Creating a Data Model (CX)

About Creating a Data Model

The data modeler can be used to create datasets where you flexibly combine data from multiple types of sources. The data modeler has nearly all of the features the data mapper does, but with some additional advanced functionality, such as left outer data joins. The data modeler also makes it easier than ever to map not just survey data, but data from your directory, tickets, and other data sources, while allowing you to combine them in the same dataset.

Qtip: Confused about what datasets are vs. data sources? Not sure how to tell the data mapper and modeler apart? See Key Terms for more information.

Incompatible Features

The following features aren’t compatible with data models yet:

• Page templates
• Date time segmentation
• Spotlight Insights
• Org hierarchies
• XM Respondent Funnel page templates (XM Respondent Funnel dataset is compatible)
• Distribution reporting

Creating a Dataset with the Data Modeler

Create a new dataset.

Under Type, select Data modeler.

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Name your dataset.

Click Create dataset.

Click Add Source.

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Under Source Data, choose a survey, ticket set, directory, or another external data source. Use the search bar and All source types drop-down to find a source.

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Qtip: Only tickets created from surveys can be mapped with the data modeler. You need to be either the owner of the associated survey or a collaborator with both Edit and View reports access.

Attention: Only projects you have access to will appear in this list. If any projects aren’t showing up, make sure you have been invited to collaborate on them.

Attention: Legacy distribution reporting is not compatible with the data modeler.

When you’re ready to select a source, adjust the Quantity to 1 (or more, if desired).

Qtip: It can be useful to add the same source more than once if you plan to perform separate unions and joins in your dataset. You cannot create a union or a join with multiple copies of the same source.

Select all the fields you want to include in your dataset. You can click on different types of data, like “Metadata”, to see only those fields, or use the search bar to search for specific fields.

Qtip: When selecting fields to include in your data model, think about which fields will be important when analyzing your data. Fields that are commonly included are survey questions, important metadata, and the field you plan on using to join your datasets.

Add more data sources, or perform joins and unions. See the linked pages for more details.

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Qtip: If you want to report on 2 different surveys with 2 years worth of feedback data, you’ll most likely use a union. If you want to create a list of all of the customers who reached out across those years, you’d use a join.

Once you’ve finished, click the plus sign ( + ).

Select Output Data Set.

Check the Dataset Editor for accuracy. Here, you can find all of your data fields.

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Qtip: If needed, you can always edit these fields later.

Qtip: Click Close to finish previewing your output dataset.

Select Publish.

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Qtip: Publishing will take you back to the Data page. See how to add your dataset to a dashboard.

Types of Sources Available in Data Models

A dataset gets its data from specific sources in Qualtrics. Data sources are often surveys, but there are many other sources you can add to data models. The following source types are available:

• DX Sources: Sessions from Digital Experience Analytics.

• Directories: XM Directory contacts.

  

  Qtip: Directory data requires additional setup before it can be used in your data models. See Enabling the Contact Dataset.

• External Datasources: Everything that doesn’t fit into the other categories listed here, such as imported data projects, segment membership data, and some older reputation management projects.

  

  Qtip: Segment data requires additional setup before it can be used in your data models. See Using Segment Data in Dashboards.

• Locations: Location directories.
• Reviews: Reputation Management programs.
• Surveys: Survey projects.

• Tickets: Tickets created from a tickets task in a survey. This does not include all tickets in the brand.

  

  Qtip: Only tickets created from surveys can be mapped with the data modeler. You need to be either the owner of the associated survey or a collaborator with Edit or View reports.

Attention: You can only use projects you have access to as data sources. If any projects aren’t showing up, make sure you have been invited to collaborate on them.

Qtip: Not all Qualtrics projects can be used as data sources in CX data models. For example, while surveys are compatible, Engagement projects are not. If you’d like to create data models with employee project data, see Employee Journey Analytics instead.

Recommended Edits

Building a data model often means combining multiple types of data – such as customer surveys, support tickets, interaction logs, and distribution data – and knowing how to structure those connections correctly can be complex. The data modeler simplifies this process by analyzing your selected data sources and suggesting the most logical next step.

Recommendations appear automatically as you work. When one is available, simply click it to apply it. The node will be added and pre-configured – including any relevant join keys or filter logic – so you can move forward quickly without having to set everything up manually.

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Recommendations are generated based on the combination of sources you’ve added to your data model. Recommendations update dynamically as your model changes, so if you add a new node, remove a node, or modify a node, the current recommendation may change. The data modeler even recognizes common use cases, such as joining survey data to support tickets, or unioning multiple survey types.

Qtip: For the best experience, add all the data sources you need at the very beginning. The more context the data modeler has, the more accurate and complete its recommendations will be.

Examples of Common Recommendations

The data modeler is designed to recognize specific data combinations. Below are examples of when recommendations will appear and what suggestions you’ll see.

1. Simple Datasets (Single Source)

If your model contains only one data source, the data modeler will immediately suggest an Output node. Click the recommendation to create a simple, single-source dataset in one step.

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2. Support Tickets and Surveys

When you are combining support ticket data with survey responses, the data modeler will guide you through the process based on how many sources are involved.

• One ticket source and one survey source: The data modeler suggests a Join with the join key pre-configured, which you can edit as needed.

Example: If you have a ticket source and a survey source added, a join will be suggested where the logic matches Record ID (survey join key) to Ticket Response ID (corresponding ticket join key). 

• One ticket source and multiple survey sources: The data modeler first suggests a Union to group all your surveys together, before suggesting you join the union to your tickets.

After all transformations are complete, the data modeler will suggest an Output node to finalize the dataset.

3. Surveys and Voice/Chat Interactions

If your model includes survey data alongside interactions, such as voice or chat sources:

• The data model will suggest a Union to combine the survey and the interaction data.

• If you then add funnel data, the data modeler will suggest a Join using Contact ID as the key.

Once your transformations are complete, the data modeler will suggest an Output node to complete the dataset.

4. Surveys and Distribution Data (Funnel)

When your model includes both survey data and a funnel source:

• The data modeler first suggests a Filter on your funnel data. This ensures your distribution data only includes records that actually match the surveys in your workflow.

• Next, the data modeler suggests a Join to attach the filtered data to your survey data.

After the join is complete, the data modeler will suggest an Output node to finalize the dataset.

Continuous vs. Periodic Dataset Updates

For the most part, data model datasets update continuously. That means that as you collect more data in your sources, your dashboard receives that data accordingly, including all unions and joins and field remappings. Continuous updates ensure your dashboard’s data stays current.

However, certain configurations in your data model can make it update periodically instead (for example, if your model is joined on a multi-value text set). Periodic updates exist to lighten the load on your dataset and ensure accuracy when particularly complex setups are involved. However, to get everything correct, the updates are slower.

Periodic datasets generally update with new data weekly. Periodic datasets cannot receive new data before the weekly update, even after recaching. This schedule cannot be customized.

Qtip: To see the specific date and time a periodic data model will update, publish your changes. Hover over the clock that appears to see an estimate.

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Identifying Features that Need Periodic Updates

Attention: Inner and full outer joins are currently in development, and are not yet available to all customers. Qualtrics may, in its sole discretion and without liability, change the timing of any product feature rollout, change the functionality for any in preview or in development product feature, or choose not to release a product feature or functionality for any reason or for no reason. If you’re interested in trying this feature, please reach out to Account Services.

Here are a few examples of features that cause a dataset to become periodic instead of continuous:

• Inner joins
• Outer joins
• Aggregations
• Chained joins

Qtip: You can currently only aggregate rows in CrossXM and Employee Journey Analytics. Aggregations are not available in CX Dashboard data models.

This list is not exhaustive. If your dataset updates periodically instead of continuously, there are a few places you can look in the data modeler to find this out:

Example: As soon as you finish a change that switches the dataset to periodic, this message appears in the information icon next to the title.

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Example: This message appears in the output dataset.

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Dashboards You Can Add Data Models To

Data models can only be added to the following types of dashboards:

• Dashboards projects (also known as CX Dashboards)
• Dashboards added to programs
• Dashboards built into certain XM Solutions
• Brand Experience

See Dashboard Data for steps to add your completed data model to your dashboard.

This feature cannot be used with any Employee Experience dashboards or with Results Dashboards. For a similar Employee Experience feature, see Data Models.