Cert Guide - Data Cloud Consultant 2024

To pass, candidates need to achieve a score of 62%, equivalent to answering 38 out of 60 questions

Solution Overview (18%)

• What is Data Cloud?

  → Why Data Cloud - to provide unified view of a customer and their related data.

• Data Cloud Core Capabilities

  • Customer 360 Data Model - The overall system that governs a set of common data model objects, how to describe them, and their relationships.

  • Data stream - A data source brought into Data Cloud.

  • Data lake - A vast pool of raw data whose purpose is not yet defined.

  • DMO - Data model object.

  • Attribute - A standardized piece of information about a DMO.

  • Data Dictionary - An inventory of all data sources.

  • Data Discovery - Identifying sources of data that are relevant to what you want to accomplish.

  • Data Explorer - View and validate the data that exists in your DMO's DLO's and Calculated Insights.

  • Data Kit - A portable and customized bundle of packageable metadata, created with Data Cloud.

  • Package - A container for custom objects and metadata created with Package Manager.

  • Data Stream - The connections and associated data ingested into Data Cloud.

• How Data Cloud Works?

  Data Cloud is a data platform that combines the power of the Salesforce Platform with the scalability of an infrastructure that allows for processing data in near–real-time. Salesforce Data Cloud offers a bridge to harness data split across many orgs, Marketing Cloud, web engagement, across warehouses and lake houses — to be used for AI, analytics, and automation.

  Brief Explanation follows

  • Connect all your data sources, whether batch or streaming real-time 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.

  • Analyze, expand, and act on your data in any channel.

  • Segment audiences and create personalized experiences.

  • Output data to multiple sources to act on data based on your business needs.

  • Continue to review, measure, and optimize data.

Data Cloud and Administration (12%)

• Hyperscale Data Store

• Data Lake:

  👀 Type of Data: Semi Structured and Unstructured Data

  🙄 Purpose: ML & AI tasks

  🧐 ACID: Non-acid Compliant: integrity

  🙂 Cost Storage: Effective, fast, flexible

• Data Warehouse:

  👀 Type of Data: Structured Data

  🙄 Purpose: Data Analytics, BI

  🧐 ACID: Acid Compliant, ensures integrity

  🙂 Cost Storage: Expensive & Time Consuming

  💡

  Cloud Data is not stored as OBJECTS, but in the Data Lake outside of Core CRM

  Data Cloud still requires API access to its Objects from within this org, because it replicates the data to the data lake.

• Explore Data Cloud UI

  → Data Cloud features or tabs

  1. Data Streams - lets you to create different data streams to get data from External Systems

  2. Data Model - it has different data model objects and create relationships between them.

  3. Identity resolutions - it has harmonized data that can be used to create matching and reconciliation rules to unify your records

  4. Data Explorer - to view the data that is ingested and unified so far.

  5. Profile Explorer

  6. Calculated Insights - to create metrics on your data

  7. Segments - filter and group into segments

  8. Data Actions - Fire events/platform events

  9. Data Cloud Setup - lets you configure connectors and so on.

     Access Connectors ⇒ Gear Icon > Data Cloud Setup > Salesforce CRM

     • You can connect any NEW external org

     • Once connected, you can create Data Stream, to allow the data to flow into Salesforce.

• Permissions - CRUD access in Data Cloud Org

  → Data Cloud Admin

  • Allows users to access all functionality within Data Cloud like mapping data to a data model

  • Creating and editing data Streams

  • Creating and editing identity resolutions, rule sets

  • Creating and editing data actions and data action targets, as well as working with calculated insights

→ Data Cloud User

• Most features access as Read Only

→ Data Cloud for Marketing Cloud Admin

• allows user to manage day to day configuration, Support, Maintenance and to perform set of System Audits

• most superpower among Marketing Cloud users

→ Data Cloud for Marketing Data Aware Specialist

• giving access to users for mapping data models

• Create and editing data streams

• Create and editing identity resolution sets

→ Data Cloud for Marketing Manager

• Giving access to create and edit segments, data, actions and data targets

→ Data Cloud for Marketing Specialist

• Gives access to create segments or look and see data streams like model objects

• Data Stream Categories

  • Profile

    Engagement

    Other

Data Ingestion and Modelling (20%)

• Data Cloud Fundamentals

  Bringing the data from different data source involves two phases

  • Data ingestion

  • Data Modelling (also called as Data Harmonization)

1. Data Ingestion: -

   → Data from data sources is brought as it is to Data Cloud (Without any transformation - the fields and datatypes are imported without transformation)

   — To get the data from the Data Source -

   • First establish the connection between the data source and data cloud

   • We can do this with the help of connectors (No worries on integrating with external systems)

→ Setup Data Streams

Create Data Streams, to get the data from the Source Systems -

• The Data that comes in through a data stream is written to a Data Lake Object. (DLO)

• Each Data Stream contains its Data Lake Object (DLO).

• Data Lake Object gets refreshed on a schedule basis.

  💡

  Remember, list show org already connected to! How to access? ⇒ Gear Icon > Data Cloud Setup > Salesforce CRM

• Data Bundles - creates multiple data streams and data mapping for a group of predefined objects.

• You can either choose data bundles or select single object

• Category - you cannot change the category after saving data stream

  • Profile

  • Engagement

  • Other

• Name the stream and Deploy

  Notice the data is inserted into Data Lake Object.

  You can refresh the data manually, via DLO UI

  2. Data Modelling

  → In this phase, the data inside a Data Lake Object is mapped to different objects in Customer 360 Data Model

  Mapping Data Streams

  → Mapping DLO to DMO

  → You can map DLO entity to multiple DMO field entities

Identity Resolution (14%)

• Identity Resolution Terms

  Source profile - Records from your source data streams that Data Cloud reviews to identify matching profiles

  Unified contact objects - Stores information related to unified profiles and can be related to many unified contact points

  Ruleset - Contains match and reconciliation rules that tell identity resolution how to match and reconcile your source profiles.

  Unified profile - Customer data that has been reconciled across multiple sources as a single record using identity resolution rulesets.

  Match method - How data is processed and reviewed during matching.

  Match rules - Tells Data cloud which profiles to unify during the identity resolution process.

  Consolidation Rate (CR)- A metric that shows how source profiles are grouped from unified profiles.

• Identity Resolution Object - Ruleset Creation

  • Create a new ruleset

  • Configure Match Rules & Match rule criteria - Fields with Match methods

  • Lookout Resolution Summary result.

• Ruleset Creation - Types of Rules

  • Match Rules

    • Match Rules match methods

      • Exact

      • Fuzzy

      • Normalized

  • Reconciliation Rules

• What happens if we delete Existing Identity Resolution ruleset

  Unified customer data associated with the ruleset will be removed. Dependencies on Data Model Object will be removed

Segmentation and Insights (18%)

• Data Segmentation Terms

  Segment: Filter your data to create useful segments to understand, target, and analyze your customers.

  Segment on: Within segmentation, segment on defines the target object used to build your segment.

  Publish: Publish is the process of searching and building a segment based on the filter criteria. You can publish your segments on a chosen schedule or as needed.

  Activation: Activation is the process of moving audience segments to an activation target.

  Direct attributes: Attributes that have a one-to-one relationship with the segment target. Meaning each segmented entity has only one data point for a profile attribute. So for customer data, they would only have one entry for postal code or for first name.

  Related attributes: Attributes that can have multiple data points.

• There are two types of Insights, you can create

  1. Calculated Insights

  2. Streaming Insights

Before creating insights, make sure you know the Data Model in your org

• Calculated insights appear in Segmentation Canvas, only IF calculated insight contains a dimension, that should be a Primary Key on Segmentation Table

• Streaming Insights allows you to query and aggregate data from real-time streams using window functions.

• Segment Deactivation - A deactivated segment no longer publishes, can't be chosen for an activation, and can't be re-enabled.

  • If you plan to use the segment again, then stop the publish schedule.

• Using Insights - we can create

  1. Segments and Activations

  2. Data Enrichments

  3. API’s and APEX

  4. Data Actions

Act on Data (18%)

• Segment and Activate your Data

  create high-value segments and activate at scale for increased conversions

  → Segment - Create a New Segment - UnifiedIndividualTexas

  • Standard Publish - Publish Schedule

→ Activation Target - Create an activation

→ Activate and Save

Resources - Practice