MASTER DATA MANAGEMENT
MDMAi
An enterprise agentic AI platform that moves master data management from batch profiling and ticket-based stewardship to continuous stewards authorizing every consequential decision.
Discover · Convert · Validate
T H E PROBLEM
A duplicate list is a backlog
A Golden Record is an asset
Every enterprise runs customer data in a dozen systems CRM, ERP, billing, claims, portal, contact center and marketing. Each creates its own version of the same person. Traditional MDM profiles that data in monthly batches, matches it with static rules, and routes every ambiguous case to a human ticket queue. The rules find duplicates faster than stewards can clear them, and the Golden Record decays between runs
92%
Completeness against a
≥95% enterprise target
3%
Duplicate rate against a <1% target
94%
Consistency against a ≥97% target
50–80%
Stewardship effort that is
manual triage
W H Y TRADITIONAL MDM STALLS
01
Rules without learning
Match, merge and survivorship rules are configured once and decay. They cannot absorb a new source system, a new naming convention or a steward’s correction. Every improvement becomes a change request routed to developers.
02
Batch by design
Profiling runs monthly and matching runs nightly. Between runs, CRM, analytics and AI platforms consume a record that is already stale and every downstream decision inherits
that staleness
03
Queue-bound stewardship
Ambiguous matches become tickets. Ticket volume scales with data volume; stewards do not. The highest risk records wait in the same queue as routine formatting corrections.
T H E OPERATING MODEL
Three layers that turn source records into trusted records
MDM AI is built on three integrated, independently scalable layers over a canonical customer data model and a source system cross-reference (XREF). Together they form a closed loop that moves from raw ingestion to Golden Record to downstream consumption continuously with steward oversight wrapped around every consequential decision.
01
Data Quality & Profiling
Profiles every source on arrival, validates against business rules, and standardizes names, addresses, phones and emails before identity resolution ever runs.
Metadata & structural profiling
Name, address, phone, email standardization
Content & relationship profiling
02
Agentic AI
Resolves identity across systems, decides which records merge, selects the surviving value for every attribute, and adapts continuously from steward decisions.
10 purpose-built MDM agents
Attribute-level survivorship intelligence
Deterministic, probabilistic & graph matching
03
Publishing & Stewardship
Publishes Golden Records to every consuming system through policy driven APIs and events, then monitors delivery, self-heals failures and routes exceptions to stewards
REST, GraphQL, Kafka, CDC publishing
Steward workspace & work queue
CRM, ERP, CDP, data lake, AI/ML
The Agent Inventory
Ten agents Each with a trigger, a memory, and an escalation rule.
MDM AI is a multi-agent system. Agents run in parallel through the orchestration framework and share state via the canonical customer model and the XREF. No agent has unilateral authority to merge above the materiality threshold, to override a verified identifier, or to publish restricted attributes that boundary is enforced in the architecture.
Profiling Agent
Continuously profiles metadata, structure, content and relationships across every source; emits a live data quality scorecard.
Rule Validation Agent
Validates records against business rules and scores completeness, accuracy, validity, consistency and timeliness.
Standardization Agent
Normalizes names, addresses, phones and emails to reference standards at ingest correcting before matching, not after.
Data Matching Agent
Runs deterministic, probabilistic, ML-similarity and graph resolution; emits a calibrated confidence score per candidate pair.
Merge Decision Agent
Determines whether matched candidates consolidate; auto merges high confidence and routes the 85–94 band to a steward.
Survivorship Agent
Selects the winning value per attribute from source trust, freshness, verification status and prior steward decisions.
Conflict & Anomaly Agent
Reconciles contradictory attributes with cited evidence and detects distribution drift before it reaches the Golden Record.
Lineage & Audit Agent
Records attribute-level provenance, agent reasoning and approvals into an append-only, hash-chained audit trail.
Steward Workflow Agent
Classifies issues, determines root cause, assigns work, monitors SLAs and escalates overdue or high-risk items automatically.
Publishing Agent
Distributes Golden Records under policy and attribute-level security; retries, queues and self-heals on consumer failure.
ENTERPRISE USE CASES
Six workflows, one closed loop from source record to Customer 360.
Each use case follows the same loop: new data arrives, the right agents reason and act, and a steward authorizes anything consequential. Every decision is reversible and carries its own lineage. The impact figures below reflect the platform’s target outcomes.
V A L U E REALIZATION
Baselines, 12-month and 24-month targets.
Baselines are drawn from the profiling reference model 12 and 24 month figures are platform targets tracked on a live data quality dashboard the basis for governance reporting and trust scoring on every published record
D A TA QUALITY OUTCOMES
| Metric | Baseline | 12-MO | 24-MO |
|---|---|---|---|
| Completeness | 92% | 96% | > 98% |
| Uniqueness (duplicate rate) | 97% (3%) | 98.5% | > 99% (< 1%) |
| Validity | 98% | 98.8% | > 99% |
| Accuracy | 96% | 97.5% | > 98% |
| Consistency | 94% | 96% | > 97% |
| Golden Record confidence | Not Measured | 90% | > 95% |
O P ERATIONAL PERFORMANCE
| Metric | Baseline | 12-MO | 24-MO |
|---|---|---|---|
| Auto-remediation success | Manual | 70% | > 85% |
| Steward review rate | All exceptions | 12% | < 5% |
| Mean time to resolve DQ issues | Days | < 24 hr | < 4 hr |
| Source-to-Golden latency | 24 hrs | 2 hrs | Near real-time |
I M P L EMENTATION
Twelve stages across four phases
Capability is built progressively data foundation and governance first, mastering agents second, enterprise scale operations third, and continuous optimization throughout each phase gated by validation before the next begins.
Frequently Asked Questions
MDMAi is an enterprise agentic AI platform for master data management. Ten purpose-built agents profile, standardize, match, merge and publish customer data continuously, turning records scattered across CRM, ERP, billing, claims and other systems into a trusted Golden Record, with data stewards authorizing every consequential decision.
Traditional MDM profiles data in monthly batches, matches it with static rules and routes every ambiguous case to a ticket queue. MDMAi profiles data as it arrives, uses learned matching that improves from steward corrections, and prioritizes stewardship work by risk, so the Golden Record stays current instead of decaying between runs.
Only when it is safe to. High-confidence matches merge automatically, while candidates in the 85–94 confidence band are routed to a data steward. No agent can merge above the materiality threshold, override a verified identifier or publish restricted attributes on its own. That boundary is enforced in the architecture, and every merge is reversible.
The Data Matching Agent runs deterministic matching on verified identifiers first, then probabilistic, ML-similarity and graph resolution across channels, devices and relationships. Every candidate pair gets a calibrated confidence score with the evidence behind it, and every source ID is mapped to a Golden Customer ID in the cross-reference (XREF).
Through attribute-level survivorship. Instead of a fixed rule like “CRM always wins”, each attribute is selected on source trust, freshness, verification status and prior steward decisions. The reasoning behind every choice is retained, and conflicting values are reconciled with cited evidence by the Conflict & Anomaly Agent.
Yes. Every steward decision becomes a labeled training pair that recalibrates match thresholds, so new source systems, naming conventions and corrections are absorbed continuously rather than through change requests to developers.
The Publishing Agent distributes Golden Records under policy and attribute-level security to CRM, ERP, CDP, contact center, data lake and AI/ML platforms through REST, GraphQL, Kafka event streaming and CDC. It monitors delivery, retries and self-heals on consumer failures, and routes exceptions to stewards.
The Lineage & Audit Agent records attribute-level provenance, agent reasoning and steward approvals in an append-only, hash-chained audit trail, so you can trace any value in a Golden Record back to its source and see exactly who or what decided it.
MDMAi is delivered in twelve stages across four phases: Foundation (months 1–3), Build (months 4–6), Deploy (months 7–9) and Operate & Optimize (month 10 onward). Each phase is gated by validation before the next begins, and progress is tracked against baselines on a live data quality dashboard.
Yes. Try the interactive demo on this page to watch the ten agents build a Golden Record from sample data, or request a demo to see MDMAi on your own data with our team.