Ultimate Guide: Leveraging Salesforce Data Cloud For Growth

Introduction

Ingest, harmonize, unify, and analyze streaming and batch data with Salesforce Data Cloud. Then use that data to unlock meaningful and intelligent experiences across Customer 360 applications and beyond.

Data Cloud puts all of your data to work for your customers. It’s deeply embedded in the Einstein 1 Platform, which means any external data lake or warehouse can now drive actions and workflows inside of your CRM. Data Cloud is about more than just bringing data together. It’s about bringing entire organizations together around the customer to improve experiences and drive growth.

What is Salesforce Data Cloud?

Salesforce Data Cloud is a comprehensive data management platform that integrates, analyzes, and leverages data from various sources to deliver actionable insights. Its core components include data integration, advanced analytics, and robust security features, making it an indispensable asset for businesses looking to thrive in the digital age.

Data Cloud is built on Salesforce’s foundational metadata layer, which provides a common language that integrates all Salesforce applications and low-code platform services including Einstein AI, Flow for automation, Lightning for UI, and Apex for deep, pro-code customization.

How Does It Work?

• Connect all your data sources whether batch or streaming data.
• Prepare your data through transformation and data governance features.
• Harmonize your data to a standard data model.
• Unify data with identity resolution rulesets.
• Query and analyze data using insights.
• Use AI to predict behavior.
• Segment your data and activate it to use in various channels to create personalized experiences.
• Analyze your data using supported analytic tools.
• Output data to multiple sources to act on data based on your business needs.
• Continue to review, measure, and optimize data.

1. Data Cloud connects to every data source

First, Data Cloud lets you easily combine your data on Salesforce with data from any other external source to create a trusted, comprehensive view of your customer. Use pre-built connectors or our zero-copy integrations to quickly pull in data from across your enterprise that’s trapped in platforms like AWS, Snowflake, and Google Big Query.

2. Data Cloud harmonizes your data

Next, since Data Cloud is purpose-built on Salesforce, it helps you take advantage of integrating all your data to our standard metadata model. Once integrated into the model, companies can access and use any of this data directly inside Salesforce applications — like Sales Cloud and Service Cloud. Unlike other data solutions that are difficult to use when harmonizing disparate data into a singular model, Salesforce makes data harmonization incredibly easy through point-and-click mapping and pre-configured data bundles that automatically map your data for you.

3. Data Cloud makes it easy to activate your data

Finally, with Data Cloud, teams can transform messy, hard-to-use data that’s scattered across your enterprise into a unified resource. Data Cloud makes it easy to use your data to build data-driven automations and business processes.

With seamless integrations to both Salesforce applications and popular destinations, like telemetry data or purchase invoicing systems, teams can activate experiences powered by Data Cloud in nearly any environment where their work takes place.

So, now you have an idea of what Data Cloud is and how to get hands-on with it. But how do you get started?

Here are five tips to help you have great discussions about Data Cloud.

1. Break down organizational silos

Data management is complex. You may not know how much of your customer data is scattered across your organization from CRM, Purchase, Engagement, Website, Mobile App Data, and several other systems. Bringing together all of this data can be complicated and will affect several teams, including IT, CRM, marketing, web, mobile, data, and many others.

When starting a Data Cloud project and the process of bringing together this disparate data, each business unit feels ownership of “their data” and “their processes.” We talk a lot about data silos, but you also have to recognize the silos of teams and people within your company. A successful Data Cloud project requires breaking down these silos to ensure executive sponsorship and alignment among all teams.

At the same time, avoid “shared responsibility” and assign a single owner. For a large enterprise, it can be the Center of Excellence (CoE), but in a small company, it can be an individual (admin, data analyst) who owns Data Cloud to drive it forward.

2. Pick a use case

Now, you don’t need to start using Data Cloud with the goal of integrating all your data sources. Even if you have just one data source (or Salesforce Cloud), you can use Data Cloud to drive impact.

What does this mean? Let’s look at some use cases of how other customers use Data Cloud across other Salesforce Clouds.

Sales Cloud — Improve Forecasting and Sales Collaboration

• Consolidate data across multiple orgs to identify opportunities with priority customers and increase revenue.
• Provide executives a full view of the sales forecast across multiple business units and orgs.
• Pass leads from one Sales org to another to facilitate cross-selling.
• Allow sales reps to collaborate with their broader account team on opportunities in separate orgs.

Service Cloud — Provide Proactive Customer Service

• Consolidate data across multiple orgs to empower service agents with a unified, 360-degree view of the customer.
• Anticipate and deflect cases by sharing info proactively (for example, warranty extension notifications, product recalls).
• Monitor events and devices to identify service actions (for example, schedule proactive maintenance based on device data).
• Predict behavior to offer assistance and recommendations (for example, provide agents with customer’s propensity to buy).

Marketing Cloud — Personalize Marketing and Drive Engagement

• Consolidate and unify subscribers across all your channels and act on real-time data to personalize every moment.
• Create and automate intelligent audiences fast.
• Gain insights into high-value segments and campaigns.
• Segment more precisely and Activate across the entire customer journey.

3. Identify success metrics

It’s important to identify and define your success metrics. Answer the following questions to create your SMART success metrics.

• What is the problem Data Cloud solves?-For example, “Harmonize and unify disparate data.”
• How does Data Cloud help?-For example, “Connect using out-of-the-box connectors and unify customer data at scale.”
• How can we measure?-For example, “Data Sources Integrated”
• What indicates success?-For example, “Measure the number or percentage of data sources successfully integrated into Data Cloud.”

When you think of metrics, it doesn’t necessarily need to be revenue-generating metrics. It can also be cost savings, efficiency gained, etc. The important thing is you need to have a baseline so you can measure improvement.

Here are some Data Cloud success metrics across different areas.

• Data integration and quality metrics
  • Data Sources Integrated: Measure the number or percentage of data sources successfully integrated into Data Cloud. This shows how well the platform is consolidating data.
  • Data Integrity: Percentage of records without errors, inconsistencies, or duplicates; high integrity ensures that decisions made using Data Cloud data are reliable.
  • Data Latency: Measure the time taken for new data to appear in the Data Cloud system. Lower latency is often better for real-time decision-making.

• User engagement and adoption metrics
  • User Adoption Rate: Number or percentage of team members actively using Data Cloud; higher adoption usually correlates with greater value generation.
  • User Training Time: This is the average time required for a team member to be trained in using the platform effectively.
  • User Satisfaction Scores: Through periodic surveys, gauge how satisfied users are with Data Cloud.

• Business key performance indicators (KPIs)
  • Customer Lifetime Value (CLTV): Monitor if Data Cloud helps in increasing CLTV by enabling more targeted and effective marketing strategies.
  • Customer Segmentation Effectiveness: Measure how accurately and usefully Data Cloud allows you to segment your customer base for marketing or analytics.
  • Customer Retention Rate: A high retention rate could indicate effective use of Data Cloud for customer engagement and personalization.

• Operational efficiency metrics
  • Query Time: The time taken to fetch specific data or generate reports; lower query time is generally better.
  • System Uptime: This is the percentage of time Data Cloud is operational and accessible.
  • Cost Savings: Quantify the amount of money saved due to operational efficiencies gained by using Data Cloud.

• Marketing metrics
  • Campaign Return on Investment (ROI): Compare the returns on marketing campaigns run using insights from Data Cloud against those run without it.
  • Lead Conversion Rate: Monitor changes in the conversion rate of marketing leads to paying customers. An increase could be attributed to more effective marketing made possible by Data Cloud.
  • Customer Engagement Metrics: This includes metrics like click-through rates, email open rates, or app engagement rates that could improve due to better personalization and targeting enabled by Data Cloud.

• Compliance and security metrics
  • Data Compliance Rate: This is the percentage of customer data in Data Cloud that meets GDPR, CCPA, or other regulatory standards.
  • Security Incidents: The number of security incidents or breaches involving Data Cloud; fewer incidents signify better security.

4. Leverage the Customer 360 Data Model

The Customer 360 Data Model is Data Cloud’s standard data model that helps make data interoperable. In the simplest terms, the model organizes different types of data and how they relate to each other. However, the Customer 360 Data Model is highly normalized so any source data that is being brought needs to be normalized before it can be mapped.

So, before ingesting and mapping your data, you must do the following.

• Inventory your data sources:
  • Understand System of Record for each data source.
  • Investigate quantity, quality, and completeness.
  • Build out a Data Dictionary for each source.
  • Determine Data Category (for each data stream).

• Align to the Customer 360 Data Model:
  • Establish how you will transform source data.
  • Plan how to Map Source Data to Data Cloud DMO.
    • Often done in a spreadsheet

5. Think big, start small

Data Cloud can solve a lot of different business challenges and serve different teams across your enterprise.

• Sales team → Improves forecasting and sales collaboration
• Marketing team → Personalized marketing and drives engagement
• Service team → Increases productivity by giving them quicker access to holistic customer data
• Executive → Shows the entire business across multiple orgs

Innovative Data Cloud features and Learning Path

Organize and unify data across Salesforce and other external data sources. After data has been ingested into Data Cloud, it can be used to drive personalization and engagement through the 

creation of audience segments. Additionally, through identity resolution you can achieve a single, actionable view of your customer built on the world’s #1 customer platform.

With Data Cloud you can:

• Create unified profiles across all touchpoints by connecting identities, engagement data, customer orders, loyalty, and marketing journeys.
• Build smarter audience segments using insights and filtering capabilities.
• Activate data from anywhere across your organization.
• Capture and unify data from anywhere with a high-scale data ingestion service.
• Analyze your data using tools like Tableau or Marketing Cloud Intelligence.

1. Data Cloud is natively integrated with the Salesforce metadata framework

This allows your organization to turn data from any source into standard objects and fields that teams using Salesforce already know how to work with. This means companies can more easily use all their data that lives outside of Salesforce within everyday apps, like Sales Cloud and Service Cloud, without having to invest in and maintain messy, expensive data pipelines.

2. Data Cloud is designed to put data to work through low-code, no-code tools

With all your data harmonized within the Salesforce metadata framework, you can put your data to work using low code tools unique to Salesforce, such as Flow, and generative AI solutions like Einstein Copilot and Prompt Builder. This helps business teams more easily use data to power all their workflows, automations, AI, and analytics without relying on IT.

3. Data Cloud is fully open and extensible

With Data Cloud, you can maximize the ROI on all of these investments. That’s because Data Cloud is designed with an open, extensible architecture that employs zero-copy integrations, allowing seamless connections to top platforms like Snowflake or Databricks — without the need to move or copy data. This approach provides unparalleled flexibility and control for managing data within Data Cloud, making it easier than ever to bring data in and send it out as needed.

Get Started Using Data Cloud

Before you start using Data Cloud, review the checklist, considerations, and product navigation.

• Understands overall guidelines and limitations that can impact billing.
• Reviews brand management and organization structure used in Data Cloud.
• Decides on a data strategy by reviewing data model concepts.
• Analyze your existing data and data sources.
• Plans to use unified profiles.
• Identifies an admin to set up Data Cloud.
• Lists users and their needed permissions.
• Identifies segmentation goals.

Checklist and Considerations

Navigation

Once Data Cloud is enabled in your account, you can find Data Cloud in the App Launcher.

On the Data Cloud home page, features are organized into tabs.

The Data Streams, Data Lake Objects, and Data Model (1) tabs are focused on data ingestion and modeling. Data Explorer and Profile Explorer (2) are data-viewing tools, allowing a view into ingested data and unified profiles. The Identity Resolutions (3) tab is where your team creates match and reconciliation rules to unify individual records. Calculated Insights (4) are predefined and calculated metrics that can be used in creating segments and for data analysis.The Setup gear (5) is where the admin configures Data Cloud features.

• Data Cloud Standard Editions and Licenses
  Data Cloud is available in several standard Salesforce editions. Some Data Cloud features also require additional licenses.

Data Cloud is available in several standard Salesforce editions. Some Data Cloud features also require additional licenses.

Standard Editions:Data Cloud is available in Lightning Experience for these standard Salesforce editions.

• Developer
• Enterprise
• Performance
• Unlimited

• Feature Availability in Data Cloud and Customer Data Platform
  Your Data Cloud license determines which features are available within your org’s Data Cloud. Some features require purchasing an add-on license.
• Data Cloud Limits and Guidelines
  Guidelines define best practice recommendations to optimize your adoption of Data Cloud for best performance. Limits are boundaries beyond which features are unavailable, performance is throttled, or usage billing charges are applied. Since Data Cloud is built to scale, some limits can be adjusted to meet your business needs. Work with your account executive to find a solution that meets your goals.
• Data Cloud Billable Usage Types
  Usage of certain Data Cloud features impacts credit consumption. For more information on how usage is billed, refer to your contract or contact your account executive.
• Billing Impact of Data Cloud–Powered Features
  Some Salesforce CRM features are powered by Data Cloud data. These features impact Data Services Credit consumption by increasing usage of associated processes in Data Cloud (such as querying, storage, processing, and analytics). Before configuring Data Cloud-powered features, review features and usage documentation. 

Start using Data Cloud today

As you begin your Data Cloud journey, it can seem like a lot of information with different acronyms and terms being thrown around. But understanding how you can get started with Data Cloud comes from answering three broad questions.

• Which data needs to be brought into Data Cloud?
• How does the data need to be harmonized and unified?
• Which action needs to be taken and why?

Use these three questions as your guiding principle to help drive successful Data Cloud implementation and adoption at your organization.

Summary

Data Cloud could be described as the ‘Holy Grail of CRM’, meaning that the data problem that’s existed since the infancy of CRM is now finally solvable. But the journey that this product / infrastructure has been on is somewhat elusive.

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.