What are metrics in business intelligence?
Metrics in BI (business intelligence) refer to the quantitative measurements used to evaluate and analyze the performance of a business. These metrics are calculations that are used to support a variety of use cases, including financial performance, customer behavior, operational efficiency, and more.
Examples of common metrics used in business intelligence include:
- Revenue: Tracks the total income generated by a business over a period of time.
- Customer retention: Measures the percentage of customers who continue to use a business's products or services over time.
- Net Promoter Score (NPS): Gauges customer satisfaction and loyalty by asking customers how likely they are to recommend a business to others.
- Conversion rate: Measures the percentage of website visitors who complete a desired action, such as making a purchase or filling out a form.
- Cost per acquisition (CPA): Calculates the cost of acquiring a new customer.
- Return on Investment (ROI): Calculates the financial return on a particular investment, such as a marketing campaign or product development initiative.
By tracking and analyzing these metrics, businesses can gain valuable insights into their performance and make data-driven decisions to improve their operations, better serve their customers, and increase their profitability.
Metrics in Sigma provide a way to ensure consistent metric logic across tables, visualizations, and pivot tables.
Why should we use metrics?
Metrics help avoid problems that can be created when developers and users have different interpretations of how a value should be calculated. This can result in bad outcomes. For example:
Inconsistent analysis:
Analysts can define calculations differently. For example, one may define revenue as the sum of [Sales Price], where another may define it as the sum of [Sales Price] - [Discount]. These differences lead to inconsistent results and a lack of trust in the data.
Repetitive work:
Common business logic for a calculation like profit margin needs to be used all over the place. Today in Sigma, analysts have to derive the definition from scratch in every new workbook. This is a lot of repetitive, tedious work.
What are some of the benefits of metrics in general?
Metrics promote accurate, consistent, and governed analysis amongst all your analysts. It allows them to create and update one configuration that will be reused throughout the organization.
Incorporating common business calculations allows your business users to save substantial time trying to gather the necessary information and technical knowledge to create a calculation independently.
To put it simply: they drive better business decisions and outcomes.
What are some of the benefits of metrics in Sigma?
Metrics in Sigma are defined at the dataset level, one-time, and reused everywhere that dataset is used.
You are able to use the full power of Sigma's formula language to create very complex calculations that are used simply by name.
Sigma Metrics provide referential integrity: Changes to the definition will trigger updates everywhere they are used, automatically.
It also includes granular access control through dataset permissions to prevent end users from changing calculations.
How do metrics affect very large datasets?
They don't. If your dataset is very large (i.e.: millions or billions of rows), you will want to leverage Materialization to optimize performance. The metrics are part of the dataset.
Materializations allow you to write datasets and workbook elements back to your warehouse as tables which can reduce compute costs. Materialization enhances query performance by allowing your data warehouse to avoid recomputing the dataset when it's used by an element or in a descendant Sigma analysis.
Learn more about Materialization.
How do you create Metrics in Sigma?
Adding new Metrics is very simple. All that is required is to provide a name and formula. The description can be very useful, but it is optional.
For example, a simple metric might be:
Learn more about creating Metrics.
Are we limited in what function a Metric can include?
The only limitation is that it must use a supported Sigma function. The list of available functions is here.
You can get really creative with Metrics and Functions. For example, you might want to calculate the number of sales opportunities that are “new accounts”, over $25,000, closed and having reps that are part of a specific team, coming from a text file.
It might look like this:
How do business users use metrics in Sigma?
First a user will create a table, visualization or pivot table in Sigma against a Dataset that contains metrics.
The user only needs to drag the metric to a calculation panel to have that metric be included.
For example, all of the metrics in #1 are dragged to #2 to become part of the grouped table shown in #3.
This saves the end user tons of time and ensures the calculations are correct.
Do existing customers have to pay for this feature?
Nope! Because Sigma is built for the cloud since day one, we are constantly adding new features and much faster than legacy BI tools can. Metrics is just another example of this practice in action. Ask your Sigma Account Executive about Metrics today and learn how you can use it to drive efficiencies and consistency in your business today.
See how easy it is to leverage Metrics.
