Discover and profile
Inventory source systems, tables, files, identifiers, attachments, quality issues and retention needs.
Map and validate client, project and investigation records from spreadsheets, legacy databases and gINT exports before operational cutover.
Australian geotechnical workflow
Geotechnical data migration is not a file-copy exercise. Legacy databases often contain duplicate project identifiers, inconsistent location names, custom fields, attachments and records whose meaning depends on old templates or local knowledge. GeoCRM migration should begin with discovery and a controlled mapping of clients, projects, locations, logs, samples, results and documents. Validation and staged cutover protect traceability and allow the business to retain an archive where transformation would remove important context.
Identify the systems, files, identifiers and record quality that shape the migration.
Map clients, projects, locations, investigations and external references into GeoCRM structures.
Test representative records and reconcile counts before wider import.
Sequence migration with configuration, training and operational cutover.
Inventory source systems, tables, files, identifiers, attachments, quality issues and retention needs.
Define how legacy fields, codes, units and relationships translate into the GeoCRM model.
Migrate a representative subset and reconcile counts, relationships, samples and high-value records.
Run the approved migration, record exceptions, retain the required archive and verify operational readiness.
Understand schema, customisations and dependencies before committing to a migration path.
Retain project, location, sample and external references needed to recognise historical data.
Keep clients, projects, investigation points, logs, results and documents connected.
Expose duplicates, missing keys, invalid codes and records that need business decisions.
Use test imports and acceptance checks before production cutover.
Keep source evidence available when not every legacy element should be transformed.
Potentially. The approach depends on the gINT schema, custom fields, libraries, file stores and required outputs, so discovery is essential.
Not always. Active, reusable and legally required data may justify transformation; other records may be retained in a searchable archive.
Use reconciliation counts, relationship checks, sampled technical review and business acceptance criteria documented before cutover.
Small, clean datasets may move quickly, but complex legacy systems usually need profiling, trial imports and staged validation.
See it with your own workflow
Bring a real project, schedule, field record, laboratory request or report. We will show how the workflow can be configured without asking you to share confidential client data.