Get Data Cloud Certified: Steps & Practice Resources

What is Data Cloud??

Data Cloud is a native Salesforce data platform that unifies your company’s data onto Salesforce’s ?Einstein 1 Platform for automation, personalized engagement, and AI use cases.

Big Data, No Problem:?

Data Cloud is built to handle massive amounts of data — we're talking terabytes of data and billions of records! This means you can get a complete picture of your customers, even if you have a lot of information to manage.

A Name with a History:?

The Data Cloud name has changed from Customer 360 Audiences in 2020 to Salesforce Customer Data Platform (CDP) in 2021, Marketing Cloud Customer Data Platform in 2022, Salesforce Genie in 2022, and finally Data Cloud in 2023. But don't worry about the name—the important thing is that it gives you a powerful way to understand your customers better than ever before.

In simple terms: Data Cloud helps you see the unified profile of your customers so you can build stronger relationships and provide better experiences.

Provision and Set Up Data Cloud

?Key features:

1. Einstein One Platform Integration: Data Cloud works seamlessly with Einstein One Platform, giving you access to powerful AI tools like Einstein Studio.
2. Unified Customer Profiles: Data Cloud combines customer data from different sources (like Salesforce, Amazon S3, or Google Cloud) into one complete profile while keeping sensitive information secure.
3. Permissions and Data Spaces: To manage and organize your data effectively, you must set up the correct permissions (like Data Cloud Admin or Data Cloud Marketing Admin) and create Data Spaces (logical partitions into brands/departments/ regions with the ability to provide user access through permission sets).

Data Streams and Bundles:?

Data streams are sourced from Salesforce CRM or third parties and categorized into profiles, engagements, or other data. A starter data bundle is a Salesforce-defined data stream definition that includes mapping from a data source to the Data Cloud DMO structure (data stream + preconfigured mapping to DMO, which can be edited to add mapping to more fields and objects).

Data Space:?

In Data Cloud, a "data space" is a logical partition for organizing different data streams and sources. You can have multiple data spaces for different brands or categories. Some limits, like the number of data spaces, vary by edition or org type. For example, enterprise orgs allow up to 50 data spaces, while Developer orgs are limited to 1.

Integration and Reporting:?

The platform offers integration with third-party systems and reporting capabilities for monitoring and debugging.

Industry Use Cases:?

The platform supports various industries by offering tailored solutions, such as customer segmentation in financial services and personalized marketing in retail.

AI Use Cases:?

Salesforce continues expanding the capabilities of Data Cloud, especially with AI use cases. For example, with the new Prompt Builder feature, you can now ingest PDFs into Data Cloud. Once the PDFs are in, you can type a prompt using Prompt Builder within Salesforce CRM. The tool will search through all those PDFs and generate a response. You can't do this with the core Salesforce, but it's possible with Data Cloud, thanks to its AI functionalities.

Create Predictions:?

Better segmentation optimizes organizations' marketing ROI. This information can be surfaced back into Salesforce on account or contact pages. Additionally, you can leverage AI functionalities to create predictions from the data within Data Cloud, similar to Prediction Builder in Sales and Service Cloud. However, with Data Cloud, you're working with much more data than you would typically have inside your Salesforce org.

Salesforce Data Cloud Advantage Over Other CDP Solutions:

Salesforce Data Cloud's key advantage over other CDPs is its pre-built integration with Salesforce objects, simplifying data mapping and harmonization across various Salesforce clouds. This streamlines integration compared to other CDPs where you might need to build custom data models.

Connect Data to Salesforce Data Cloud: Flexible Options

Salesforce Data Cloud offers a variety of ways to integrate your data:

• Salesforce Connectors: Pre-built connectors for seamless integration with other Salesforce products (like Marketing Cloud). Many include starter data bundles for quick setup.
• SDKs and APIs: Comprehensive tools for custom data integration.
• Third-Party Connectors: Connect data from external sources like Amazon S3.
• Bring Your Own Lake (BYOL): Secure, real-time access to partner data without copying it into Data Cloud.

This flexibility ensures that you can connect virtually any data source to Data Cloud, enabling a truly unified view of your customer.

Let us Break Down the Key Concepts:

Here's an overview of key concepts related to data ingestion, transformation, and harmonization within the Data Cloud:

Ingestion:? Bring in and Connect Your Data to Data Cloud

You've gathered your data from different sources, such as your Salesforce CRM, Amazon S3, or Google Cloud Storage. Now, it's time to combine it all in Data Cloud.

Think of it like this:

1. Connect the Pipes: You establish connections (like pipes) between Data Cloud and your various data sources.
2. Let the Data Flow: Your data starts flowing into the Data Cloud, either in batches (like scheduled deliveries) or continuously in real time (like a live stream).
3. Organize and Make Sense of It: Data Cloud uses a standard "data model" to organize and understand your data, no matter where it came from. The result? A unified view of your customer data, ready for you to use to create better experiences and make smarter decisions.

EXAMPLE:?

Let us imagine an individual named John Smith exists differently across five different data sources; you can then harmonize through Data Cloud's individual object using the Contact Point Phone object or Contact Point Email object to create John Smith's unified profile by mapping data sources, harmonizing to the Data Cloud standard data model.

Once you've harmonized, you can do the most crucial step, which is unifying that data, creating that single source of truth to create that unified profile of the individual or account. You can then run analysis, predictions, and segment the data to act on in your Salesforce org. You can then publish a segment to activation platforms (targets), such as Marketing Cloud Engagement).

Finally, you can use this knowledge to take action! For instance, you might send a targeted email campaign to John and others like him using Marketing Cloud.

Salesforce's Data Security, aka Einstein Trust Layer, emphasizes that sensitive data will not be exposed to external large language models like ChatGPT. This ensures that enterprises with strict data governance can safely use these platforms. Even small organizations, including nonprofits, can benefit from Salesforce's Data Cloud for creating unified profiles across various systems.

• Data Sources: Data can be ingested from various sources, such as Salesforce CRM, external databases, and Cloud Storage like Amazon S3. Data streams are created for each source.
• Data Bundles and Kits: Data bundles are pre-packaged data sets from Salesforce, while data kits can be custom or third-party-packaged data.
• Categories in Data Streams:

Profile Data: Contact or Account information or Information about customers, such as names and email addresses.

Engagement Data: Activities or interactions, like clicks or purchases.

Other Data: Information such as products or store locations.

• Stream Setup: The stream type cannot be changed once a data stream is set up. It must be disconnected correctly by removing associated segments and data streams before disconnecting the source.
• Delete a Data Model Object and Remove Field Mappings: In the data mapping canvas, you can delete a data model object (DMO) and remove data lake object (DLO) field mappings.

2. Transformation

Purpose: Data Cloud allows basic transformations to prepare data for segmentation, monitoring, and analytics. However, it's not intended to replace data cleansing or more complex transformations done in data management platforms.

Types of Transformations:

1. Row-Level Transforms (Formula Fields): These are created during ingestion to perform row-level calculations, such as conditional statements or arithmetic operations. Example scenario: Calculating "Profit" from "Revenue" and "Cost" columns: Create a formula field named "Profit" with the expression Revenue - Cost. This will automatically generate a new "Profit" column in your data set, where each row's value is calculated based on the corresponding "Revenue" and "Cost" values.
2. Batch Transforms
3. Streaming Transforms

Available Data Types: Text, Number, Date, and DateTime are the primary return types for formulas.

Transformation Process: The transformation happens at the ingestion stage, where data is mapped and harmonized according to the Data Cloud model.

Data type (Example fields like Email/Phone are given text data type by default; we can change it to Email data type or Phone data type) These fields should be changed at the ingestion stage if required, as it can't be done past this stage.

Harmonization

Key Steps

• Data Mapping: This involves standardizing data from various sources so that different terms or formats that refer to the same entity (like email addresses) are mapped to a standard model.
• Identity Resolution: After harmonizing data, identity resolution involves unifying multiple records for the same customer into a single profile.
• Harmonization refers specifically to mapping; it includes unification and further data processing steps.

Data Mapping and Unification.

Key Points:

1. Data Models in Salesforce Data Cloud: Salesforce provides a standard canonical data model with pre-existing data model objects (DMOs) to which users should map their data sources. Users are encouraged to use these standard DMOs rather than creating custom ones, though custom DMOs can be created when necessary, leading to a hybrid data model.
2. Mapping Process:?Mapping connects data from Data Lake Objects (DLOs) to DMOs. Salesforce offers tools for reviewing and adjusting these mappings, which can involve many-to-one relationships. It's essential to verify mappings, including checking relationships between objects and ensuring that primary keys are mapped.
3. Field Mapping and Unmapped Fields: The tool allows viewing both mapped and unmapped fields. Users can create new fields in the data model, choosing from various field types if necessary. The importance of correctly mapping data in Salesforce Data Cloud cannot be emphasized enough. Proper mapping is crucial for creating unified customer profiles because if the data is mapped correctly, Data Cloud will be able to combine the data properly. This leads to smooth segmentation and exposing data back into Salesforce.

??Warnings! - Salesforce provides tools to help with this process by offering Warnings!? when necessary mapping is incomplete. For example, if address fields like "address line one" and "city" aren't mapped, Data Cloud will alert you that these fields need to be mapped for proper unification. For example, in a nonprofit use case,? when mapping donations to the opportunity object in Salesforce's standard data model, proper mapping and harmonization are necessary after data ingestion.

Unification

Creating a Unified 360 Degree View of the Customer:

• Profile Unification: The process of merging different records of the same customer into a unified profile.
• Account Unification: Although most documentation focuses on individuals, Data Cloud also supports unification at the account level.

Unification and Identity Resolution:

• Unification consolidates customer data from multiple sources into a single profile using match and reconciliation rules.
• Match rules can be strict or loose, impacting match accuracy. We prefer under-consolidation to over-consolidation to avoid the risk of merging two different individuals.

• Reconciliation rules [Link] determine which data to retain when conflicts arise based on frequency, recency, or source reliability.

• The session also mentions "Anonymous Profiles," created when data lacks identifying information. These profiles become known when additional data is matched.

Data Cloud architecture uses unification, including link objects that manage one-to-many relationships during the unification process.

Take Action - Planning and Consumption

Managing Your Data Cloud Usage

• Consumption Credits [Link]: Data Cloud operates on a consumption-based model where credits are used based on the actions taken (e.g., ingestion, harmonization, unification).
• Planning Usage: It's crucial to plan how Data Cloud will be used to ensure adequate credits are available, especially as different processes consume credits at varying rates.

6. Usage Example:

• Data Flows: Once connections and data streams are established, data moves through the stages from ingestion to harmonization, unification, and finally activation, where insights are drawn and actions are taken based on the processed data.

This overview highlights the key aspects of managing and transforming data within Data Cloud, providing a clear understanding of how data is ingested, mapped, transformed, and unified to create a holistic view of customers or other entities.

Data activation, segmentation, and insights in Salesforce, particularly within the context of data cloud and identity resolution.

Key points:

Calculated Insights: These are multi-dimensional metrics stored in Salesforce Data Cloud, used to personalize information for targeting audiences. They can be created using either SQL queries or a visual builder, allowing you to aggregate data across different objects and filter based on specific criteria. For example, to calculate the lifetime value of donations, you would aggregate all donations related to a unified account. This process involves using a visual builder in Data Cloud, which allows you to visually create metrics like lifetime value by joining and aggregating data from different sources.? Calculated insights can be scheduled to run regularly (e.g., hourly or daily) to ensure the data is as up-to-date as possible. After running the calculated insights, you can review the data in the Data Explorer to ensure accuracy.

Streaming Insights: Unlike calculated insights, streaming insights are aggregation queries based on real-time engagement data points, such as website interactions (Web SDK and Mobile SDK) or live customer behavior. You can use a streaming insight to build time series aggregation in near real-time that can drive orchestration or data actions in the Data Cloud. Streaming insights aren’t supported in segments or activation.

Segmentation involves creating targeted groups of individuals (unified individuals) based on specific criteria, such as lifetime customer value or purchase history. Segments can be published and activated on different schedules, and they help identify and target the right audience for marketing or sales campaigns.

Analyze -

Tools and processes that enable you to transform raw data into actionable insights include:

• Analytics Tools: Leverage Tableau, Einstein AI, and custom reports for data visualization and predictive insights.
• Real-Time Insights: Analyze live data streams for real-time customer behavior and interactions.
• Data Exploration: Use SQL-based queries and ad-hoc exploration for quick insights.
• Visualization: Create interactive dashboards and reports to track trends and make data-driven decisions.

Activation: After creating segments, the next step is to deliver these segments to external platforms like Amazon S3 or Marketing Cloud. Activation makes sure that the right audience is targeted based on the segments you've created. This process involves setting up activation targets, which could be either batch processes or real-time actions (data actions).

Deleting and Disabling: If an activation target or segment is no longer needed, it can be deleted or disabled. However, Salesforce recommends disabling rather than deleting if you need to reuse the segment or target.

Differences Between Activation and Data Actions: Activation generally involves batch processes, while data actions are real-time and use platform events, such as Data Cloud—Triggered Flow. Data Cloud-Triggered Flows in Salesforce help streamline and automate data actions in real-time within Data Cloud.

Leveraging unified data—Once you're satisfied with the unification process, the next steps involve using the unified data for segmentation, calculated insights, and actions that add real value.

Data Enrichment through Copy Field - After "calculated insights" and "actions" within Data Cloud, the goal is to expose unified donation (opportunity) data back into Salesforce on the account screen and how to calculate the lifetime value of these opportunities/donations within Data Cloud. This calculated value is then copied to a field in the Salesforce account.

Data Cloud Related List Enrichment - You can add a related list of "unified opportunities/donations" to the account page in Salesforce, which displays all opportunities/donations linked to an account from multiple systems. This is done by setting up a "Data Cloud Related List" enrichment in Salesforce, which allows data from Data Cloud to be shown on Salesforce account pages. Enrichment is achieved by copying the calculated lifetime value (CLV) from Data Cloud to a field in Salesforce. This is done using the "Copy Field" functionality, mapping a source field from Data Cloud to a target field in Salesforce. This process can also be scheduled to ensure regular updates.

Finally, the AI capabilities within Data Cloud, such as using Einstein Studio to create predictive models, allow organizations to focus on high-value opportunities. These models can predict outcomes like opportunity/donation amounts.

The data cloud also allows for the regular ingesting of unstructured data, such as PDFs stored in Amazon S3, which can be integrated into the data cloud for use with AI tools like Prompt Builder.

Salesforce Data Cloud, especially when combined with AI features like Prompt Builder, allows you to perform advanced tasks that aren't possible with the core Salesforce platform. For instance, you can upload PDFs into Data Cloud and then use Prompt Builder in Salesforce CRM to search through those PDFs and get responses based on their content. This capability is unique to Data Cloud and unavailable in the core Salesforce platform.

San Ramon Trailblazers YouTube Videos

1. ??????How to Get Certified in Data Cloud and Hands-on Exercise Links ??
2. Data Architecture For Data Cloud ?? Warning?? Do Not Implement Before Planning

Resources : Topics - ?? Solution Overview ?? Data Cloud Setup and Administration

1. About Data Cloud
2. Data Cloud Standard Permission Sets
3. Sign up for 14-day trial org / 30-day org if affiliated with a partner [Link]
4. Knowledge Check: Set Up and Administer Data Cloud
5. Cert Prep: Data Cloud Consultant
6. Setup Amazon S3 Data Stream [Link]
7. Data Sources Connectors in Data Cloud
8. Worksheet: Data Cloud Checklist and Considerations
9. Data Spaces in Data Cloud - [Link]
10. Data Cloud Limits and Guidelines
11. Data Cloud Reports and Dashboards: Limits and Limitations
12. How to Add Data Cloud to Your Salesforce Account [Link]
13. Unlock your Data with Data Cloud: https://sfdc.co/DCPrep
14. Data Cloud Consultant Exam Guide: https://sfdc.co/DCCertGuide
15. Data Cloud: Advanced Data Cloud Curriculum: https://sfdc.co/DCAdvanced

Resources : Topics - ?? Data Ingestion and Modeling ?? Identity Resolution

1. Data Cloud Ingestion, Data Modeling, Unification [Slide Deck]
2. YouTube Playlist - [Link]
3. Data Cloud Study Sheet-Public [Link]

Resources : Topics - ?? Segmentation and Insights ?? Act on Data

1. Segmentation and Activation [Link]
2. ?? Data Cloud Academy Guide for Salesforce Partners [Link]