Normalize
Field standardization
Apply agreed formats to names, dates, categories, identifiers, addresses, URLs, and other structured fields.
Data cleaning and verification
Make your dataset consistent, testable, and ready to use.
Morrow Creek cleans and verifies CSV, Excel, and JSON data against agreed rules—normalizing fields, detecting duplicates, identifying missing values, validating formats, and preparing a controlled customer release.
Cleaning is not guessing.
Every dataset has its own definition of correct. Before production, the job establishes the schema, allowed values, duplicate logic, treatment of blanks, normalization rules, verification sources, and required delivery formats.
That written contract separates safe mechanical corrections from changes that require business judgment. Ambiguous values can be flagged rather than silently rewritten when the scope does not provide a reliable resolution rule.
Common quality operations.
Deduplicate
Duplicate detection
Identify or consolidate repeated records using exact keys or approved matching rules, with uncertain cases handled explicitly.
Validate
Completeness and verification
Check required values, types, ranges, and source-backed fields against the acceptance criteria defined for the job.
What you can provide.
- The source CSV, Excel, or JSON files
- A description of what each row represents
- Required columns and expected data types
- Allowed values and formatting conventions
- Your rule for identifying duplicates
- Fields that require external verification
- The format your downstream system needs
Customer files are uploaded inside the private job workspace when required. Sensitive or regulated data may need additional review and may be excluded if the project cannot be handled within the service's approved controls.
Acceptance before release.
Completed work is tested against the confirmed requirements. Checks can cover exact column names, file types, row expectations, uniqueness, missing values, allowed formats, provenance, and the absence of unfinished or internal-only artifacts from the customer package.
No cleaning process can establish facts that the input and approved sources do not support. Where a value cannot be verified under the agreed rules, the deliverable can preserve, flag, or leave it blank according to the contract rather than inventing certainty.
Commission a dataset your team can evaluate.
Describe the source files, known quality problems, required output, and acceptance rules. We will define the paid scope and exact price before you commit.
Start a paid job