Customer Data Readiness Hub glossary

Customer Data Readiness Hub (CDRH)

A centralized framework designed to unify disparate data sources, streamline integration, and provide a trusted, compliant, and actionable view of customer data for use across operations, analytics, and marketing.

Data readiness

The state of having data that is accurate, complete, timely, actionable, trusted, and compliant—ready for use in business operations, analytics, and customer engagement.

Automated data quality

Continuous, identity-centric discipline that ensures data is clean, standardized, validated, and unified at every stage of the pipeline, resulting in a single, trusted profile (Golden Record).

Standardization

The process of unifying data formats, schemas, and values (e.g., names, addresses, emails, phone numbers) to ensure consistency and reliability across all systems and channels.

Validation

The process of checking data for accuracy, completeness, and adherence to required formats or business rules.

Cleansing & enrichment

Improving data quality by correcting errors, removing duplicates, and appending additional information (e.g., postal verification, geocoding, enrichment lookups).

Golden Record (GR)

A continuously updated, unified customer profile that serves as the single source of truth across the enterprise.

Identity resolution

The process of matching, merging, and relating signals to the correct entity (person, household, account) using deterministic and probabilistic rules, resulting in a governed, consent-aware identity.

Profile unification

The end-to-end process of ingesting, standardizing, matching, merging, and governing customer data to create persistent, activation-ready profiles (e.g., Individual, Household, Customer, Loyalty, Member).

Survivorship

Policy that selects which source “wins” for each attribute in a unified profile, based on trust, recency, and completeness.

Identity graph

A structure linking people, households, devices, accounts, and events to enable comprehensive identity resolution.

Anonymous-to-known identity

The process of transitioning users from anonymity to a known identity by stitching together session and device identifiers and linking them to a specific individual when a deterministic signal is detected.

Metadata

Contextual information that describes, structures, and governs data throughout its lifecycle. Types include:

• Source metadata: Origin, collection method, consent status.

• Structural metadata: Schema, field names, data types.

• Semantic metadata: Business meaning and context.

• Operational metadata: Data freshness, error rates, transformation history.

• Taxonomic metadata: Classification systems and controlled vocabularies.

Data lineage

The history of data’s origin, transformations, and usage, supporting auditability and compliance.

Data stewardship

Roles and processes responsible for maintaining data quality, integrity, and compliance.

Privacy, compliance & trust

Principles and practices ensuring data is handled in accordance with regulations (GDPR, CCPA), customer consent, and organizational policies.

Consent management

Capturing, storing, and enforcing customer permissions (opt-in/opt-out) for data use, including integration with Consent Management Systems (CMS).

Data access rights

Granular controls over who can view or manipulate specific data, enforced via role-based access control (RBAC), sensitivity tags (PII, PHI), and consent metadata.

Auditability

Comprehensive logging of data access, modifications, and exports for compliance and transparency.

Data orchestration

The intelligent, governed, and agile flow of data across systems, supporting real-time, batch, and streaming processes for unified customer engagement.

Realtime decisions

The ability to capture, unify, decide, orchestrate, and learn from customer interactions in real time, leveraging identity resolution and context-aware decisioning.

Segmentation

Dynamic, data-driven grouping of customers for personalized engagement, built on the Golden Record and leveraging behavioral, demographic, transactional, and contextual data.

Data ingestion

The secure and scalable onboarding of external data sources (databases, files, APIs, streams) into the Data Readiness Hub, with built-in validation, standardization, and enrichment.

Feed layout

A structured template outlining expected columns, keys, and validations for a specific subject area.

Connector

An adapter facilitating the movement of data between external systems and Redpoint.

Landing zone

A secure object-store for initial deposits of raw data before processing.

Conformation

Mapping raw data to the canonical model for consistency and usability.

Ingestion options (Snowflake example)

• Snowflake Table: Static table, manual/ETL load.

• Snowflake View: Logical layer, always up-to-date.

• Dynamic Table: Materialized, auto-refresh.

• Stream + Task: Change data capture, scheduled tasks.

• External Table: References external storage (e.g., S3).

Customer Data Readiness Hub

CDRH

Redpoint Data Management

RPDM, DM

Redpoint Interaction

RPI

Roles Based Access Control

RBAC

Feed Layouts

FL

Personally Identifiable Information

PII

Protected Health Information

PHI

Canonical model

Standardized schema for representing key entities (people, households, events, etc.).

Audit trail

Record of all data access and changes for compliance and governance.

Activation

Making unified data actionable for downstream systems (marketing, analytics, personalization).

Data quality KPIs

Metrics such as schema conformance rate, duplicate rate, golden coverage, consent coverage, event integrity, activation reliability, and time-to-onboard.