Is this a tool purchase or a process programme?

Both, in that order. We fix ownership, definitions, and stewardship first, then choose tooling to enforce it. Tools bought before the operating model exists usually end up shelfware.

How does this affect our AI initiatives?

Directly. Retrieval and model accuracy depend on clean, well-labelled, permission-aware data — governance is the cheapest way to lift AI quality across every use case at once.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Data Governance & Master Data Management

One trusted definition of customer, product, and supplier.

Book Free Consultation All Services

90%+

Duplicate records resolved

1 glossary

Certified metric definitions

Daily

Automated quality scorecards

Overview

What Data Governance & Master Data Management means at NeonAITech

Conflicting records and undefined metrics quietly undermine every dashboard and AI model you build. We establish master data management, cataloguing, quality monitoring, and stewardship so the organisation argues about decisions instead of arguing about whose number is right.

Tools & Platforms

Collibra
Great Expectations
dbt
Informatica
OpenMetadata
Monte Carlo

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

Master Data Management

Matching, survivorship, and golden-record management for customer, product, supplier, and location domains.

Catalog & Semantic Layer

Business glossary, lineage, and certified metric definitions so a term means the same thing in every report.

Data Quality Engineering

Automated validation rules, quality scorecards, and alerting that catch bad data before it reaches a decision.

Capabilities

Inside the Engagement

Entity resolution and deduplication

Data ownership and stewardship operating model

GDPR, DPDP, and CCPA data handling

Retention, classification, and masking policies

Quality SLAs with automated monitoring

How We Work

A delivery rhythm you can see into

Every Data Governance & Master Data Management engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.