Use names that make it obvious what you're counting or summing.

Description¶

Explain what this measure calculates and what it represents.

Example: "Total count of unique customers" or "Average order value across all transactions"

This helps both your team and AI agents understand what the measure means.

Decimals¶

Specify how many decimal places to show in results. Use the up/down arrows to adjust:

• 0 for whole numbers (e.g., counts of users: "127")
• 2 for currency or percentages (e.g., "1,234.56" or "45.32%")
• Higher values for precise scientific calculations

Unit¶

Select the unit that describes what this measure represents. This ensures agents present results correctly and helps prevent confusion.

Common units:

• Counts: customers, orders, items, products, users, transactions
• Currency: EUR, USD, GBP, JPY
• Percentages: percent, rate, ratio
• Time: seconds, minutes, hours, days
• Other: bytes, KB, MB, GB

Example: If you're counting customers, select customers as the unit. The result might display as "1,247 customers".

SQL Expression¶

The calculation logic that defines what this measure computes. This is where you specify the aggregation function.

Examples:

• COUNT(DISTINCT(id)) - Count unique IDs
• COUNT(*) - Count all records
• SUM(revenue) - Sum up revenue values
• AVG(duration) - Average duration

Sample Value¶

After you create a measure, Actian AI Analyst shows a sample calculation result. This helps you verify the measure is working correctly.

Example: "1,247 customers" indicates your measure is counting successfully.

Common Measure Patterns¶

Counting Records¶

Count how many records exist:

COUNT(*)

Counting Unique Values¶

Count distinct values (e.g., unique customers):

COUNT(DISTINCT(customer_id))

Summing Values¶

Add up numerical values (e.g., total revenue):

SUM(order_amount)

Calculating Averages¶

Find the average of a numerical field:

AVG(order_value)

Measures vs Dimensions¶

Measures = Numbers you calculate (requires aggregation like COUNT, SUM, AVG) Dimensions = Attributes you filter or group by (no aggregation)

Example:

• "Show me total revenue (measure) by customer (dimension)"
• "Count number of orders (measure) by month (dimension)"

Building Blocks for Metrics¶

Measures are the foundation for more complex calculations called Metrics. While a measure performs a single aggregation, a metric can combine multiple measures with business logic.

Learn more about Metrics →