Metric types
Understanding the variety of metric types in Sightfull is essential for users aiming to leverage the full potential of data analytics. This documentation distinguishes between two ways of classifying metrics into types, Structural and Functional, and how to choose the type of metric that best suits your needs.
Structural types
All metrics can be sorted into one of two Structual types: Aggregate or Formula.
In Sightfull, metrics either aggregate the raw data, or use a formula based on other metrics to calculate their results. The metric schema structure depends on which of these actions the metric performs.
Aggregate metrics are the foundation of your analytics system, capturing the essential patterns in your data. On top of this foundation, Formula metrics can add layers of increasingly more complex and precise analyses. Both Aggregate and Formula metrics are important for building a robust reporting and analytics stack.
Aggregate metrics
Aggregate metrics connect directly to entities defined in Semantic Models, performing calculations on measures and applying conditions on dimensions to extract patterns and insights.
The Aggregate metric schema has the following properties:
| Schema property | Description | Example |
|---|---|---|
name | The name of the metric. | bookings |
entity | The entity type associated with the metric. | opportunity |
filters | Optional SQL filters to select the subset of data used in the metric. | - sql: $is_won = true |
joins | Related entities required in the metric calculation. | See joins documentation |
operation | The mathematical operation to perform on the data. | sum |
measure | The value or expression used for calculating the metric. | $amount_in_usd |
x_axis | Configuration for calculating the metric value in each time period. | - sql: $close_date >= $period.start or -sql: $macros.PERIOD_IN($close_date) |
For example, the bookings metric is an Aggregate metric that measures the sum of opportunities that were closed-won during the time period:
- name: bookings
entity: opportunity
operation: sum
filters:
- sql: is_won = true
x_axis:
period:
-sql: $macros.PERIOD_IN($close_date)
measure: $amount_in_usd
See the X-axis documentation to learn more about using $macros to configure metric periods.
Formula metrics
Formula metrics are derived by performing calculations on other metrics. They use other metrics as variables in an equation to provide answers to higher-order analytical questions.
The Formula metric schema has the following properties:
| Schema property | Description | Example |
|---|---|---|
name | The name of the metric. | opportunity_win_rate |
entity | The entity type associated with the metric. | opportunity |
formula | The mathematical operation to perform on the data. | $metric__won_opportunities / $metric__closed_opportunities |
For example, the opportunity_win_rate metric is a Formula metric that measures the proportion of closed opportunities which were won in each period:
- name: opportunity_win_rate
entity: opportunity
formula: $metric__won_opportunities / $metric__closed_opportunities
Functional types
Functional types focus on the analytical objectives of metrics, representing different quantities and aspects of data for insightful analysis. The functional types are Flow, Stock, Ratio and Waterfall metrics:
Flow
Flow metrics are designed to measure dynamic quantities that change over time. They are key in tracking activities or quantities that vary over specified periods. A common use of Flow metrics is tracking the number of created opporortunities every quarter. This metric helps businesses monitor their sales trends, understand seasonal variations, and plan for future demand.
Stock
Stock metrics provide a snapshot of cumulative data at a specific point in time, reflecting static conditions or resources. Measuring the total amount of open opportunities in the pipeline each month is an excellent application of Stock metrics. It helps businesses understand their volume of open opportunities at a certain point in time, how it fluctuates and whether it is steadily increasing or decreasing.
Both Ratio and Waterfall functional types are created using the Formula schema structure.
Ratio
Ratio metrics compare two different quantities, offering insights into rates or proportional comparisons. The opportunity win rate is a vital Ratio metric, indicating the effectiveness of the sales process by comparing the number of won opportunities to total opportunities.
Carefully configure the components of Ratio metrics in order to compare the right quantities to ensure they provide meaningful and actionable insights.
Waterfall
Waterfall metrics break down a total figure into its contributing components over a period, useful in financial and operational analysis. The ARR Waterfall metric is used to understand the components contributing to the Annualized Recurring Revenue over time, such as new business, expansions, contractions, and churn.
Waterfall metrics are most effective when they cover all relevant components and are used in consistent time intervals for comparison.
Note on metric structure & function
Ratio and Waterfall metrics are Formula metrics in structure; using other metric values as components.
Flow and Stock metrics will most often be simple Aggregate metrics but can also be created by combining smaller components of the same type in a sum Formula.
Wrapping up
The understanding of Structural and Functional metric Types in Sightfull is key to harnessing the full potential of data analytics. These classifications give comprehensive view of business metrics, helping you construct your metric layer efficiently.