Creating a dataset in Salesforce CRM Analytics (formerly Einstein Analytics or Tableau CRM) is a fundamental step for transforming raw data into actionable insights, enabling robust reporting and powerful dashboards. This process allows you to extract, transform, and load data from your Salesforce organization directly into the analytics platform for advanced analysis.
Understanding Salesforce CRM Analytics Datasets
A dataset in CRM Analytics is essentially a collection of denormalized, optimized data that can be queried rapidly to power interactive dashboards, lenses, and stories. Unlike standard Salesforce reports that query live transactional data, CRM Analytics datasets are pre-processed and stored separately, offering superior performance for complex analytical queries. They are crucial for discovering trends, patterns, and insights that might be difficult to uncover with traditional reporting.
Step-by-Step Guide to Creating a Salesforce Dataset
To create a new dataset directly from your Salesforce data within CRM Analytics, follow these straightforward steps:
- Access the Analytics App: Begin by navigating to the Analytics App in Salesforce. You can typically find this by using the App Launcher and searching for "Analytics Studio" or "CRM Analytics Studio."
- Initiate Dataset Creation: Within the Analytics App, click the "Create" button, usually located on the right side of the page. From the dropdown menu, select "Dataset".
- Choose Your Data Source: When prompted to select where your data lives, click "Salesforce". This indicates that you wish to import data directly from your Salesforce organization.
- Name Your Dataset: Provide a meaningful "Name Dataset" for your new dataset. Choose a name that clearly indicates the data it contains, making it easy to identify later.
- Select Source Objects: You will then "Select Object" to choose the primary Salesforce object you want to use (e.g., Account, Opportunity, Case).
- Define Fields for Import: To add fields from related objects, hover over the Object Selection area and click the "+" icon. This allows you to join data from related objects. Choose the specific fields you want to import data from both the primary and related objects. Be selective to include only necessary fields, as this impacts performance and storage.
- Finalize and Configure: After selecting all desired objects and fields, click "Next". You may be prompted for additional configuration, such as setting up security predicates or initial dataflow options. Follow any subsequent prompts to complete the setup.
Once these steps are completed, CRM Analytics will initiate the process of extracting the specified data and creating your new dataset. This dataset will then be available for building lenses, dashboards, and stories.
Key Considerations for Dataset Creation
Creating effective datasets involves more than just selecting fields. Here are some critical points to keep in mind:
| Feature | Description | Best Practice |
|---|---|---|
| Data Volume | The amount of data you're importing can impact processing time and storage. | Only include fields and records essential for analysis. Consider filtering data at the source if possible. |
| Data Security | Control who can see which data within the dataset. | Implement row-level security (Security Predicates) to restrict access based on user profiles or roles. Learn more about CRM Analytics Security |
| Performance | Large, complex datasets can be slower to query. | Optimize by flattening data, pre-aggregating, and choosing appropriate data types. Avoid excessive joins if possible. |
| Naming Conventions | Consistent naming helps with organization and collaboration. | Use clear, descriptive names for datasets, fields, and labels (e.g., Sales_Opportunities_FY23). |
| Data Type Mapping | Ensure Salesforce field types map correctly to CRM Analytics data types for accurate analysis. | Verify data type assignments during dataset creation. |
Alternative Methods for Data Integration
While directly creating a dataset from Salesforce objects is common, CRM Analytics offers more advanced tools for data preparation and integration:
- Dataflows: For complex transformations, merging data from multiple sources (both Salesforce and external), and scheduling data refreshes, Dataflows are powerful tools. They allow you to prepare your data before it becomes a dataset. Explore Dataflows in Salesforce Help.
- Recipes: Data Prep Recipes offer a more visual and intuitive interface for cleaning, transforming, and combining data. They are ideal for users who prefer a graphical approach to data preparation. Discover Data Prep Recipes.
- Connectors: CRM Analytics can connect to various external data sources (e.g., Amazon S3, Google BigQuery, external databases) to bring data into your analytical environment.
What Happens After Dataset Creation?
Once your dataset is successfully created and has completed its initial data sync, it becomes available in CRM Analytics Studio. You can then:
- Create Lenses: Explore your data interactively by creating lenses, which are specific views of a dataset.
- Build Dashboards: Design compelling dashboards that visualize your data with charts, tables, and key performance indicators (KPIs).
- Develop Stories: Use Einstein Discovery to build stories that uncover insights, predict outcomes, and recommend actions based on your dataset.
By leveraging these powerful features, you can turn your raw Salesforce data into strategic intelligence, driving better business decisions.
