Conclusion

1. Conclusion

Good job! This is the end of the course. Let's review what we learned.

2. Chapter 1

In chapter one, we covered the use cases for a data model, understanding the role data models play in centralizing and governing data in a Sigma organization. We discussed the importance of curating a base table, and introduced joins and unions to help us build it.

3. Chapter 2

In chapter two, we discussed metrics and relationships. Metrics allowed us to centralize common calculations, and relationships gave us an option for combining data in use cases where a join isn't ideal.

4. Chapter 3

In chapter three, we added on parameters and column-level security, to more easily govern and filter our data. Then, we combined what we learned to make a fully-featured data model.

5. Congratulations!

We hope you enjoyed learning about data models, and can apply what you learned to effectively and scalably govern data for your Sigma users. Congratulations!

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This exercise is part of the course

Data Modeling in Sigma

BeginnerSkill Level

5.0+

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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.

Exercise 1: What is a data model?Exercise 2: Data models vs workbooks Exercise 3: Centralizing KPI updates Exercise 4: Joins and unions in a data model Exercise 5: Combine two tables with a union Exercise 6: Building a data model base table Exercise 7: Combine two tables with a join Exercise 8: Publishing and using a data model Exercise 9: Using a data model in a workbook

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.

Exercise 1: Define core calculations with metrics Exercise 2: Create a source of truth for a KPI Exercise 3: Metrics vs. calculated columns Exercise 4: Enrich a data model by adding a metric Exercise 5: Creating your first metric Exercise 6: Use a metric in a workbook Exercise 7: Manage joins efficiently with relationships Exercise 8: Relationships vs. joins Exercise 9: Relationship types Exercise 10: Add columns to a base table with a relationship Exercise 11: Adding a relationship between loans and customers Exercise 12: Exploring relationships in the workbook

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.

Exercise 1: Filtering data with data model parameters Exercise 2: Parameters vs. filters Exercise 3: Add a parameter to a data model Exercise 4: Using column security to govern sensitive data Exercise 5: Add column security to a data model Exercise 6: Support your team with a fully-featured data model Exercise 7: Finish a data model from a specification Exercise 8: Prototype analytics Exercise 9: Conclusion

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