Some of you may know I’ve started working more with Data Cloud and I thought it was time to start sharing some content on Data Cloud. I figured I’d start with a fundamental blog on what Data Cloud is all about including the different Data Cloud concepts. Coming from core Salesforce or CRM Analytics I’m sure you are used to working with data, however, it’s probably hard to wrap your head around Data Cloud and it’s different. At least I know it was for me, so let’s get started.
Why is Data Cloud a Big Deal?
Let’s first answer the question of why Data Cloud is a big deal, and not just because we told you so at Dreamforce.
Data Cloud is not the first to gather all data sources, nor is it the first to clean, prep, and unify your data; however, the unique value prop for Data Cloud is that it is built on Salesforce metadata, so it works across the clouds, and integrates with the core Salesforce platform (Sales, Service, Marketing, Commerce) and includes all the rich functionality you’re used to like Flow, APEX, analytics etc. Plus guess where Salesforce’s Einstein GPT (generative AI) gets its rich insight from? That’s right, Data Cloud.
Most companies have data stored in many places and it is more often than not fragmented and perhaps even out of date. Trying to piece together a holistic view of your customer becomes more and more challenging if not impossible. Taking data out of transactional systems like Salesforce, combining it with high-volume data from systems of engagement, and pushing it back to source systems or CRM. And as a Salesforce Admin, you may not even have access to all these data sources (think website tracking, mobile tracking, transaction data) since they’re outside of CRM, yet they are still relevant in understanding your customer. Plus, as a Salesforce Admin, you probably don’t have access to the tools to unify and improve the data quality from Salesforce, even if you have the data readily available.
Data Cloud isn’t a rebranding or repackaging of an existing product. It was a huge undertaking to attempt to consume both real-time streaming and batch data securely at scale, unify it, and allow anyone to create/consume insights and then take action on that holistic set of data. Oh and did I mention that we pioneered technology with Snowflake, Google, and AWS that allows us to consume data without having to physically copy over the data from your cloud EDWs? No more duplication of data or paying for data residing in multiple places! Our storage tier now contains not only data but metadata as well. Fewer trips back and forth means faster performance!
An Overview of How Data Cloud Works
Let’s continue with a Data Cloud overview – you’ve probably already seen this image before but it describes very well the different parts of Data Cloud.
As we have already mentioned data is typically stored several places, Data Cloud can get access to this data through connectors and APIs. Once we have that data in Data Cloud there are several tools to prepare this data (see Connect & Prepare in the image below) in a way that we need for further processing whether that being for Marketing, Sales, Service, or other use cases.
Once the data has been connected and shaped we can start harmonizing the data one being making sure the right people have access to the right data but also making sure the data is mapped and joined to the data model. This data model is especially helpful when you have fragmented data as the data model and the Data Cloud tools a holistic picture of a customer’s activity and profile card can be created.
Once we have created this 360-degree view we can start activating this data – in other words, take action and get insight in many different ways.
There is plenty of new terminology to learn if you are new to Data Cloud – I know because I found myself trying to understand all the new concepts when I started learning Data Cloud. The Salesforce Doc team has put together a great glossary, which is more in-depth than what I’ve gathered here, so once you start diving more into Data Cloud make sure to review that list.
Below you’ll find a list of concepts and terminology that I found helpful to understand when I started looking at Data Cloud. If you hit the + next to each term I’ve gathered additional resources for you to explore and learn.
You Will Rarely Hear People Ask for a “Data Cloud”
Now you know what Data Cloud is; understand its key concepts and features. However, most Salesforce customers won’t actually directly ask for Data Cloud. To determine the needs for a solution like Data Cloud you need to listen closer to their statements. Below are some statements you may hear where Data Cloud could provide the solution they need.
- We want to increase agent productivity by giving them quicker access to customer data.
- Our sales teams are bogged down by bad data and it’s hurting their productivity.
- Our executives need to see the entire business across multiple orgs.
- We just merged and need to consolidate our Salesforce orgs.
- We need to leverage data across our enterprise to forecast better.
- We need to provide more personalized customer experiences.
- We want to increase efficiency by using AI and automation to improve employee’s jobs.
- We want access to all the engagement data to trigger incredible real-time experiences for customers.
- We need to get to get a single view of our customer and their data.
- We need more intelligent insights to help our sales team know where to focus their time to increase revenue and productivity.
- We need to improve productivity by automating processes across orgs.
Hopefully, this introduction to Data Cloud helps frame why Data Cloud is so powerful, but also allows you to understand the important concepts of working with many data sources to streamline it for a data-centric business model. Data Cloud is more than just CDP. It gives Salesforce Admins a full data platform to power customer-centric interactions for B2C and B2B.
If you want to explore the Data Cloud and the concepts more, I can recommend taking the “Data Cloud-Powered Experiences” module on Trailhead.