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Data 360 Consultant

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Data 360 Consultant Study Checklist

  • Platform Administrator
  • Platform App Builder
  • Platform Foundations
  • Platform Developer
  • Platform Administrator II
  • Agentforce Sales Consultant
  • Agentforce Service Consultant
  • Platform Data Architect
  • Platform Development Lifecycle and Deployment Architect
  • Platform Identity and Access Management Architect
  • Platform Integration Architect
  • Platform Sharing and Visibility Architect
  • Heroku Architect
  • B2C Solution Architect
  • Experience Cloud Consultant
  • Agentforce Field Service and Operations Consultant
  • Agentforce Nonprofit Consultant
  • Data 360 Consultant
  • Omnistudio Consultant
  • CRM Analytics and Einstein Discovery Consultant
  • Platform User Experience Designer
  • Platform Strategy Designer
  • B2C Commerce Developer
  • JavaScript Developer
  • Omnistudio Developer
  • Platform Developer II
  • Marketing Cloud Engagement Administrator
  • Marketing Cloud Engagement Specialist
  • Marketing Cloud Engagement Consultant
  • Agentforce Sales Foundations
  • Business Analyst
  • Marketing Cloud Engagement Developer
  • Marketing Cloud Engagement Foundations
  • Agentforce Specialist
  • Agentforce Life Sciences Consultant
  • B2B Commerce Administrator AP
  • B2B Commerce Developer AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • MuleSoft Integration Foundations
  • MuleSoft Developer
  • MuleSoft Developer II
  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
  • Tableau Data Analyst
  • Tableau Consultant
  • Tableau Server Administrator
  • Tableau Architect
  • Slack Administrator

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0/198Learned

Describe Data 360’s key terminology and business value

0/9

Data 360 data-processing stages and the features in each

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Data streams, data lake objects and data model objects

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Harmonization versus unification in Data 360

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Segments, activations and activation targets

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Analyze-and-act options: insights, segments, enrichment and triggered flows

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Positioning connection and collaboration options for enterprise data

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Home org and companion orgs for multi-org Data 360 access

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Matching Data 360 capabilities to customer business outcomes

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KPIs and ROI for demonstrating Data 360 value

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Articulate Data 360’s foundational role in generative and predictive AI

0/5

  • Data 360 as the data foundation for trusted AI and Agentforce
  • Predictive AI versus generative AI business use cases
  • Grounding generative AI in Data 360 data versus training a custom model
  • Unstructured data readiness for retrieval augmented generation
  • Data + AI + CRM strategy and trusted AI planning

Identify initial use cases for Data 360

0/8

  • Marketing personalization and journey use cases for Data 360
  • Paid media and advertising audience use cases
  • Sales use cases for lead qualification, upsell and pipeline visibility
  • Service use cases for a unified customer view
  • When a use case calls for real-time Data 360 capabilities
  • Readiness discovery before implementing a Data 360 use case
  • Prioritizing a first Data 360 use case for quick, measurable value
  • Data 360 alongside Informatica IDMC in the data foundation

Describe the principles of data ethics and governance

0/8

  • Salesforce data ethics best practices and the personalization-privacy balance
  • Data minimization in Data 360 ingestion and segmentation
  • Sensitive attributes and proxies for protected classes
  • Consent management and customer preferences in Data 360
  • Data subject rights: deletion, restriction of processing and Do Not Sell or Share
  • Third-party activation partners and data chain of custody
  • Data 360 Governance: tag-based classification and access policies
  • Platform Encryption and Private Connect for Data 360 data security
  • Provisioning Data 360 in production, sandbox and Developer Edition orgs
  • Enabling Data 360 features with Feature Manager
  • Data 360 Setup access: System Administrator profile and Architect permission set
  • Standard Data 360 permission sets by user persona
  • Standard versus custom permission sets for Data 360 access
  • Moving users from legacy to updated Data 360 permission sets
  • Associating permission sets with Data 360 data spaces
  • Data space feature permissions versus permission set object permissions
  • Salesforce CRM connection permissions and connector object access
  • Data access levels for CRM users viewing Data 360 data
  • Data spaces for segregating brand, region or business unit data
  • Data tagging, classification and taxonomies for governance
  • Default Allow All policy and administrator data visibility
  • Tag-driven object and field access policies versus permission set grants
  • Platform Encryption for Data 360 key management options
  • Data residency requirements and Data 360 org placement
  • Data deletion and Right to be Forgotten requests in Data 360
  • Restriction of processing versus data deletion requests
  • Unified lineage for data dependencies and change impact
  • Data 360 sandbox setup, refresh and synchronization
  • Promoting Data 360 configurations from sandbox to production
  • Standard data kit versus DevOps data kit
  • Standard data kit packaging and distribution workflow
  • Deploying installed data kit components in a target org
  • Updating, versioning and uninstalling data kits
  • Reusing data kits and bundles across data spaces
  • Data Explorer versus Profile Explorer for inspecting records
  • Query Editor for validating and troubleshooting Data 360 data
  • Data stream refresh status and ingestion failure diagnosis
  • Identity resolution processing results for unification issues
  • Segment and activation status and error diagnosis
  • Processing dependencies behind stale segment and activation results
  • Control and metadata change events for Data 360 status monitoring
  • Troubleshooting data kit installation and component deployment
  • Org diagnostics for Data 360 incidents and support cases
  • Batch versus streaming data transforms
  • Data transform inputs and outputs across DLOs and DMOs
  • Batch data transform node types for reshaping data
  • Streaming data transform mechanics and SQL definition
  • Formula fields in data streams for cleansing and derived keys
  • Formula fields versus data transforms for data preparation
  • Normalizing denormalized source data before mapping
  • Code extension for custom Python batch transforms
  • Ingestion flow from data stream to data lake object
  • Data source inventory and connection planning
  • Choosing a connector type for each source system
  • Admin connection setup before data stream creation
  • Salesforce CRM connector for home and external orgs
  • Starter data bundles versus manually built data streams
  • Cloud storage, SFTP, and file upload ingestion
  • Web and Mobile App connector for engagement and behavioral data
  • Ingestion API streaming versus bulk interaction patterns
  • Ingestion API connector setup sequence
  • Data stream category selection and downstream impact
  • Primary key and composite key selection for data streams
  • Anonymous versus known profile categorization at ingestion
  • Data stream refresh modes and schedules
  • Matching ingestion frequency to latency requirements
  • Source schema review and changes after data stream creation
  • Unstructured data ingestion for AI grounding and search
  • Zero copy data federation versus zero copy data sharing
  • Data ingestion versus zero copy federation for external lakehouse data
  • External data lake objects created by zero copy data streams
  • Query federation versus file federation
  • Live query versus accelerated (cached) federation
  • When to use zero copy data sharing for external analytics and ML
  • Data 360 clean rooms for privacy-safe partner collaboration
  • Where unification sits in the Data 360 data flow
  • Unified profiles as a system of reference versus golden records
  • Business use cases for unified profiles
  • Identity resolution ruleset creation and ruleset IDs
  • Match rule design and match methods
  • Reconciliation rules for unified profile attribute values
  • Unified Individual versus Unified Link objects
  • Contact point preservation in unified profiles
  • Filtering source records before identity resolution
  • Ruleset processing frequency and unified profile updates
  • Choosing a profile unification type for a business requirement
  • Household unification built on individual ruleset output
  • Real-time identity resolution for in-engagement personalization
  • Evaluating, comparing, and retiring identity resolution rulesets
  • Data lake objects versus data model objects
  • Customer 360 Data Model purpose and subject areas
  • Standard versus custom DMOs and custom attributes
  • DLO-to-DMO field mapping workflow
  • DMO categories and how mapped data sets them
  • DMO relationships, cardinality, and keys
  • Mapping source profiles to Individual and contact point DMOs
  • Data mapping requirements for identity resolution
  • Choosing standard DMOs for engagement and transactional sources
  • Modeling account (B2B) versus individual (B2C) data
  • Data bundles and Simple Start for prebuilt mappings
  • Key qualifiers and fully qualified keys across sources
  • Planning and maintaining a data model for mapping
  • Calculated insight measures, dimensions, and metric levels
  • Insight Builder versus SQL for authoring insights
  • Joining engagement data to unified profiles in insights
  • Choosing among calculated, streaming, and real-time insights
  • Calculated insights versus batch data transforms for derived data
  • Data graphs for precomputed views of unified data
  • Data share use cases and choice of shareable Data 360 objects
  • Data share and data share target setup sequence
  • Governance and record visibility for data shares at the receiving system
  • Data shares versus activations and data actions for outbound data
  • Data 360-to-Data 360 data shares versus companion org connections
  • Programmatic query options for external applications
  • Data graphs as a low-latency data source for other applications
  • Related list and copy field enrichments in the Salesforce org
  • Query Editor and Data Explorer versus reports for inspecting Data 360 data
  • Standard reports versus custom report types on Data 360 objects
  • Grouping, filtering, and summarizing in a Data 360 report
  • Dashboards combining Data 360 reports with CRM reports
  • Access and folder sharing for Data 360 reports and dashboards
  • CRM Analytics Direct Data for live Data 360 analysis
  • Tableau Semantics semantic models on Data 360 data
  • Selecting an analytics tool for Data 360 data
  • Model Builder, BYOM, and BYOLLM options in AI Models
  • Matching predictive model types to business questions
  • Preparing training data and evaluating predictive model quality
  • Predictions, prescriptions, and top predictors as model outputs
  • Activating models and consuming predictions across Salesforce
  • Combining predictive models with agents and prompt templates
  • Grounding prompts and agents with unified profile data
  • Chunking unstructured data into search index objects
  • Vector search versus hybrid search indexes for grounding
  • Retrievers and retrieval augmented generation in prompt templates
  • Document AI for extracting data from unstructured documents
  • Natural-language segment creation with generative AI
  • Segmentation purpose and common Data 360 segmentation use cases
  • Segment On entity selection for individual, household, and account audiences
  • Choosing a segment type for a business requirement
  • Real-time segments and the real-time data graph
  • Waterfall segments for priority-ordered, mutually exclusive offers
  • Data model prerequisites for accurate segmentation
  • Segment canvas direct and related attributes from the attribute library
  • Include, exclude, and nested segment criteria
  • Calculated insights as segmentation criteria
  • Group, Rank, and Limit within segment containers
  • Segment lookback windows at segment and container level
  • Interpreting segment status and publish status
  • Validating segment membership and population counts
  • Editing, copying, deactivating, and deleting segments
  • Segment creation options beyond the segment canvas
  • Choosing an activation target for the destination platform
  • Segment and activation type compatibility with activation targets
  • Activation membership and the Unified Individual
  • Contact point selection and source priority in activations
  • Direct and related attributes for personalized activation payloads
  • Segmentation filters versus activation filters
  • Standard publish, Rapid Publish, and Publish Now for segments
  • Full versus incremental activation refresh
  • Monitoring activations from the activation record home
  • Streaming versus batch DMO activation
  • Communication capping for B2C activations
  • Activation templates for standardized activation configuration
  • Permission sets for activation targets, activations, and publishing
  • Data actions versus segment activations for acting on data
  • Choosing a data action target for event-driven orchestration
  • Data action source objects, event rules, and action rule conditions
  • Enriching data action payloads with data graphs
  • Standard, real-time, and vector search data actions
  • Data 360-triggered flows for DMO and insight changes
  • Querying Data 360 objects with Get Records in flows
  • Record-triggered flow path requirements for Data 360 queries
  • Data actions to Platform Event targets for flow orchestration
  • Activation-triggered flows for delivering activated data to external APIs
  • API activation initiated from Flow
  • Chaining Data 360 processes with Flow Builder automations

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Ask questions and get the latest info from other Data 360 Consultant studiers. 662 members and growing.

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