Data Cleaning

Cleaning an unstructured dataset involves transforming raw, messy data (such as user data , spreadsheets, scanned invoices, or system logs) into a structured, usable format.

Netzone International understands the data first and finds what type of unstructured data the client is dealing with, and to find whether the client has master data system.

The cleaning process will involve Text extraction (OCR, PDF parsing), Regular expressions for entity extraction, Fuzzy matching to resolve name inconsistencies, NLP to tag delivery instructions or complaints, etc.,

The summary of the types of unstructured data cleaned , key challenges identified (e.g., inconsistent date formats, missing units, abbreviations) , Cleaning methods used (e.g., NLP for text parsing, regex, OCR) will be defined.

Finally, data quality will be provided, % of missing values handled, Duplicates removed, Standardized units (e.g., kg vs lbs), Entity disambiguation (e.g., resolving "Netzone Ltd" vs "Netzone Inc.") . Then entity mapping and master data linking will be done.

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