From Pillars to Lakes: Using Data Cloud As Your Source of Truth

The power of Salesforce comes from its data. When Salesforce launched its CRM platform in 2000, it allowed businesses to control their sales data right from their browser. As more and more customization options arrived, it meant they could build an enterprise application right where their business data resided.

At the platform’s core, its strength comes from the fact that it requires zero additional setup. You can build a UI out of the box, add additional logic, and automate tasks without having to build any bridges to external database systems. Need to add more custom data models? The system is updated for you, courtesy of the metadata layer.

However, 2025 is a far cry from the early days of Salesforce. Now, it’s more common that admins deal with multiple orgs with different users and data models. External systems like Snowflake, Databricks, and AWS are frequently used with Salesforce systems to complete a picture of what users need for their day-to-day jobs. With more than 200 prebuilt connectors and support for zero-copy integrations, Data Cloud is the tool for navigating these different systems.

It’s time to stop thinking in terms of a series of interconnected pillars and shift to a combined data lake for a precise view of all of your important data and files.

Data Cloud as a source of truth

While many of Data Cloud’s features come from its history as a Customer Data Platform, that’s not the only trick it has up its sleeve. It’s true that admins can consolidate multiple identity references into a single person using Data Cloud’s roots as a Customer Data Platform—but thanks to the Salesforce Platform’s unified data model, those identities can also be updated in real or near real time and trigger automation through familiar tools like Flow. This not only considers the old question of “Is this the correct record?” but also makes it easy to utilize that harmonized data with the rest of your Salesforce implementation. Add in Data Cloud’s Governance and Compliance features, using policy based data classification, and it’s easier than ever for admins to maintain data privacy and adhere to regulations.

Moving away from a pillar (or pillars) of data to a centralized data lake will also help streamline your processes and make solutions more efficient. Agentforce can only utilize the data it can see within the Salesforce Platform, so giving it a unified view of your important data helps your artificial intelligence (AI) be both intelligent and competent.

Data Cloud and the Salesforce ecosystem

A lot of the heavy lifting within Data Cloud is done through integration with the Customer 360 family, having been built on top of Data Manager for data reconciliation, Privacy Data Center for privacy and consent regulation, and Customer 360 Identity for identity management. This helps admins handle tricky but important policies like General Data Protection Regulation (GDPR).

The family of connectors means it can work with data warehouses like Snowflake if that’s more efficient than simply offering a replacement. Of course, the use of MuleSoft means you can still have secure connectivity with other enterprise systems. Future enhancements are expected from the Informatica acquisition, including improved data quality, mobile device management (MDM), compliance, security, and integration capabilities. As the Salesforce family grows, Data Cloud provides a centerpiece to manage the new data.

And of course, if you’re a Salesforce Admin overseeing multiple Salesforce orgs, Data Cloud can perform that important task of maintaining a single source of truth across your instances. Data Cloud One allows you to establish a single org to centralize data sharing, automation, and insights by connecting to this home org via companion connections, facilitating seamless data sharing and access. Data space (logical partitions within Data Cloud) enables selective data sharing, allowing you to control and organize the data shared with each companion org, ensuring data governance and security.

Remember that the intent of Customer 360 was to solidify Salesforce as a customer-centric solution, an early move away from data silos and toward a consolidated, accurate view. Data Cloud provides a solution for weaving that view from across the Salesforce ecosystem.

Tips for using Data Cloud as a source of truth

To effectively leverage Data Cloud, consider the following essential steps.

Establish clear ownership and collaboration: Consider appointing a dedicated owner or establishing a Center of Excellence (CoE) to lead the initiative, ensuring unified data management and processes across the organization.
Prioritize a quick-win use case: Begin with a focused, achievable use case that delivers immediate value. Demonstrating Data Cloud’s potential, such as enhancing sales forecasts or personalizing marketing, encourages wider adoption.
Map data sources to the Customer 360 model: Conduct a comprehensive inventory of all data sources and align them with the Customer 360 Data Model before integration. This alignment guarantees data consistency and interoperability, forming a unified customer view and maximizing data utility.
Uphold ethical data handling and privacy: Like security, ensuring ethics and privacy is a day 1 task, and every day after that. Detail the policies and regulations your data will need to follow early, and ensure it’s part of the solution during testing.

Strategize your integrated, harmonized data view

Data Cloud is a multifaceted tool. Every implementation must be tailored to the specific needs of you, your organization, and your users. And the payoff is well worth the journey. Adding data lakes as your source of truth promotes data consistency across various pillars and silos. It establishes a more complete view of your important data, integrated tightly with the Salesforce tools you already use. This helps you make better data-driven decisions and provides an environment based on clean, updated data that creates more intelligent solutions, including Agentforce.