Master Data Management

Build the data foundation your AI can trust.

Reltio and Informatica MDM — implementation, migration and programme rescue. We treat master data as a governance change delivered through a platform, not a tool installation.

Deep hands-on work across Reltio and Informatica MDM
Match rules tuned against labelled samples, with precision and recall reported
Trust hierarchy agreed with the business before anything is configured
Independent on platform choice — no vendor commission, ever

Why programmes stall

MDM rarely fails on platform capability.

It fails on four decisions taken early, quietly, and usually by whoever happened to be configuring the tool that week. Each one is cheap to get right at the start and expensive to unwind later.

The golden record was defined by the source, not the consumer

The model grew outward from whichever system loaded first. Nobody asked which decisions the record has to support, so survivorship was configured for every attribute equally.

Trust hierarchy was set as a technical parameter

Trust scores encode a commercial judgement about which system is authoritative. Configured without that agreement, they get disputed the moment a business owner sees their system lose.

Match rules shipped on vendor defaults

Defaults are a starting point, not a configuration. Without precision and recall measured against a labelled sample, you have opinions about match quality rather than evidence.

Stewardship was sized to the configuration

If the platform generates more tasks than the team can clear, the backlog grows indefinitely — and to everyone downstream an unworked backlog is indistinguishable from bad data.

Capability

What we do across the master data lifecycle.

From strategy through implementation to the support retainer that keeps the record trustworthy after go-live.

Strategy & Governance

MDM strategy and maturity assessment
Data domain and roadmap definition
Business case development
Governance framework and operating model
Data ownership and stewardship design
Metadata, glossary and lineage strategy

Implementation

Reltio: L3 modelling, match, survivorship, RDM, workflows
Informatica: MDM Hub, IDQ, hierarchies, ActiveVOS
Crosswalk and reference data management
Initial load, delta strategy and safe reprocessing
Integration: API-led, CRM, ERP, warehouse and lake
Platform migration, including Informatica to Reltio

Quality & Operations

Data profiling, cleansing and standardisation
Match and merge tuning against labelled samples
Data quality scorecards and KPIs
Root-cause analysis and remediation
L1–L3 production support under SLA
Monitoring, enhancement and release management

Delivery standards

Ten commitments we hold to on every engagement.

These are the decisions that determine whether anyone trusts the golden record. We make them explicit at the start rather than discovering them at go-live.

Define the golden record by consumption, not by source

Start from the decisions and downstream systems the record has to serve. That determines which attributes genuinely need survivorship and which merely need storing — and it stops the data model being shaped by whichever source was loaded first.

Agree the trust hierarchy with the business before configuration

Trust scores encode a commercial judgement about which system is authoritative for which attribute. That is an ownership decision, not a technical one. Configuring it without that agreement guarantees it will be disputed after go-live.

Tune match rules against a labelled sample before go-live

Vendor defaults are a starting point, not a configuration. We measure precision and recall against a hand-labelled sample and publish the numbers. Over-matching destroys business trust faster than under-matching, and is far harder to unwind.

Make survivorship per-attribute, not per-record

The most reliable source for a postal address is rarely the most reliable source for a tax identifier or a credit status. Record-level survivorship is simpler to configure and consistently produces a golden record nobody quite believes.

Onboard sources in waves with explicit exit criteria

Every additional source changes match behaviour across everything already loaded. Sequencing sources with defined acceptance criteria is what makes a regression traceable to the wave that caused it.

Size the stewardship queue to the team you actually have

An MDM platform that generates more tasks than stewards can clear degrades quietly into a backlog, and a backlog is indistinguishable from bad data to the people consuming it. Auto-merge thresholds are tuned against real steward capacity.

Treat reference data as a governed domain

Country, currency, industry and status codes feed match rules and survivorship directly. Left as uncontrolled lookup tables, they undermine the golden record from underneath while every dashboard still reports green.

Design for reprocessing from day one

Match rules will be retuned after go-live — that is a sign the programme is working, not failing. If reload and re-match cannot be run safely and repeatably, tuning stops and the configuration ossifies at its least-informed state.

Baseline data quality before, and publish the scorecard after

Without a measured starting point, MDM cannot demonstrate what it changed, and becomes a cost line at the next budget review. The scorecard is agreed with the business at the start of the engagement.

Keep MDM out of the integration business

It is a mastering system, not an enterprise service bus. Absorbing orchestration and transformation logic couples the entire estate to the MDM platform and makes future migration prohibitively expensive.

Glossary

Master data management, in plain terms.

The words that come up in every MDM conversation, defined the way we use them with business owners.

Master data: The core entities a business runs on — customers, products, suppliers, locations, employees — shared across many systems.
Master data management (MDM): The discipline, processes and platform that keep one trusted version of those entities across every system that uses them.
Golden record: The single best version of an entity, assembled from all the sources that hold data about it.
Match and merge: Finding records that describe the same real-world entity and consolidating them into one.
Survivorship: The rules that decide which source supplies each attribute of the golden record when sources disagree.
Trust hierarchy: A ranking of sources by how authoritative each is for a given attribute — a business decision expressed as configuration.
Crosswalk: The link from the golden record back to each contributing source record and its identifier, so every merge can be explained and reversed.
Data stewardship: The people and workflow that review uncertain matches and correct the data that rules cannot resolve on their own.
Reference data: Controlled lists such as country codes, currencies or product categories that master data depends on.

Fixed scope, fixed price, fixed dates.

Most engagements begin with a short paid assessment. It de-risks your first commitment and gives you something useful whether or not we work together afterwards.

2–3 weeks

Master Data Management Health Check

Your platform is live and the business will not act on the record. Two to three weeks to find out why.

2 weeks

Data Readiness for AI

An AI initiative is blocked and the data is the reason. Assess whether the foundation supports it.

1–2 weeks

Platform Selection Review

Choosing between Reltio, Informatica and alternatives. An independent read against your domains.

Working on a specific platform?

Reltio consulting Informatica MDM consulting

Questions

Master data management: common questions.

What does a master data management consultant do?

Helps an organization decide which entities to master and who owns them, designs the data model, match and survivorship rules, implements them on a platform such as Reltio or Informatica, and sets up the stewardship and governance that keep the golden record trusted after go-live.

Reltio or Informatica: which is better?

Neither in general. The right choice depends on your domains, existing estate, integration patterns, skills and commercial terms. We assess the options against your situation and accept no commission from any vendor.

Why do MDM programmes fail?

Rarely because of the platform. Most stall on four early decisions: a golden record designed around the source instead of the consumer, a trust hierarchy set without business agreement, match rules left on defaults, and stewardship sized to the configuration instead of the team.

How does master data management relate to AI?

AI is only as intelligent as the data behind it. Models and agents that read duplicated customers or conflicting definitions produce confident, wrong answers. Resolved entities and agreed definitions are the foundation an AI use case needs before it can be deployed.

How long does an MDM implementation take?

It depends on the number of domains and sources and how much agreement already exists on ownership. We give an estimate after a short scoping assessment rather than a generic number, and phase delivery so the first domain proves value early.

Where should we start?

If you are live and the business does not trust the record, start with a master data management health check. If an AI use case is blocked by data, start with the data readiness for AI assessment. If you are choosing a platform, start with a platform selection review.

Tell us where the programme actually is.

Whether you are choosing a platform, mid-implementation, or live with a record nobody trusts — describe the situation and we will tell you plainly what it would take, and whether we are the right team for it.

Reltio and Informatica are trademarks of their respective owners. VYLQORA implements, migrates and supports these platforms; naming them describes the technologies we work on and does not imply partnership, certification or endorsement by either vendor. Our platform recommendations are independent — we accept no commission, referral fee or incentive from any software vendor.