Jennifer Lee in a new episode of Did You Know.

Integrate with Data Cloud and Keep Your Org Data Clean

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Lead Admin Evangelist Jennifer Lee demonstrates how admins can build solutions by integrating siloed systems with Data Cloud to connect and unify all of your data. Plus, learn how to keep your org data clean in preparation for AI innovations by implementing data cleanliness best practices. Watch the demo and then learn more below.


What Is Data Cloud?

You’ve heard Data Cloud mentioned in many Salesforce keynotes, presentations, and blogs, but what is it? Data Cloud is a hyperscaled data platform that stores all of your data from various systems with Salesforce using a data lake architecture. With all of your mapped data in one place, Data Cloud provides real-time access to harmonized data across the entire Customer 360. This way, for example, you don’t have multiple versions of Jennifer Lee (Jennifer W. Lee, Jen Lee, J.Lee, etc.), you have the one Jennifer Lee record. At Dreamforce ’23, Salesforce announced a free Data Cloud account for customers of Unlimited and Enterprise Editions.

The first step is to connect the data and ingest the associated data into Data Cloud. This is done through a Data Cloud data stream.

Data Cloud data streams.

Then, you map the data from the external system to its associated field in Salesforce.

A Contact Data Lake Object Loyalty Tier Level field mapped to the Tier Level field in the Salesforce Individual object.

From there, Data Cloud harmonizes data from these streams so external systems and Salesforce stay up to date. As a result, you get a complete, real-time 360-view of your customer in Salesforce, allowing you to create great experiences for your customers. You can also create Data Cloud related lists and add them to your record pages. Note: Data Cloud related lists were not shown in the demo video.

Data Cloud harmonized data brought together in a unified customer profile.

Use Data Cloud data in your automated processes

But, that’s not all! Just like with your Salesforce CRM data, you can use Data Cloud objects in your automation!

You can reference Data Cloud data in your automation to make informed, real-time decisions using a Get Records element. When Data Cloud is enabled in your org, you’ll see the Data Source selector in Flow Builder to specify whether your data source is a Data Cloud object or Salesforce object. Once you select Data Cloud Object as the data source, the Data Space and Object selector are shown. Note: To use Data Cloud objects in Flow, users need Read object permissions for the data model object (DMO) and Manage Flows permissions.

Get Records element in Flow to retrieve data from a Data Cloud object.

In a Data Cloud enabled org, you’ll see a new flow type called Data Cloud-Triggered Flow, allowing you to automate processes based on changes to Data Cloud objects. Data Cloud-triggered flows launch when the Data Cloud DMO or calculated insight object (CIO) conditions are met.

New flow type called Data Cloud-Triggered Flow appears in the New Flow creation screen in a Data Cloud enabled org.

This flow type is very similar to a record-triggered flow for Salesforce object record changes. In the below example, this Data Cloud flow is triggered anytime the Unified Individual object record is updated and the Tier Level is not the standard tier.

Example of a Data Cloud-triggered flow.

Note: While the functionality is available to create Data Cloud-triggered flows, I recommend holding off and not trying out the new, shiny object for now. Currently, there’s no ability to test this flow in a sandbox before deploying it for use in production. It’s an admin best practice to always build and test your automations in a sandbox first before deploying to production.

#ForwardLookingStatement: The product team is working on the ability to have Data Cloud sandboxes and be able to debug/test this type of flow in the future, possibly in the next release. So, until then, I recommend holding tight on implementing this new feature as we do not build and test automation directly in production.

Prepare your org for AI

Artificial intelligence (AI) innovations like Einstein can dramatically change the way your company manages customer relationships, but AI requires that your data be clean, updated, accessible, and complete. We recommend focusing on data management now and prepping your org for these AI innovations. Your data doesn’t need to be perfect, just clean. Validation rules, duplicate matching rules, picklists, and conducting data audits using reports and dashboards are a few ways to ensure your org data is clean.

You can also use Flow to ensure your users aren’t changing data they shouldn’t. And now, you can inform your users why they can’t update that data instead of them seeing those ugly error messages Flow sometimes shows that make no sense to your users.

Example of a non-user-friendly Flow error message.

Use a Custom Error element to configure a user-friendly error message to handle specific errors in your flow. You can configure it to show in a window on the record page or as an inline error. You can also configure multiple error messages, and use merge fields in the error message for dynamic error messages. This element comes in handy in fault paths.

Flow with configured Custom Error element.

This is an example of a Custom Error configured to show in a window on the record page.

Flow Custom Error configured to show in a window on the record page.

It’s such an exciting time with these new AI innovations that will boost your productivity and increase efficiency for your company. But, remember—they depend on clean, real-time data. Get your org prepared now by integrating all of your data in one place with Data Cloud and implementing data cleanliness best practices.

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