Get to Know Salesforce Data Cloud: A High-Level Overview, Key Use Cases, and Value-Focused Tips

Author: Janet Elliott

We’ve recently been fielding a lot of client questions about Salesforce Data Cloud — namely “what does it do,” “how does it work,” and “do I need this.” If you’ve also been scratching your head about what sets Data Cloud apart, you know Kicksaw is here to help. 

We’ve put our expertise (and Data Cloud certifications) to work for this blog. You’ll learn:

• What Data Cloud is 
• How it differs from other Salesforce solutions
• How it works
• Key use cases
• Tips for Salesforce Data Cloud implementations and use

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Salesforce Data Cloud Overview

Okay, so the first big question, what exactly is it? You’re going to come across a lot of terminology if you start looking this up.

• Per Salesforce: Data Cloud is Salesforce’s hyperscale data engine, a foundational component of the Einstein1 Platform that unifies a company’s structured and unstructured data to create a 360-degree view of its customers.

Let’s break this down further to understand exactly what this means. 

1. It’s a hyperscale data engine, which means it’s able to both increase and diversify an organization’s ability to process data (more on that in #3)

2. It’s a foundational component of the Einstein 1 Platform, meaning it helps set up an organization to improve their Salesforce AI capabilities

3. It unifies a company's structured and unstructured data — so aside from ingesting (“taking in”) data from different Salesforce orgs, it can bring in data from various other sources from your tech stack and beyond. 

4. And it’s doing this to create a 360-degree view of your customers

How Salesforce Data Cloud is Different 

You may already be familiar with the term ‘Customer 360’. When we hear that phrase being used in reference to Data Cloud, think of it as an evolved version of the term that is up-to-date with what today’s technology is capable of.

Older mentions of ‘Salesforce Customer 360’:

• Referred to manually connecting data from various Salesforce clouds based on a common identifier in each system to match up related records
• As well as integrating data from external systems with sometimes complex builds.

Salesforce Data Cloud improves and expands on that traditional customer 360-degree view:

• It offers a low-code option to build data streams that bring in structured and unstructured data from Salesforce clouds and external systems
• It makes it easier to bring in data from sources like data lakes, data warehouses and third-party applications via pre-built connectors

Data Cloud’s purpose also sets it apart from other Salesforce products. 

While the Salesforce platform is meant to consolidate data and tools to make your Sales and/or Service processes more efficient — Data Cloud takes in and harmonizes (i.e., maps — more on that in the next section) large volumes of data to create unified profiles related to contacts, without all of that data having to live directly in your Salesforce org. 

You can then leverage the harmonized data and unified profiles, which helps:

• Reduce org data storage and simplify your data integration strategy 
• Ground your AI to get more accurate results using more complete information
• Support organizations with multiple orgs and/or business units to more easily obtain data that’s actionable

Think of Data Cloud as a key ring that unifies and harmonizes data - with your Salesforce org and other data sources being the keys. It’s a tool that unlocks a lot of potential, particularly when it comes to Marketing and Sales.

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Good to Know…but How Does It Work Exactly?

1. Salesforce Data Cloud starts by ingesting data from your selected data sources (this can be data from across your Salesforce Clouds, cloud storage, devices, APIs, and SDKs)

• Think profile data from Sales Cloud, customer service activity from Service Cloud, order data, financial info, engagement info from Marketing Cloud, clicks from your website or ecommerce site, etc. 

• Data ingestion is done via batching (for processing large volumes of data at scheduled intervals) or streaming (for processing data in a time-sensitive way) — method selection is done during solution design, and should take into consideration both your needs and Data Cloud credit consumption

2. It has its own data model (think tables that house data in a normalized format), which it uses to harmonize your data (map data into this common data model)

• Different systems have different field names for the same type of data (e.g., birthdate, loyalty number, etc.), and mapping it helps create a standard
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3. When the data has been mapped, Data Cloud can then run Identity Resolution and Match Rules, which can use fuzzy and direct matching to link the correct data to the correct customer/prospect — to create unified profiles viewable in Data Cloud, on related lists or selected data copied to a field in your org

• This is the improved 360-degree view mentioned in the previous section that is easily configured 

4. Now that all of your data is neatly organized in unified profiles, you have a straightforward way to query that data using Calculated Insights 

• For example: determine customer lifetime value, customer churn risk score, build product recommendations, rank customers by spend or engagement scores, etc.
  

5. For Marketing use cases you can also use the Segmentation and Activation functionality

• For example: build a segment that identifies everyone who has clicked on a website link that lives within 50 miles, last purchased a complimentary item, and has a total lifetime spend of $500+ — and then publish it to an activation target like Marketing Cloud Engagement (see below)

6. It’s these insights that can be used to make more informed decisions and focus on highly targeted strategies that can directly benefit your business using 

• You can also act on these insights using Salesforce tools, applications, Actions, Flows (Salesforce org or Data Cloud sourced), or Activations

• These are automation set to launch based on defined triggers (e.g., sales and marketing notifications when early warning signs of attrition show or special offers sent based on customer activity)

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Key Use Cases

Okay, we know what Salesforce Data Cloud is, how it differs from other Salesforce solutions, and how it works — let’s get into what it can and can’t be used for. 

Specific use cases are pretty infinite, so we suggest you run them by your friendly neighborhood Salesforce expert (that’s us!) to see if Data Cloud is or isn’t a good fit for your specific organization. If you have separate lines of business and/or separate Salesforce orgs, it’s very likely that you’ll have some relevant use cases for Data Cloud.

Generally speaking, it’s ideal for helping consolidate and analyze data across multiple orgs and/or sources to:

Unify Prospects for Targeted Selling: by identifying opportunities with priority customers who can increase revenue.

• For example, if you’re a manufacturer who also offers services, you could identify accounts that are ideal for upgraded SLAs

Unify Customers for Personalized Service: by empowering service agents with info.

• For example, being able to automatically update appointments for customers experiencing travel delays

Unify Customers for Marketing Campaigns: by creating unified profiles that give organizations an omni-channel view of each customer.

• For example, using location (geofencing data) and browsing history to inform select candidates about events 

Ground Your Data for AI Use: because it can consolidate and unify data from across multiple orgs and/or sources for AI tools to maximize their impact.

Salesforce Data Cloud can’t be used for:

• Master data storage or management
• BI (Business Intelligence) platform
• Merging Records
• Data cleansing, backup, or governance

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Tips for Salesforce Data Cloud Implementation and Use

In terms of getting more value out of your Salesforce Data Cloud implementation, it helps to come into this project having done your homework. 

• Identify your data sources (both within Salesforce and outside) and the type of information they contain
• Take a look at data quality and consistency
• This solution is ideal for large organizations with siloed data sources looking to scale their data use and have more impactful marketing and/or customer service — reach out with questions if you’re unsure about suitability 
• Outline specific goals for Data Cloud and which data points and sources service this goal - it's smart to start small and build from there

Implementing Data Cloud is 80% analysis and design and 20% implementation: 

• Be realistic about the time frames involved, and don’t begin building before you have reviewed your data and have the Data Cloud design pinned down 
• Once you start building, there are certain areas that you can’t redo and you’ll have to start over

It also helps to start thinking about what makes the most sense for your Data Cloud functionality and data storage ahead of time. This can get a little confusing, because you have a lot of options (using it through an existing org with Amazon S3 storage, using a net new org, zero copy integrations), but it comes down to a combination of costs and best practices. 

Take a look at:

• Your overall project budget
• How many resources, departments, etc. will be using this tool
• How many existing Salesforce orgs, lines of business, and data sources your organization has

This will help you narrow down options when you speak with an implementation partner.

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Next Steps

Data Cloud is a big topic, with a lot of moving parts, but we hope this blog helped you get at least a basic understanding about this product and its use. 

Kicksaw has the certifications and experts if you have any questions or are looking into Salesforce Data Cloud suitability for your own organization — so, don’t hesitate to contact us.

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