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Put enterprise data to work in Salesforce without unnecessary movement

Customer teams need information from across the business, but copying data between platforms can create additional complexity, duplication and governance challenges.

Salesforce and Databricks zero-copy capabilities can help organizations make relevant data available across platforms without relying on traditional data movement for every use case.

Megnity helps organizations design and implement this connection around the data, workflows and business outcomes that matter.

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Use cases first

Start with the data your teams actually need

We begin by understanding what Salesforce teams need from enterprise data and what Databricks users need from Salesforce.

This keeps the architecture focused on specific business use cases rather than connecting data simply because it is available.

Connecting two platforms doesn’t mean every dataset needs to become available everywhere.

Databricks to Salesforce

Make Databricks data useful in Salesforce

Important customer context may already exist in Databricks.

We help make relevant enterprise data available through Salesforce and Data 360 so sales, service, marketing and other teams can work with broader context within their existing workflows.

This helps bring enterprise data closer to the point where customer decisions are being made.

Explore Data 360

Salesforce to Databricks

Make Salesforce data available for wider analysis

The connection works in both directions.

Customer and engagement information from Salesforce may provide valuable context for analytics and other data workloads in Databricks.

We help organizations identify where that information can add value and how it should fit into the wider data environment.

Explore Databricks

Zero-copy

Reduce unnecessary copies of data

Traditional integrations can require information to be extracted, copied and stored in multiple places.

That can introduce additional pipelines, storage and data management requirements.

Where appropriate, zero-copy approaches allow platforms to work with relevant data without creating another physical copy simply to make it accessible.

The right approach depends on the use case, architecture and governance requirements.

Agentforce

Give Agentforce richer business context

AI agents become more useful when they can work with relevant information from beyond the CRM.

Connecting Salesforce and Databricks can give Agentforce access to appropriate enterprise context through the Salesforce data environment.

We help determine what information the agent needs, how it should be made available and what permissions should apply.

Explore AI agents

  • Salesforce
  • Data 360
  • Databricks

Agent context

  • Customer record
  • Enterprise context
  • Permissions applied

Customer engagement

Support more connected customer experiences

Sales, service and marketing teams may each need different information from the enterprise data environment.

A connected Salesforce and Databricks architecture can help make appropriate data available across those workflows without building separate data movement processes for every team.

The result is a more consistent foundation for customer engagement.

Sales

Salesforce + Databricks

Appropriate enterprise context for the account.

No separate data movement /

Service

Salesforce + Databricks

Different information, one connected foundation.

Marketing

Salesforce + Databricks

A consistent foundation for engagement.

Life Sciences

Build for Life Sciences data

Life Sciences organizations can have important information spread across commercial, medical, clinical and operational environments.

We help identify where that data can provide useful context within Salesforce while considering access, governance and how different teams are permitted to use the information.

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This can create a stronger foundation for analytics and AI while keeping data controls part of the architecture.

Governance

Keep governance in the design

Making data easier to access doesn’t mean making it available to everyone.

We help define which information should be accessible, who should be able to use it and how permissions should work across the connected environment. For regulated or sensitive use cases, additional validation, traceability and governance requirements can be considered as part of delivery.

Explore validation and compliance

Right-sizing

Know when zero-copy makes sense

Zero-copy is useful when it solves a real data access problem.

It isn’t automatically the right answer for every integration. Some workflows may still require traditional integrations, data pipelines or other approaches.

We look at the business requirement, data architecture, performance and governance needs before deciding how Salesforce and Databricks should work together.

Before deciding, we look at
  • The business requirement
  • Data architecture
  • Performance
  • Governance needs

How we deliver

From separate platforms to connected data

01

Understand

Identify the business use cases and data required.
02

Assess

Review the existing Salesforce and Databricks environments.
03

Design

Define how data should be made available across platforms.
04

Govern

Establish access, permissions and appropriate controls.
05

Connect

Configure the required Salesforce, Data 360 and Databricks capabilities.
06

Test

Confirm that the right information is available to the right workflows.
07

Activate

Put connected data to work across analytics, customer processes and AI.
08

Improve

Expand the connection as new business use cases emerge.

The stack

A connected Salesforce and Databricks environment

Our work can bring together:

The architecture depends on the information being used and what the business needs to accomplish with it.

  • Salesforce
  • Data 360
  • Databricks
  • Agentforce
  • Tableau
  • MuleSoft and enterprise integrations

Contact

Want to make Databricks data useful inside Salesforce?

Tell us what information your Salesforce teams need and where it lives today.

We’ll help you determine whether zero-copy is the right approach and how it can fit into your wider data architecture.

Talk to us