About metrics
In this page you can find a high-level overview of metrics; what they are, why they're central to data analytics and what the main categories of metrics are.
What are metrics?
Metrics are a crucial tool in the transformation of raw data into meaningful information that provides actionable insights. They stand at the intersection of data sources and consumers, delivering data that reflects the reality decision-makers must navigate. To understand the concept of metrics, consider the following definition:
Metrics show Measures of Facts in context.
Facts capture real-world information in data. They represent transactions, objects, events, and more. Facts can be represented by entities with dimensions and relationships between them (read more about entities, dimensions and relationships in Sightfull).
An open sales opportunity is a fact.
Measures are aggregate descriptions of data. They are mathematical operations of facts that provide a result that summarize facts to quantify the real-world.
The number of all sales opportunities is a measure.
Metrics are measures in context, displaying how they change over time periods and compare across other dimensions. Metrics help visualize and understand the trends and composition of the underlying data, providing more meaningful insights than raw facts or flat measures. The metrics that are most critical to an organization are Key Performance Indicators (KPIs)
The number of sales opportunities created in each time period is a metric.
By putting measures in context, metrics make our analysis meanignful; allowing us to assess if the values are high or low, improving or declining. For example, comparing this quarter's sales to the previous quarter or analyzing customer engagement by region. Metrics can transform these comparative insights into a narrative that informs and directs business strategies.
Analytics with metrics
Metrics transform complex data into actionable insights, enhancing decision-making efficiency and aligning organizational teams around standardized KPI. They serve as a strategic asset in analytics, simplifying data interpretation and focusing efforts on areas that drive performance and business success.
Metrics make business intelligence:
More actionable
Metrics provide the necessary context to understand what the data means and therefore what action is required. They help spot the trends and patterns in data, paving the way for informed decision-making. Analysis with context is much more effective at delivering insights that truly inform business strategy.
More efficient
Metrics are the basic unit of analytics that follows the DRY principle (Don't Repeat Yourself). Metric calculation is defined once, then used where and when it's needed to answer both routine and ad hoc business questions. In this way, metrics save the large swaths of time wasted on manually querying the same data at different times, or slicing & dicing the same dataset by different dimensions.
More consistent
Metrics play a vital role in keeping data consistent in the organization. Well defined metrics provide a stable and agreed upon perspective on the data, keeping different teams aligned to the same view of their business reality. Metrics standardize Key Performance Indicators (KPIs), ensuring a cohesive understanding of performance across various departments. This common framework of evaluation keeps all team efforts aligned with the organization's overall objectives.
Metrics transform the way businesses interact with their data, turning it into a strategic asset that drives informed decisions, efficiency, and organizational alignment.
Types of metrics
Understanding the different types of metrics is crucial for effectively using metrics in data analysis and decision-making. Broadly, there are two main classes of metrics:
- Aggregate Metrics: directly relate to a subset of
entities(selected with Filters), along with any related data (through Joins), and return the aggergate result for eachperiod(according to the X-Axis configuration) - Formula Metrics: are derived by performing calculations on other metric results and return their sum, quotient or other product.
These classes in turn can be divided into different types:
Aggregate metrics
- Flow metrics: measure how something changes over time. They track dynamic activities or quantities that vary, such as opportunities created per quarter or website visitors per week.
- Stock metrics: represent a cumulative quantity at a specific point in time. They provide a snapshot of static conditions, like the total volume of open opportunities each month or inventory levels on a particular date.
Flow metric values "reset" at the start of each period, while Stock metrics reflect the current value at each point in time (typically the start or end of the period).
Formula metrics
- Ratio: Ratio metrics compare two different quantities to each other, captures rates or proportional comparisons. Common examples include opportunity win rate, stage progression rate or cost per lead (campaign cost divided by the number of sourced leads).
- Waterfall: Waterfall metrics present the components that contribute to - or detract from - a sum figure in each period. They are useful for understanding the composition or development of the total value in a given period. Popular examples of Waterfall metrics are the ARR Waterfall and the Quarterly Pipeline Waterfall which show how the Annualized Recurring Revenue (ARR) and Pipeline set to close in the quarter developed during the period, respectively.
Wrapping up
Metrics are crucial tools for effective and efficient data analytics; streamlining the transformation process of raw data into actionable insights. Our goal in this document was to clarify metrics' function and application, by defining what metrics are, their significance in analytics, and detailing their types - Aggregate (Flow and Stock) and Formula (Ratio and Waterfall). With this information, we encourage you to select and build the metrics that best benefit you and your team.
See this related documentation to learn more about the metric types or get started building your first metric.