Connect records and keep the meaning
Different systems describe customers, products, and transactions in different ways. We connect those sources and apply agreed rules so the combined data has consistent formats, clear relationships, and definitions users can follow.
- SourcesIdentify records and definitions in each system
- IntegrationEstablish the route and frequency for each feed
- TransformationApply mappings, standardization, and matching rules
- ValidationCheck relationships and isolate unresolved differences
- PublicationDeliver the combined dataset to its intended users
Resolve differences at the source
Combining files does not resolve conflicting identifiers, units, or business definitions. We work through those differences with the relevant owners, then implement the connections and transformation rules needed for a usable result.
Identify what each source contributes and which record takes precedence when values conflict.
Work with the interfaces, export structures, and access limits of the systems involved.
Define where combined datasets will live and how consumers should access them.
Shape source records into the relationships and measures required by reporting.
Review current extracts and transformations to determine what can be reused or improved.
Replace one-off combinations with a defined process for applying new and changed records.
Connect the flow and define the rules
- Source coordinationAccount for differences in availability, refresh frequency, and ownership across feeds.
- Database and application accessChoose extraction methods that suit source capabilities and operational constraints.
- Platform feedsHandle the formats, pagination, and update patterns of upstream interfaces.
- Replay and orderingDefine how late arrivals, repeated messages, and interrupted transfers are handled.
- StandardizationNormalize agreed formats and units while retaining the original values where needed.
- Business calculationsImplement formulas and classification rules with examples their owners can verify.
- Record matchingJoin related records using defined keys and a process for ambiguous matches.
- Analytical structuresBuild the tables and relationships required for the intended queries.
- Model-ready datasetsPrepare inputs with consistent transformations and documented treatment of missing values.
Make every change traceable
A shared dataset needs rules people can understand and review. We document how source values become published fields, including the exceptions that cannot be resolved automatically.
- Field definitionsDescribe what each source field represents before mapping it to a target.
- Identifiers and keysEstablish how the same entity is recognized across different systems.
- Source precedenceAgree which source supplies a value when several records disagree.
- Formats and unitsSpecify conversions for dates, currencies, quantities, and other relevant values.
- Derived fieldsDocument the calculations that produce new measures or categories.
- Missing informationDefine whether an absent value is retained, rejected, or resolved through an agreed rule.
- Duplicate handlingIdentify repeated records and apply the approved method for resolving them.
- Relationship checksCheck that related records join as intended and expose unmatched items.
- Exception reviewGive data owners a way to inspect cases that need a business decision.
- Mapping versionsKeep a record of rule changes so earlier outputs can be understood.
- Change testsCompare expected results before publishing a revised transformation.
- Consumer coordinationCommunicate field or definition changes to the teams using the output.
The result should be a dataset whose meaning is explicit. Mapping documents, validation results, and exception handling help your team maintain that meaning as source systems change.
Which records need to come together?
Bring a sample of the sources, the result you want to produce, and the mismatches you already know about. We can work through the mappings, business rules, and integration requirements with your team.