Master Data Management for AI Data Quality

Golden Record Agent

Over 80% of AI projects fail because of poor data quality. valantic’s Golden Record Agent identifies, cleanses, and governs your master data so your AI initiatives have a foundation they can rely on.

Ready to establish a reliable data foundation?
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Better data, better decisions.

More than 80% of AI, data, and analytics projects fail. The leading cause is not a lack of algorithms or compute capacity. It is poor data quality. Duplicated records, inconsistent fields, outdated entries, and contradictory information across systems make it impossible to build reliable AI, no matter how sophisticated the models.

Any organization that intends to use AI at scale needs a data foundation that is clean, consistent, and governed. That foundation rarely exists by default. It has to be built deliberately, with the right tools, the right governance, and the right approach to keeping it that way over time.

valantic’s Golden Record Agent addresses this directly. It combines systematic data quality analysis, AI-supported cleansing and harmonization, and embedded governance into a structured program that gives your organization a master data foundation it can trust.

Where most data journeys stall

The same four patterns come up in almost every organization that has attempted to build on its data:

Duplicate, incomplete, and inconsistent records across systems

Master data rarely lives in one place. Business units maintain separate copies. Migrations create orphaned records. Manual entry introduces errors that compound over time.

Limited trust in data for reporting, automation, and AI

When data quality is uncertain, people compensate with manual checks, shadow spreadsheets, and gut-feel overrides. AI initiatives built on this foundation inherit the same uncertainty.

No governance to maintain quality over time

Many data quality initiatives produce a one-time cleanup. Without defined ownership, documented rules, and automated checks, the data degrades again within months.

High manual effort to identify and correct issues

Without automated detection, data quality work is slow and reactive. Teams discover problems through incidents or complaints, not systematic monitoring.

How we help: three steps, one data foundation

The Golden Record Agent program moves in sequence from visibility to cleansing to governance. Each step builds on the previous one and produces specific outputs your team can act on.

01 · Data Quality Assessment

A systematic, measurable baseline of your master data quality: where the problems are, how bad they are, and what the business impact is.

02 · AI-Supported Cleansing and Harmonization

AI agents that scan, identify, and correct master data issues at scale, with transparent and consistent updates.

03 · Governance and Sustainable Data Quality

Governance rules formalized, automated checks implemented, and quality maintained without continuous manual intervention.

Data Quality Assessment

The first step creates an objective, measurable baseline. Before anything can be fixed, the extent and nature of the problem must be understood.

  • Systematic data quality analysis: specialized tools and methods uncover duplicates, missing values, inconsistencies, and outdated records across systems
  • Identification of problem areas: data-driven approaches identify root causes and make them measurable, replacing anecdotal reports with evidence
  • Automated error detection: algorithms detect and categorize quality deficiencies, showing where issues occur, how often, and with what business impact
  • Output: a starting point the leadership team can agree on, and a clear list of what needs to change, ordered by priority
Colleagues reviewing data analytics on a tablet and screen

AI-Supported Cleansing and Harmonization

The second step cleans and standardizes master data at scale. This is where AI agents do the work that would otherwise require extensive manual effort.

  • Identification of hidden inconsistencies: incomplete, stale, or contradictory information is often buried in master data and document attachments. We surface these patterns systematically
  • AI agents for master data and documents: agents scan master data and related documents to detect gaps, conflicts, and rule violations based on your business logic and quality rules
  • Continuous validation and enrichment: specialized agents flag and correct data issues automatically, with transparent and consistent updates to master data
IT professional reviewing data on a laptop in a server room

Governance and Sustainable Data Quality

The third step anchors data quality in the organization. Without it, a cleansed dataset degrades again within months.

  • Making governance rules explicit: governance rules for master data are often implicit, fragmented, or undocumented. We surface and formalize them
  • Data-driven rule discovery: by comparing extraction results with expert validation, we derive and document the underlying business rules and quality criteria
  • Automated governance checks: once defined, governance rules are embedded into automated checks that detect deviations early and ensure sustainable, audit-proof master data quality
Businesswoman and businessman reviewing data charts on a laptop

What you take away

Five concrete results your organization gains from the Golden Record Agent program:

  1. 1

    70 to 90% reduction in duplicate records

    Automated matching and consolidation across systems eliminates the data redundancy that undermines reporting and AI.

  2. 2

    20 to 40% improvement in measurable data quality

    Master data standardization and systematic cleansing produce a measurable uplift that can be tracked over time.

  3. 3

    Higher process efficiency

    Clean data reduces manual corrections, exception handling, and downstream disruptions across operations.

  4. 4

    Stronger foundation for AI and analytics

    Harmonized master data enables reliable reporting, automation, and AI use cases that were previously blocked by data quality issues.

  5. 5

    Documented governance rules and automated checks

    Data quality is maintained without continuous manual intervention, with rules embedded into the operating model.

Proven in practice

valantic’s intelligent master data migration cut migration efforts by 57% and reduced future data handling by 90%, enhancing data quality and operational efficiency.

The data governance workshop at Roche laid the foundation for a robust data management organization, creating a clear 18-month roadmap that strengthens data quality, governance, and readiness for the upcoming SAP migration.

Our AI-driven intelligent master data migration reduced future data handling by 90%, significantly cutting migration effort and sustainably improving data quality and operational efficiency.

See all valantic case studies for more examples across industries.

First step: the Golden Record Discovery Workshop

Before committing to a full program, most organizations benefit from a structured diagnostic. The Golden Record Discovery Workshop evaluates your data landscape and maturity, identifies key data quality and governance gaps, and defines concrete next steps toward a Golden Record strategy.

Pre-workshop questionnaire

Focused on your most critical data domains and systems.

Workshop session

Stakeholder interviews, data maturity assessment, use case identification, and gap analysis across data, processes, and governance.

Findings report and roadmap

Current-state summary, prioritized recommendations, and a phased roadmap with quick wins and long-term initiatives.

Format: One-day workshop including preparation and a written follow-up report

Investment: EUR 10,000

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Ready to establish a data foundation you can rely on?

If your AI or analytics initiatives are stalled by data quality issues, or if you do not yet know the extent of the problem, the Golden Record Discovery Workshop is the right place to start. One focused day produces a clear picture of where you stand and what the path forward looks like.

Fabian Schepp Manager valantic

Fabian Schepp

Manager

valantic