Data cleaning & validation

MakeBeacon cleans and validates business data so teams can use it with greater confidence. Work can include finding duplicates, standardizing formats, checking required fields, and producing a clear report of records that need review.

Data assembled from spreadsheets, exports, forms, or different business systems often contains inconsistent formats, missing values, duplicates, and invalid entries. These issues can make reporting unreliable and cause problems when data is imported into another system. MakeBeacon reviews the source data and agrees the cleaning rules with your team before applying changes. Depending on the project, this can include standardizing dates and text, checking required fields, identifying duplicates, validating values against agreed rules, and separating records that need a person to review. The handover can include the cleaned dataset, the validation rules used, and a summary of changed, rejected, or flagged records. The original data and agreed handling requirements guide how the work is carried out.

Problems this solves

• Spreadsheet columns contain mixed date, number, or text formats.
• The same customer, product, or transaction appears more than once.
• Required fields are blank or contain values in an unexpected format.
• Imports fail because records do not meet a system’s requirements.
• Data from separate sources uses inconsistent names, categories, or units.
• Reports contain values that are out of range or do not match agreed rules.
• Teams cannot tell which records were changed or need manual review.

What is included

• Review of data files, structure, and intended use.
• Agreed rules for formatting, validation, and duplicate handling.
• Standardization of dates, text, categories, and numeric values.
• Checks for missing, malformed, duplicate, or out-of-range records.
• Flagging or separation of records that need human review.
• Summary of validation results and data issues found.
• Cleaned output prepared in an agreed file format.

Typical deliverables

• Cleaned dataset in the agreed format.
• Validation and cleaning rules used for the work.
• Summary of records changed, removed, or flagged.
• Issue list for records requiring review.
• Repeatable cleaning script, when included in the agreed scope.
• Handover notes describing assumptions and limitations.

Technologies & tools

• Python
• Pandas
• NumPy
• openpyxl
• Python csv module
• Regular expressions (re)
• Excel and CSV formats
• SQLite, where required for dataset handling