Platform · Collect

Unified Data Hub

A secure lakehouse that unifies SIS, LMS, CRM, web, and other sources into one trusted schema, powering analytics and agentic AI across campus.

Unified identities & events across systems

Connectors for SIS/LMS/CRM & files

Encryption in transit/at rest, PII minimization

Typical rollout: 4–6 weeks

Enterprise-grade security with FERPA/GDPR compliance
What it is

A governance-ready lakehouse schema for higher education

The Common Data Model (CDM) is a governance-ready lakehouse schema for higher education. It standardizes people, courses, sections, enrollments, interactions, and outcomes, and supports event-style telemetry so every downstream service, from alerts to planning to career, works off the same truth.

Single source of truth

All your institutional data unified in one trusted, queryable model

What it does

From ingestion to insight

Ingests and reconciles data from your SIS, LMS, CRM, and web sources; resolves identities; normalizes entities and events; and publishes clean data marts and feature sets to analytics tools and Infinize's agentic services.

Ingest data from all source systems
Resolve and unify student identities
Normalize entities and events
Publish clean marts for analytics and AI

See how it works

Watch the unified data hub in action

Capabilities

Key capabilities

Everything you need to build a trusted data foundation for analytics and AI

Near real-time data pipelines

Ingest and reconcile data from SIS, LMS, CRM, ERP, and web sources with near real-time streaming, so analytics and AI agents always work with the freshest data.

API-first architecture

REST and GraphQL APIs enable seamless integration with any campus system. Extensible connectors and webhooks let you plug in new sources without custom engineering.

Secure Lakehouse

A unified lakehouse that brings together SIS, LMS, CRM, and ERP data into one trusted schema, eliminating silos and powering analytics and agentic AI across campus.

Data governance & quality

Automated validations for completeness, accuracy, and freshness with full lineage tracking. Versioned schemas and change logs ensure analytics never break unexpectedly.

RBAC & auditability

Role-based access control with field-level masking, purpose-based permissions, and comprehensive audit logs, ensuring every data access is tracked and accountable.

FERPA/GDPR-ready compliance

Built-in privacy controls including PII minimization, encryption at rest and in transit, data retention policies, and regulatory compliance frameworks ready out of the box.

Outcomes you can measure

Proven results from institutions using the Unified Data Hub

Foundation
1
Unified view

Single source of truth

All institutional data unified into one trusted, queryable model that every team and AI agent relies on.

Efficiency
90%
Fewer silos

Reduced data silos

Consolidate fragmented SIS, LMS, CRM, and ERP data into a single lakehouse, eliminating redundant extracts and shadow databases.

Trust
100%
Audit-ready

Compliance-ready infrastructure

FERPA/GDPR-ready with full lineage, RBAC, access logs, and field-level masking built in from day one.

Speed
80%
Faster answers

Real-time insights

Near real-time data pipelines deliver fresh, reliable data so leaders and AI agents get answers in minutes, not days.

Integrations

Works with your existing systems

Prebuilt connectors for the systems you already use, plus downstream tools

Source Systems

Connect SIS (Banner, Workday, PeopleSoft), LMS (Canvas, Blackboard, Moodle), CRM (Salesforce, Slate), and files (CSV, PDF, forms).

Analytics Tools

Publish clean data marts to Tableau, Power BI, Looker, or your custom BI stack, with automatic refreshes and lineage tracking.

Infinize Services

Powers all agentic AI services, alerts, planning, major exploration, career recommendations, with unified, trustworthy data.

Trust & Compliance

Security, privacy, and ethics

Enterprise-grade protection and responsible data practices built into every layer

Security & privacy

Encryption at rest/in transit; VPC-isolated data plane
Role-based access control (RBAC) & least privilege
Field-level masking; secrets vault; key rotation
Audit trail for data access and agent actions
PII minimization and non-prod redaction

Responsible AI

Human-in-the-loop approvals for sensitive actions
Model cards & explainability for risk signals
Bias checks on features and outcomes
Opt-outs, purpose limitation, and data retention controls
Timeline

Implementation timeline

From kickoff to production in 4–6 weeks

1
Week 1

Discovery & setup

  • Credential exchange
  • Source system audit
  • Schema mapping workshop
2
Weeks 2–3

Ingestion & validation

  • Connector deployment
  • Initial data load
  • Quality checks & fixes
3
Week 4

Testing & UAT

  • Data mart validation
  • Access control setup
  • Stakeholder review
4
Weeks 5–6

Production & handoff

  • Go-live
  • Monitoring setup
  • Training & documentation
FAQs

Frequently Asked Questions

Ready to put a trusted data foundation under every decision and AI agent?

See how the Unified Data Hub powers personalized experiences and actionable insights across campus