What you receive
- A cleaned dataset in an agreed structure
- A change log covering deduplication, field changes, and other rules
- A field dictionary with definitions and standardized values
- Records that still need a business or policy decision
Services
Data cleaning
For incomplete, duplicated, inconsistent or hard-to-match data, we first identify the issues, then apply agreed cleaning rules and document the work so results can be compared with the source files. Records needing a business decision are listed separately. Document text extraction, table extraction and de-identification can be included in the project scope.
Discuss your data needWhat we need to begin
- Source files or a representative evaluation sample
- The intended analysis, report, product feature, or workflow
- Known business rules, identifiers, and definitions your team already uses
How we work
- 01
Review the files and identify issues that affect the intended use
- 02
Confirm rules for duplicates, missing values, formats, and exceptions
- 03
Deliver the cleaned files, change notes, and open items for review
Assess the fit for your project
Data cleaning organizes inconsistent, duplicate, incomplete, or hard-to-match files under agreed rules and retains change notes and records needing business decisions. Its goal is fitness for an agreed analysis or workflow, not rewriting business facts on its own.
What inputs are needed before cleaning?
Provide source files or representative samples, intended use, existing field definitions, identifiers, and business rules. This separates format issues that can be handled from exceptions requiring business decisions.
How is traceability preserved after cleaning?
Agree deduplication, missing-value, format, and merge rules in scope, then deliver a field dictionary and change notes. Records that cannot be reliably resolved should be listed separately, not silently deleted or arbitrarily filled.
What affects cleaning-project pricing?
It mainly depends on file count and structural differences, record volume, fields to standardize, duplicate or matching difficulty, share needing human review, output format, and exception handling. A representative sample helps assess that work.
FAQ
Frequently asked questions
Will the raw data be changed?
The original files remain identifiable. We deliver the cleaned results as a separate version and document the applied rules so your team can compare them with the source.
Can you clean files from several sources?
Yes. We can compare available fields and identifiers, standardize agreed values, and flag records that do not match.
Deliverables are assessed against agreed specifications, sampling and acceptance criteria. New fields, rule changes or expanded scope require separate agreement. Domain annotation is not medical, legal or investment advice, or regulatory certification.Sources and usage ↗
Begin with a clear question.
Discuss your data need