In this chapter, you'll learn about the core use cases for data models in Sigma. Using a data model, you can create custom data sources that are available to other Sigma documents in your organization. This will enable you to scale, govern, and maintain your team's insights, analytics, and apps.

What is a data model?

Data models vs workbooks

Centralizing KPI updates

Joins and unions in a data model

Combine two tables with a union

Building a data model base table

Combine two tables with a join

Publishing and using a data model

Using a data model in a workbook

Supporting sustainable insights

In this chapter, you’ll learn how to scale insights outside a single table using metrics and relationships.  You'll build an example of each of these core data model features, and then see them in action in a workbook, so you can understand the impact first-hand. After learning about these features, you'll be able to provide calculations across an analytics team, and control join logic centrally from a data model while still offering flexibility to users.

Define core calculations with metrics

Create a source of truth for a KPI

Metrics vs. calculated columns

Enrich a data model by adding a metric

Creating your first metric

Use a metric in a workbook

Manage joins efficiently with relationships

Relationships vs. joins

Relationship types

Add columns to a base table with a relationship

Adding a relationship between loans and customers

Exploring relationships in the workbook

Enriching tables with metrics and relationships

In this chapter, you'll learn about two advanced data model features (parameters and column security) before carrying on to implement everything you've learned in one final example. Parameters will give you the ability to configure flexible filters on your models,  and security will help you keep sensitive data safe. Then, by combining all the features and best practices you've learned up to this point, you'll get a chance to cement your mastery of scalable analytics.

Filtering data with data model parameters

Parameters vs. filters

Add a parameter to a data model

Using column security to govern sensitive data

Add column security to a data model

Support your team with a fully-featured data model

Finish a data model from a specification

Prototype analytics

Conclusion

Bringing it all together with parameters and security

Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models. In this course, you’ll learn the when and why of data models in Sigma, understanding their unique abilities to increase performance, standardize calculations across an organization, and govern sensitive information.

We’ll explore unions, joins, relationships, metrics, parameters, and column-level security to create and share solid building blocks for analysis.

By the end of this course, you’ll know when to make a data model, and which features you’ll need to fit your use case. No more ad hoc joins, and no more user confusion.

Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.

Data Modeling

Data Modeling in Sigma

Sigma Fundamentals

Metrics vs. calculated columns

Data Modeling in Sigma

Hands-on interactive exercise