Quality profiling
Assess critical fields against agreed completeness, validity and consistency rules; identify whether correction belongs at the source.
Data Quality & MDM
Data Quality & MDM
Improve critical data through profiling, quality rules, matching, stewardship, reference data, and master-data operating processes.
Inside the delivery
Resolve quality issues without losing record ownership
Measure completeness, validity and duplication for the entities that affect business decisions. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Profile critical entities
Measure completeness, validity and duplication for the entities that affect business decisions.
Output
Quality baseline and entity inventory
- 02
Rules and matching
Define validation, identity matching and survivorship rules with business stewards.
Output
Quality rules and matching specification
- 03
Stewardship workflow
Route uncertain matches and conflicting values for review with an auditable decision history.
Output
Review queue and master-record workflow
- 04
Monitor and maintain
Track rule failures, false merges and source changes; assign correction at the right system.
Output
Quality monitoring and stewardship playbook
Controls across the workflow
- Steward approval
- Match confidence
- Merge audit trail
- Source correction
Decisions that shape the scope
- Can matching rules create a reliable master record automatically?
- Some records can be matched confidently; ambiguous cases need review. Survivorship rules must reflect authoritative sources, and merge decisions need a way to investigate or reverse mistakes.
- Do we need a dedicated MDM platform?
- Often not for a single domain. Matching, survivorship and stewardship can be implemented in the data platform you already run. A dedicated product earns its cost across multiple domains with complex hierarchies and a large steward population.
Before you commit
Is this the right engagement?
- What we need from you
- Entity definitions, source records, identifiers, known duplicate examples, authoritative-source rules and available stewards.
- How you accept the work
- Track rule failures, false merges and source changes; assign correction at the right system. Acceptance records the tested scope, unresolved issues and the owner's decision.
- Scope & alternatives
- Some records can be matched confidently; ambiguous cases need review. Survivorship rules must reflect authoritative sources, and merge decisions need a way to investigate or reverse mistakes.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Entity resolution
Design matching and survivorship for customers, products or other shared entities, including uncertain matches and conflicting authoritative values.
Steward review
Build review queues, decision history and merge investigation so business owners can manage exceptions without losing provenance.
Master-data distribution
Define how approved master records reach consuming systems, including identifiers, update propagation and failed synchronization.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Can matching rules create a reliable master record automatically?
Can matching rules create a reliable master record automatically?
Some records can be matched confidently; ambiguous cases need review. Survivorship rules must reflect authoritative sources, and merge decisions need a way to investigate or reverse mistakes.
02Do we need a dedicated MDM platform?
Do we need a dedicated MDM platform?
Often not for a single domain. Matching, survivorship and stewardship can be implemented in the data platform you already run. A dedicated product earns its cost across multiple domains with complex hierarchies and a large steward population.
03Can duplicate matching be fully automatic?
Can duplicate matching be fully automatic?
Mostly, but not entirely, and claiming otherwise is how bad merges happen. Confident matches merge automatically, ambiguous ones route to a steward, and the threshold between them is a business decision we set with you.
04Where should we start?
Where should we start?
With the entity that causes the most argument, usually customer or product. One domain done properly establishes the rules, the stewardship pattern and the measurement approach that every later domain reuses.
05What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Entity definitions, source records, identifiers, known duplicate examples, authoritative-source rules and available stewards.
06What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes quality monitoring and stewardship playbook. Track rule failures, false merges and source changes; assign correction at the right system.
07How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Entity definitions, source records, identifiers, known duplicate examples, authoritative-source rules and available stewards. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Related services and delivery options
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Data Quality & MDM.