Add Einstein Discovery Predictions to your flow

Supported in Tableau Prep Builder version 2021.1.3 and later. Flows that include prediction steps are not currently supported on Tableau Server or Tableau Online.

Use Einstein Discovery-powered models to bulk score predictions for the data in your flow. Predictions can help you make better-informed decisions and take actions to improve your business outcomes.

When applying these models, a new field for predicted outcomes (in the form of probability scores or estimated averages) is automatically added to your flow. Optionally, you can include top predictors (factors that contributed most significantly to the prediction) and improvements (suggested actions to take to improve the predicted outcome). If you add these, fields for these factors are included in your flow data as well.

For example, to predict employee retention, you could build a model using historical data (where you already know the outcome) in Einstein Discovery, then apply that model to the data set in your flow in Tableau Prep Builder and generate the predicted outcome. Prediction results are applied at the row-level, helping you dive deep into your analysis in Tableau.

If you need to apply multiple models to your data set, you can include multiple prediction steps in your flow. Each prediction step applies a single prediction model to the flow. Signing into Salesforce to apply models using different credentials in a single flow is not supported.

Note: You must have a Salesforce license and user account that is configured to access Einstein Discovery to use this feature. See Prerequisites for more information.

What is Einstein Discovery?

Einstein Discovery augments your business intelligence with statistical modeling and supervised machine learning to identify, surface, and visualize insights into your business data. It quickly sifts through millions of rows of data to find important correlations, predict outcomes, and suggest ways to improve those predicted outcomes.

For more information about Einstein Discovery, see Getting Started with Discovery(Link opens in a new window), and Explain, Predict, and Take Action with Einstein Discovery(Link opens in a new window) in Salesforce help. You can also expand your knowledge with the Gain Insight with Einstein Discovery(Link opens in a new window) trail in Trailhead(Link opens in a new window).

Note: Einstein Discovery in Tableau is powered by salesforce.com(Link opens in a new window). Consult your agreement with salesforce.com(Link opens in a new window) for applicable terms.

Prerequisites

To configure and use Einstein Discovery predictions in your flow, you need certain licenses, access, and permissions in Salesforce and Tableau Prep.

Salesforce Requirements

requirement description

Salesforce license

One of the following licenses:

  • Einstein Discovery in Tableau license
  • Tableau CRM Plus license
  • Einstein Predictions license

These licenses are available for an extra cost.

Salesforce user account

Account that is configured to access Einstein Discovery.

If you use the Einstein Discovery in Tableau license, your user account must have the View Einstein Discovery Recommendations Via Connect API system permission assigned to it.

If you use either the Tableau CRM Plus license or Einstein Predictions license:

  • To get predictions using already deployed Einstein Discovery models, the account must have the View Einstein Discovery Recommendations system permission assigned to it.
  • To build, deploy, and manage predictions in Einstein Discovery, the account must have the Manage Einstein Discovery permission assigned to it.

To configure user accounts, see Set Up Einstein Discovery(Link opens in a new window) in Salesforce help.

Tableau Prep Requirements

requirement description

Tableau Prep license and permissions

Creator license.

As a creator you need to be able to sign into the Salesforce org account to access prediction definitions and add models to your flow.

Add prediction data to your flow

Note: Flows that include prediction steps can currently only be run manually in Tableau Prep Builder.

To apply Einstein Discovery predictions to your flow, you will need:

  • Access to a Salesforce org and Tableau Prep Builder version 2021.1.3 or later.
  • An Einstein Discovery prediction model that is deployed in Salesforce.
  • Source data in Tableau Prep Builder with fields that match the model fields required by the Einstein Discovery prediction model.
  1. Open Tableau Prep Builder and connect to a data source.

  2. Apply any cleaning operations as needed.

  3. Click the plus icon, and select Prediction from the Add menu.

  4. In the Prediction pane on the Settings tab, click Connect to Einstein Discovery. You will need to sign in using your Salesforce credentials.

    When you click this button, a web page opens, asking you to sign in to your Salesforce account. After you sign in, a web page opens asking if you want to let Tableau access your Salesforce data. Click Allow to continue, and then close the resulting tab in your browser.

  5. Click Select Prediction Definition. This opens the list of deployed models that you have access to. The models are built and deployed in Salesforce using Einstein Discovery. For more information about predictive models see, About Models(Link opens in a new window) in Salesforce help.

  6. In the Prediction Definitions dialog, select the prediction definition that maps to your data set. To generate predicted outcomes using your flow data, all fields in the model must map to a corresponding flow field.

  7. In the Options section, select up to 3 top predictors and improvements to include in your flow data. This is supplemental data you can add to your flow.

    • Top predictors indicate which factors contributed the most to the predicted outcome.

    • Top improvements suggest actions to take to improve the predicted outcome.

  8. In the Map Fields section, map your flow fields to your model fields.

    • All model fields must be mapped to a corresponding flow field.

    • Field names that match exactly are automatically mapped.

    • You can't map the same flow field to multiple model fields.

    • Model and flow field data types must match.

      If your flow field is assigned to a different data type, you'll need to change it to match the data type assigned to the model field.

      To change the data type, in the Map Fields section, simply click the data type for the flow field, then select the new data type in the menu. You can then change the data type back in a subsequent cleaning step.

      For more information about changing data types, see Review the data types assigned to your data(Link opens in a new window).

  9. To apply your settings and run the model against your data, click Apply. The prediction results show in the profile pane and data grid.

    If you change any settings, you can click Apply again to re-run the model with your changes. If you leave the Prediction step before clicking Apply, the model won't run and your changes will be lost.

Reviewing your results

After you apply the predictive model to your flow data you can generate your flow output and use the new data source to analyze the predicted outcomes at the row level in Tableau. To understand the results of the prediction model, let's look at an example.

In this topic, we applied the Employee Retention Prediction model to our employee data in Tableau Prep Builder to get a probability score that an employee will stay with the company.

This gave us the following results:

Let's look at what these results tell us for Employee 2:

Question Prediction Where is this?
How likely will this employee stay? Einstein Discovery predicts that there is an 81.38% chance that they will stay. Prediction field
What factors impact this result? The years with the current manager reduces the chance that this employee will stay by 2.2%.

Predictor 1 field (top predictor)

Predictor 1 Impact (percent impact of the top predictor)

What can improve this predicted outcome? Increasing the employee's monthly rate between 4923 to 5725 increases the likelihood that the employee stays by 3.86%.

Improvement 1 field (top improvement)

Improvement 1 impact (percent impact of making the suggested change)

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