System replacement
Translate legacy tables and reference data into the structures required by a new CRM, ERP, finance or operational platform.
01 · Data & Reporting
Data mapping: Make every field, key, rule and ownership decision visible before building the next layer.
Lumaivo maps how information moves from source systems to reports, integrations or replacement platforms. The result is a practical crosswalk between what exists now, what the business means, and what the target needs.
What this service is for
Start with the business meaning, then make the technical route explicit.
Data mapping is the point where technical structure and business meaning meet. A customer number in one system may be an account reference in another. Dates may describe booking, completion, invoice or payment. A blank field may mean unknown, not applicable or simply not captured. Those differences need to be documented before they are automated.
We profile approved source data, record field definitions and effective dates, identify candidate keys, and build source-to-target mappings with explicit transformations. Conflicts and unknowns remain visible for business review instead of being quietly resolved by guesswork.
Where it becomes useful
Common use cases, framed around the decision or hand-off.
Reporting alignment
Trace management measures back to source fields so teams can see why reports differ and which definition is approved.
Integration design
Define the payloads, keys, ownership rules and transformation logic needed before two systems exchange data.
Method
A controlled route from discovery to an accepted output.
- 01
Inventory
List the approved systems, extracts, reports, owners and business questions in scope.
- 02
Profile
Inspect structures, formats, completeness, duplicates, candidate keys and value distributions without changing the source.
- 03
Crosswalk
Map source fields to target fields or reporting concepts, including data types, permitted values and effective-date rules.
- 04
Resolve
Route ambiguous meanings, missing ownership and conflicting definitions to named business decisions.
- 05
Compile
Turn approved mappings into deterministic transformation specifications that can be tested and reused.
Typical deliverables
What the review-ready work can contain.
- Source and target data inventory
- Field-level mapping workbook or data dictionary
- Key, reference and relationship map
- Documented transformation and defaulting rules
- Open-question and decision register
- Validation criteria for the next migration, integration or reporting stage
Boundaries
What stays explicit instead of being over-promised.
- Source data is preserved; profiling does not silently correct production records.
- A mapping is not treated as approved until the relevant business meaning has an owner.
- Unknowns and one-to-many relationships are recorded explicitly.
- Sensitive fields are limited to the minimum needed for the agreed scope.
Questions before the first conversation
Plain answers, with the limits left in.
What is the difference between data mapping and data integration?
Mapping defines what fields and records mean and how they correspond. Integration implements the repeatable movement of that data between systems. A reliable integration normally needs an approved mapping first.
Can you map spreadsheets as well as databases?
Yes. Approved Excel and CSV files can be profiled alongside SQL tables, APIs, SaaS exports and manual reference files. The important part is recording ownership, structure and meaning.
Do we need the target system in place?
Not always. An early discovery map can document the current estate and business concepts before a target is selected. A source-to-target crosswalk needs the target structures or interface specification.
Will mapping fix bad data?
It identifies quality problems and defines how accepted values should be treated. Changes to source records or target-loading rules remain a separate, approved activity with validation and rollback controls.
What is needed to start?
A bounded business question, an owner for the source data, approved sample access or exports, and the reports or processes that show how the data is currently used.
Related services
Continue through the connected system.
02 · Data & Reporting
Data validation
Prove what is complete, consistent and fit for purpose - and surface what is not.
Read service 03 · Data & ReportingData migration
Move business-critical data through rehearsed, reconciled and reversible gates.
Read service 04 · Data & ReportingData integration
If it has a way out, we can usually connect it.
Read serviceStart with the messy version. Make the first useful outcome explicit.
Send the systems, reports, constraints and decisions involved. Lumaivo will help define a bounded first step and the evidence needed to accept it.
Map the first useful step