Data is a critical asset for businesses, but it can also be complex and difficult to navigate. Sigma Computing’s Metrics are a powerful tool for simplifying this complexity and making it easier for business users to access and understand data.
In this blog, we will explore what Metrics are, how they work, and why they should be used in data modeling.
Note: Metrics are currently in beta, with some improvements on their current roadmap. This article covers the functionality of Metrics as of the written date.
What Are Metrics From Sigma?
Metrics are Sigma’s semantic layer, which is a layer between raw data ingestion and data analysis for defining custom, reusable, aggregation calculations. A Metric’s syntax is defined at the data source level and can be used at any level of grouping in a workbook. Metrics can help organizations simplify and standardize their analysis by pre-determining common or atypical calculations.
How To Create Metrics
Metrics can be created at any data source level, which currently means within a dataset or database connection. To create a Metric, navigate to a table or dataset, enter edit mode, click on the Metrics tab, then click Add a Metric.
Next, add a Name, Description, and Formula to create a Metric. Metrics use Sigma’s formula language, so almost any functions available in a workbook are available for a Metric. Then click Publish to save the Metric in this data source.
How to Use Metrics
To use a Metric, connect a data element to the data source containing the Metric. Since Metics are aggregate calculations, add a dimension to a grouping in the table element. Next, drag the metric into the groupings’ calculation. Metrics will be displayed in a separate Metrics tab in the lefthand element properties.
Metrics can be used and reused like other aggregate calculations. For example, in the image below, the current month’s count is displayed at multiple levels or multiple group by statements.
Note: Metrics are identified in the formula bar using the uppercase Sigma symbol ( Σ )
Why Use Metrics
Metrics and semantic layers are a critical component of a data model because they provide a simplified view of complex data, making it easier for business users to understand and access data. Here are some reasons you should consider using Metrics in your data models:
- Simplified Data Access: Metrics provide a simplified view of complex data, increasing user adoption by providing human-centered labeled calculations.
- Improved Data Quality: Metrics can be used to enforce data quality rules, ensuring that formulas are consistent and accurate across the organization.
- Business-Focused View of Data: Metrics are designed to present data in a way that is relevant to the business, rather than the technical details of the underlying data structures.
- Reduced Maintenance Costs: By providing a single, centralized source for creation, organizations can save time when changes need to be made across multiple workbooks.
In summary, Sigma Computing’s Metrics provide a powerful solution to the challenge of navigating complex business data. This blog offers an in-depth overview of what Metrics are, how they function, and why they should be incorporated into data modeling.
By simplifying data access and improving understanding for business users, Metrics can enhance decision-making and accelerate insights. If you’re looking for ways to optimize your data strategy and empower your team or how to replicate other platforms’ semantic layers, contact us to learn more about how Sigma Computing’s Metrics can help.
Frequently Asked Questions
At the time of writing, Metrics can only be created at a data source level (in a Dataset or Database Table Connection). Creating a Metric in a workbook is on Sigma’s roadmap.
To create a Metic on a Dataset, a user must have been granted the “Edit Dataset” permission for their account and be the owner or granted “Can Edit” permission for the dataset. To learn more about User Permissions, check out this blog: How to Create Custom Account Types in Sigma Computing