Unlocking the Power of Data Kits in Salesforce Marketing Cloud Next

sfmcstories
Nov 16
4 min read

In the era of data-driven marketing, the way we manage and deploy metadata can make all the difference. This is where Data Kits — a powerful feature of Salesforce Data Cloud — come into play. If you're a marketer, admin, or architect, understanding Data Kits is key to streamlining your data deployment, maintaining consistency across environments, and safeguarding your configuration investments.

Harnessing the potential of Data Kits in Salesforce Marketing Cloud for enhanced data visualization and targeted strategies.

What Are Data Kits?

At its core, a Data Kit is a bundle of Data Cloud metadata — not the actual customer data, but the definitions, configurations, and structures that govern how Data Cloud works. Think of it as a “configuration suitcase”: inside, you carry things like:

Data streams (connectors to data sources)
Data object definitions (how data is modeled)
Identity resolution rules (how duplicate or related records are unified)
Calculated insights (metrics derived from data)
Segments (audience definitions)
Activation targets (where data is pushed, e.g. Marketing Cloud)

These are packaged together so they can be deployed in a consistent, controlled way.

Why Use Data Kits?

Consistency Across Environments
When you’re building in a sandbox, testing, and then deploying to production, Data Kits ensure that the same metadata (streams, objects, rules) moves reliably without missing dependencies.
Version Control & DevOps Compatibility
For teams following DevOps or CI/CD practices, there’s a special type called the DevOps Data Kit, designed to work with version control systems (like Git) and tools like Salesforce CLI.
This allows you to:
- Export your Data Kit as a manifest
- Commit it to source control
- Deploy it reliably across environments (dev → QA → prod)
Deployment Order with Dependencies
Data Cloud has interdependent components — for example, a calculated insight might rely on a data object, which in turn depends on data streams. A Data Kit knows this order and handles deployments in the correct sequence so things don’t break.
Modularity
You can create standard Data Kits to package reusable Data Cloud solutions, or DevOps Data Kits for migrations.
For example, you might maintain a “Marketing Data Kit” that only includes marketing-specific streams, segments, and insights. Separately, you might have a “Core Data Kit” for identity resolution and shared data objects.

Important Considerations When Working with Data Kits

Environment Restrictions: Salesforce recommends creating standard Data Kits from a Developer Edition org.
Package Separation: When packaging Data Cloud metadata, you need to keep Data Cloud components separate from other Salesforce metadata. Starting in certain releases (like Winter ’25), Salesforce doesn’t allow mixing Data Cloud metadata with non-Data Cloud metadata in a single package.
Activation Credentials: While Data Kits bundle streams and definitions, sensitive connector credentials (like API keys) are not included. You must reconfigure authentication in each target environment.
Publishing: After installation, you typically need to activate or publish the data kit inside the Data Cloud setup to make all components live.

How to Deploy a Data Kit

Depending on your setup and maturity, you have different deployment options:

Change Sets
If you prefer clicks over code, you can use Salesforce change sets. Just include the Data Package Kit Definition component type and related metadata when building the change set.
Salesforce CLI & Manifest
You can download a manifest (a package.xml) directly from your Data Kit and deploy it via the CLI. This is ideal for automated, repeatable deployments.
Manual Manifest
For advanced users, hand-crafting a package.xml gives you full control over which components and in what order they are deployed.

Once deployed, you often need to go into Data Cloud Setup (in the target org) and activate the kit so that its components (streams, insights, segments) are published.

Best Practices

Modularize your kits: Don’t bundle everything into one huge Data Kit. Instead, separate by domain (e.g., marketing, identity, activation) so you can manage changes cleanly.
Use source control: If you have DevOps capabilities, maintain your Data Kits in Git. This helps with versioning, traceability, and rollback.
Test in sandbox first: Always deploy and activate your Data Kit in a non-production environment to validate dependencies, publishing order, and behavior.
Document well: Given that Data Kits carry configurations (not raw data), maintaining good documentation around each component is crucial — especially for onboarding new team members or auditors.
Align with your architecture: Think about your long-term Data Cloud architecture. How will your Data Kits evolve as you add new sources, insights, and activation targets?

Why Marketers Should Care

For marketing teams, Data Kits unlock several strategic advantages:

Quick onboarding of new business units: As new geographies or campaigns kick off, you can deploy the same trusted data structure and segmentation logic reliably.
Governance and compliance: By packaging identity resolution, consent, and activation rules, you ensure that compliance policies travel across environments.
Scalability: As your Data Cloud usage matures, Data Kits support a modular, scalable deployment model — no need to rebuild from scratch every time.

Final Thoughts

Data Kits are a foundational aspect of Marketing Cloud Next (now tightly integrated with Data Cloud). (Salesforce) They provide a structured, reliable way to package, version, and deploy Data Cloud metadata — helping you bridge the gap between development and production without losing control over your configurations.

Whether you're a Salesforce architect building out your Data Cloud strategy, a DevOps engineer managing CI/CD pipelines, or a marketer waiting on data readiness for campaign activations — Data Kits are your bridge to consistency and scale.