Reviewed Matches and Trusted Attributes
We design matching and source-priority rules for agreed entities and attributes. Ambiguous cases go to review, with traceability and correction procedures for incorrect matches or merges.
Analytics
Master Data Management
Consistent Customer, Product, and Supplier Records
We help teams reconcile important business records across selected systems. Matching rules, source priorities, and stewardship workflows define how records are reviewed and maintained, with integration patterns chosen for your operating needs.
What We Deliver
Where We Make the Difference
Different systems can describe the same customer, product, or supplier in conflicting ways. We agree on how those records are identified, which sources take precedence, and who reviews uncertain matches or changes before implementing the supporting technology.
A Domain-by-Domain Approach
We start with priority domains and reuse common governance practices where they fit. Each domain can retain the definitions, review rules, and workflows its business owners need.
Controlled Publishing to Consuming Systems
We publish approved changes to selected systems using supported interfaces and agreed update schedules. Monitoring and reconciliation help identify failed updates or conflicts.
Integration With ERP and CRM
We map identifiers and attributes between the selected systems and the master-data solution. Ownership, update direction, and conflict-handling rules are defined before synchronization is enabled.
Our Approach
How We Deliver Master Data Management
Step 1: Profile and Prioritize Domains
We profile master data for duplicates, gaps, and conflicts across systems, then rank domains by business impact and how feasible they are to fix first.
Step 2: Define Matching and Source Priority
We design the data model for each domain, the matching logic that finds likely duplicates, and source-priority rules (often called survivorship) that decide which system's value is kept for each attribute. Review thresholds send ambiguous matches to a steward.
Step 3: Test, Review, and Connect
We test match rules on representative records, review uncertain merges with your stewards, and validate integrations before broader rollout. Correction or unmerge procedures are set up where the platform supports them.
Step 4: Equip the Stewards
We train data stewards and set up review routines and data quality dashboards. Go-live includes an agreed stabilization period, acceptance criteria, and named business and technical owners.
Which master data domain should we start with?
We prioritize domains by business impact, data readiness, available ownership, and integration complexity. A focused first domain helps test matching rules and stewardship before expansion.
Do we need a dedicated MDM platform?
Not always. A narrow scope may be supported by existing data tools and clear stewardship. We assess domain complexity, review workflows, publishing requirements, and operating cost before recommending a dedicated platform.
Who owns master data after go-live?
Accountable business owners define policies and approve important decisions. Data stewards manage day-to-day quality and review work, while IT or platform teams operate integrations and technical controls. We agree on these responsibilities during delivery.
How is MDM different from data governance?
Governance establishes policies, ownership, and standards across data. MDM applies those practices to selected business entities, using matching, review, and publishing processes to maintain consistent records across agreed systems.
What happens if two records are matched incorrectly?
We define review thresholds, retain relevant source identifiers and change history, and establish correction procedures. Where the platform supports it, these can include reversing a merge and reconciling affected downstream records.
Keep Exploring