Understand sources, fields, formats, completeness and recurring quality issues
Data audit and standardization
Give fragmented data a shared foundation.
We profile sources, clarify definitions, normalize structures and clean identifiers so teams can work from data that is understandable, comparable and ready for the next step
Agree canonical meanings, taxonomies, identifiers and ownership
Leave practical quality rules, documentation and exception paths
The problem
Before data can be connected, people need to agree what it means.
Source files often look similar while carrying different definitions, units, naming conventions, date logic or levels of detail. Cleaning the visible errors without resolving those differences only moves the problem downstream.
We create a usable view of the current data landscape and turn implicit assumptions into rules the business can explain, check and maintain
What sources and fields exist?
Map systems, files, owners, refresh cycles, formats and dependencies
What should each value mean?
Align definitions, taxonomies, units, identifiers and reference data
Who maintains the quality?
Set rules, responsibilities, exception handling and documentation
Typical inputs and outputs
A data foundation the next workflow can use.
The exact format follows the sources, operating model and decision the data needs to support
Files, systems, fields, owners, refresh frequency, formats and known dependencies
Completeness, duplicates, validity, consistency, outliers and missing identifiers
Canonical terms, allowed values, units, hierarchies and reference-data rules
Checks, ownership, exception paths, documentation and a prioritized improvement backlog
Tools and system boundaries
Work with the tools and constraints already in place.
Inputs may include spreadsheets, exports, databases, APIs or partner templates. We can design the rules and working model around the available environment without implying that every engagement requires a new platform or enterprise-scale engineering program.
Related thinking
Product carbon footprint is also a product-data problem.
A related article on definitions, source information and interoperability when product data must travel across organizations