Data Quality & Migration

Cleaning the data before building on it

You can't build decision support tools on garbage data. Product catalogues have duplicates. Customer records are incomplete. Legacy system data doesn't map to new structures. Manual exports create inconsistencies. Teams know the data is messy but don't have capacity to fix it.

Data Quality & Migration gets data into usable shape - audits for completeness and accuracy, deduplicates records, normalises formats, migrates from legacy systems, and validates the result. This is prerequisite work before AI, decision intelligence, or integration projects can succeed.

For organisations where data quality is known to be a problem and building on top of it would be risky.

What you get

Clean, normalised data ready for decision support tools, AI applications, or system integration. Our customers use this to derisk subsequent projects, eliminate manual cleanup cycles, and establish data foundations that won't require constant rework.

Timeline:: 4-8 weeks depending on data volume and complexity
Deliverable:: Cleaned and migrated data, quality reports, validation documentation, schema definitions, and data governance recommendations

How it works

Data Audit

Assess completeness, accuracy, duplication, format inconsistencies, and quality issues across systems and records.

Cleanup & Deduplication

Remove duplicates, fix format inconsistencies, fill critical gaps, standardise naming and structures, flag unfixable issues.

Schema Design

Define target data structures, map legacy fields to new schema, establish naming conventions, document relationships and constraints.

Migration Execution

Move data from legacy systems to new structures, transform formats, validate integrity, handle exceptions, maintain audit trails.

Validation & Testing

Verify completeness, test data quality against requirements, sample-check accuracy, document migration results, hand over to next phase.

What's required

Access to source systems and data. Stakeholder clarity on what "clean" means for your use case. Time for validation and correction cycles. Realistic expectations - some data may be unrecoverable or require business decisions to resolve.

This work is iterative. Initial cleanup reveals deeper issues that require additional cycles.

"We thought our product data was ready. The quality audit found 40% of records had missing attributes. Better to know that before feeding it to AI."

Head of E-commerce, Consumer Electronics (UK)

Talk about data readiness.

Let's discuss whether data quality is blocking your decision support or AI initiatives.

Talk about data readiness