Data workflow

Operational data, ready for review

A private data-normalization utility that turns inconsistent operational records into a structured workbook without concealing ambiguity.

Relationship
Client engagement
Stage
Implemented
Capabilities
  • Product strategy and management
  • Software and data engineering

Availability

Implemented for private client use. The client identity, source data, usage details, and outcomes are not public.

The problem

A working spreadsheet mixed inconsistent naming and role conventions with records that still required human judgment.

The contribution

JodyInk translated the working rules into a focused Python utility: routine transformations happen consistently, and uncertain values remain visible for review.

Decisions that shaped the work

  1. Preserve the original role beside any standardized value instead of overwriting the record.

  2. Flag complex names and unmapped roles rather than forcing a plausible-looking result.

What was implemented

  1. Active-record filtering, name normalization, explicit role mapping, and exception logging.

  2. A protected review sheet with controlled editable fields, data validation, and visible review cues.