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Are You Ready to Invest in Salesforce Data Cloud?

By Lucy Mazalon

Data Cloud is the foundation that all Salesforce products run on – in other words, it ‘gets the data flowing’ between clouds. However, even though it is the foundation that all customers leverage everyday, it’s also an offering that organizations can purchase for additional capabilities. 

For example, with Marketing GPT, all customers can use the email content creation functionality, regardless of whether they are using Data Cloud directly – but of course, this all becomes even stronger with Data Cloud as a personalization and data unification platform, and opens up “Segment Creation” and “Segment Intelligence”.

With Data Cloud receiving lots of attention, it’s a good time to talk about the signs that you’re ready to invest in Data Cloud, as well as the considerations you should take before ‘jumping in’.

What is Salesforce Data Cloud?

Data Cloud (formerly Salesforce CDP) is a system that gathers all your customer data in one place, thereby making organizations smarter and customer-data-centric when it comes to engaging their customers and the next best actions to be taken.

In the current landscape of third-party cookies being depreciated, companies need to collect, combine, and manage their customer data by themselves, instead of relying on third parties to do that for them. However, doing this in real time and at scale requires some serious muscle. 

Data Cloud solves this by leveraging a flexible data model with massive processing power to combine data from many different sources into single, unified customer profiles. With unified profiles, it’s much easier to offer personalized interactions for your customers.

READ MORE: Your Guide to Customer Data Platforms (and Salesforce CDP)

Salesforce Data Cloud Cost

Now let’s talk about the ‘elephant in the room’ – Data Cloud is expensive. Starting at $10k per org, per month, it’s an investment that needs careful consideration. Realistically speaking, your organization may already need to spend more than that on Data Cloud, depending on your data volumes. It’s always something to keep in mind as your data evolves over time. 

Perhaps your organization is in a good position to demonstrate return on investment (ROI). With solid use cases in mind, you could be confident in the timelines for reaching that. The strongest use cases are those that will enable your organization to truly leverage first-party data.

On the flip side, as one Salesforce executive pointed out, if you endeavor to do it yourself, you’d be expecting to spend 5-10x more – plus, with a homegrown solution, you’d be missing out on the innovation (Data Cloud is receiving a huge amount of resource allocation at Salesforce) and the peace of mind that it’s scalable (and therefore, future-proof). 

Above: Data mapping in Data Cloud

Salesforce Data Cloud Skills 

As with all technology, it’s not about the tool but knowing how to use it – this is where most companies fall short when implementing a CDP. 

Skills in your team that you’ll need include people with: 

  • Developer skills: For ​​during implementation and when updating events and data coming into Data Cloud.
  • Data management skills: As each organization has its own data model and architecture, Data Cloud will need someone to configure the platform accordingly. 
  • Business analyst skills: To identify and solve business challenges, otherwise you risk ending up with an expensive, siloed system that nobody really understands.
READ MORE: Salesforce Data Cloud (CDP): Which Skills Do You Need on Your Team?

Other Data Cloud Readiness Factors

  • Data volumes: Data Cloud is best justified when you have plenty of data to play with, especially cross-functional data and/or data from multiple sources. Again, this plays into demonstrating ROI – the more complex the segmentation you want to perform, and the more cross-team benefits, then the more promising the ROI will be.  
Above: Segment creation in Data Cloud
  • Data quality: The average organization doesn’t fare well when it comes to data quality. While Data Cloud does perform identity resolution, using Data Cloud when your starting point is bad data will only exacerbate the problem. Remember, garbage in, garbage out. One example that could keep you up at night is consent consolidation – that is which channels customers have opted-in/out of. This includes the metadata that accompanies consent data, such as the date opted-in. Lack control over consent data, and you could be exposing your organization to privacy regulation breaches. 
  • Establishing a governance model: As mentioned, Data Cloud works by mapping data points from multiple sources in order to perform identity resolution. One change to a data point in one system (whether that be an API name change, or changes to data format) has the potential to ‘throw a spanner in the works’. Establishing governance requires that all people working with your technology stack are on the same page, and that any changes are communicated effectively. However, there is a barrier for marketing teams which have specific data where teams are protective over its ownership.  
  • Team capacity: Similar to the skills point raised before, your team needs to actually have time to use Data Cloud effectively, requiring effort to prepare data for each activation you want to do.
Above: Segment Creation, part of Marketing GPT
READ MORE: What is Salesforce CDP (Data Cloud)? Salesforce Genie vs Marketing Cloud Customer Data Platform


Data Cloud is a great investment, but only if your organization is in a good position to demonstrate return on investment (ROI). With solid use cases in mind, you could be confident in the timelines for reaching your goals, and reap the rewards of unified customer profiles.

The Author

Lucy Mazalon

Lucy is the Operations Director at Salesforce Ben. She is a 10x certified Marketing Champion and founder of The DRIP.

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