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Trillium — Data Quality & Data Governance

Last reviewed: 2026-09-17

Trillium (now part of Precisely) is a data quality and data governance platform. It lets organisations profile, cleanse, standardise, match and monitor data across enterprise systems so that records stay accurate, consistent and auditable.

Overview

Trillium is used by data engineers, data stewards and compliance teams to:

  • Profile data — analyse structure, completeness and patterns
  • Cleanse data — correct, standardise and enrich records
  • Match and merge — identify duplicates across datasets (MDM)
  • Monitor quality — track data quality metrics over time
  • Govern data — define policies, rules and ownership

A job is typically a pipeline: read a source → profile → apply quality rules → standardise → match against a reference → write cleansed output plus a quality report.

Topics Covered

  • Data profiling and column statistics
  • Data quality dimensions and rule definition
  • Standardisation, parsing and enrichment
  • Deterministic vs probabilistic matching
  • Survivorship rules and householding
  • Running quality jobs as containerised batch processes
  • Data source connectivity and job configuration
  • Integration with transformation and governance tooling

Key Trillium Capabilities

Data Profiling

Automated analysis of data sources to uncover:

  • Column statistics (min, max, mean, nulls, distinct values)
  • Data type inference and pattern detection
  • Frequency distributions and value ranges
  • Referential integrity between tables
  • Anomaly detection (outliers, unexpected values)

Data Quality Rules

Define quality dimensions:

Dimension Description Example Rule
Completeness Missing values "Email must not be null"
Uniqueness No duplicates "Customer ID must be unique"
Validity Correct format "Phone number must match +1-XXX-XXX-XXXX"
Accuracy Correct values "Zip code must match city"
Timeliness Up-to-date "Last contact must be < 90 days"
Consistency Cross-system agreement "Same customer, same name in CRM and ERP"

Data Matching & Deduplication

  • Deterministic matching — Exact field matches (SSN, email)
  • Probabilistic matching — Fuzzy/statistical matching (name + address + DOB)
  • Survivorship — Rules for which record "survives" in a merge
  • Householding — Group related records (same family, same address)

Data Enrichment

  • Address validation (via postal databases)
  • Geocoding (lat/long from address)
  • Name parsing (given name, surname, prefix, suffix)
  • Phone and email standardisation

Running Trillium as a Container

Data quality jobs are usually scheduled batch processes. Packaging the engine as a container makes the job reproducible across environments — the deployment shape is a service definition plus configuration for source connectivity, licensing and storage:

# Conceptual deployment shape for a containerised quality job
services:
  trillium:
    image: precisely/trillium:latest
    ports:
      - "8080:8080"
    environment:
      - TRILLIUM_LICENSE_KEY=${LICENSE_KEY}
      - DB_CONNECTION=${DB_CONNECTION_STRING}
    volumes:
      - ./config:/etc/trillium
      - ./data:/data

Operational notes that apply to any such deployment:

Concern Practice
Configuration Mount configuration read-only; keep it under version control
Credentials Inject via environment variables or a secret store, never baked into the image
Source connectivity Grant a dedicated read-only database account per source system
Ports Expose the admin/API port only on the internal network
Outputs Persist cleansed data and rejection reports to a mounted volume
Scheduling Run as a batch job (cron, Airflow, or an orchestrator) with exit-status alerting

Use Cases

  • Data migration — Profile and cleanse data before moving to a new system
  • Data warehouse — Ensure quality before loading into analytical databases
  • Master Data Management (MDM) — Match and merge customer/product records
  • Regulatory compliance — GDPR right-to-erasure, data accuracy requirements
  • Data lake governance — Catalog, profile and monitor source data
Tool Category Trillium Relationship
Precisely Spectrum Data integrity suite Rebranded platform
Great Expectations Open-source data quality Lightweight alternative
dbt (data build tool) Data transformation Can integrate with Trillium profiles
Apache Atlas Data governance Open-source governance catalog
Alation / Collibra Data catalog Enterprise competition

Resources