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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

Checklist progressProgress

0/198Learned

  • Data 360 data-processing stages and the features in each
  • Data streams, data lake objects and data model objects
  • Harmonization versus unification in Data 360
  • Segments, activations and activation targets
  • Analyze-and-act options: insights, segments, enrichment and triggered flows
  • Positioning connection and collaboration options for enterprise data
  • Home org and companion orgs for multi-org Data 360 access
  • Matching Data 360 capabilities to customer business outcomes
  • KPIs and ROI for demonstrating Data 360 value
  • 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
  • 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
  • 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

Explain data transformation capabilities within Data 360

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Batch versus streaming data transforms

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Data transform inputs and outputs across DLOs and DMOs

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Batch data transform node types for reshaping data

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Streaming data transform mechanics and SQL definition

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Formula fields in data streams for cleansing and derived keys

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Formula fields versus data transforms for data preparation

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Normalizing denormalized source data before mapping

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Code extension for custom Python batch transforms

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Describe processes and considerations for data ingestion from varied sources into Data 360

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  • 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

Describe Data 360’s data collaboration capabilities, including Zero-Copy

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  • 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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  1. Data 360 Consultant Checklist
  3. Data Source Connection and Ingestion

Normalizing denormalized source data before mapping

Explainer

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Practice Question

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Next conceptCode extension for custom Python batch transforms

Checklist progress

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0 of 198 concepts learned

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  1. Data 360 Consultant Checklist
  3. Data Source Connection and Ingestion
Normalizing denormalized source data before mapping

Explainer

Learn More

Practice Question

Keep going

Next conceptCode extension for custom Python batch transforms

Explainer

The Data 360 standard data model is normalized, so data must be in normalized form before you can map it to data model objects (DMOs). Many source systems export denormalized data instead, for example one contact row with three separate phone number columns. A data transform reshapes that data into normalized records after ingestion so that every value survives the mapping step.
Core information
  • Incoming data must be normalized before it can be mapped to the Data 360 standard data model, and not all source systems offer normalized export options, so assess the source's field-level data first to decide how to transform and map it.
More details and nuances
  • The source and target of a streaming data transform must be different objects, so normalization always writes to a separate target DLO, and that target DLO is the object you map.

Explainer

The Data 360 standard data model is normalized, so data must be in normalized form before you can map it to data model objects (DMOs). Many source systems export denormalized data instead, for example one contact row with three separate phone number columns. A data transform reshapes that data into normalized records after ingestion so that every value survives the mapping step.
Core information
  • Incoming data must be normalized before it can be mapped to the Data 360 standard data model, and not all source systems offer normalized export options, so assess the source's field-level data first to decide how to transform and map it.
More details and nuances
  • The source and target of a streaming data transform must be different objects, so normalization always writes to a separate target DLO, and that target DLO is the object you map.