5 tips for getting started with data cloud.

5 Tips for Getting Started with Data Cloud


As an admin, you’ve probably heard of Data Cloud, but maybe you haven’t prioritized it right away because you have other company challenges to address. Well, now’s the time to move Data Cloud to the top and dig in.

If you’re thinking, “What is Data Cloud? Can you break it down for me?”, you’re in the right place.

How Data Cloud Works.

The image above breaks down how Data Cloud works, but let’s simplify it some more.

Imagine you have pieces of a puzzle (Data Sources) spread across different rooms in your house. Data Cloud is like a super-efficient organizer that gathers all these pieces (Connect), puts them together (Harmonize), and shows you the complete picture (Unify) on your coffee table.

It connects to different data sources, harmonizes and unifies that data to make it understandable and usable, and then lets you act (Activate) on this data to make smart decisions. This could mean sending a perfectly timed offer to a customer or resolving their service issue swiftly. It’s all about making sure every piece of customer information you have is used to create better experiences.

At Dreamforce 2023, we announced free Data Cloud for all Salesforce customers (Enterprise Edition and above) to help jump-start your data and artificial intelligence (AI) journey. So, if you’re a Sales Cloud or Service Cloud admin, you can start using Data Cloud for absolutely free and unlock value today!

Unlock Value from Data Cloud for Sales & Service Cloud.

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 within your company 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.

Takeaway: Data Cloud will need involvement from several different groups. While everyone needs to cooperate, you need a single owner to drive its implementation and adoption.

How to ensure organizational alignment with Data Cloud.

2. Pick a use case

As a fan of author Stephen Covey, I believe the principle of “begin with the end in mind” is applicable for not only our daily lives but also Data Cloud.

Start by answering the question, “What will you do with your data?” Remember: Once your data is in Data Cloud, it can be activated across all of Salesforce and other external applications.

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.

Takeaway: Pick a high-value, low-effort use case. Remember, you can start integrating with just one data source to drive impact.

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.

Example Data Cloud Success Metrics.

Takeaway: Identify your SMART success metrics and make sure you have a baseline so you can measure progress.

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 will you transform source data.
    • Plan how to Map Source Data to Data Cloud DMO.
      • Often done in a spreadsheet

Inventory your data sources and align to the Customer 360 Data Model before ingesting and mapping your data.


Takeaway: Inventory your data sources and align to the Customer 360 Data Model before ingesting and mapping your data. Take your time and don’t rush into pushing buttons in Data Cloud.

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 → Personalizes 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

But as an admin, ask yourself, “What will have the greatest impact in the shortest amount of time?”

Think Big, Start Small.

This will help you identify the quick wins that can move the needle which can be further expanded.

Here’s where you can put the earlier tips into action to prioritize your use cases as well as ensure your organization is aligned on those priorities so everyone moves in the same direction.

Takeaway: Identify the quick wins that can move the needle which can be further expanded on as you mature.

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.

Good luck on your Data Cloud journey!


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