Identity problems
Names, identifiers, products, organizations, and locations can appear in several forms. Merging too early can join different entities; avoiding every merge leaves duplicates that distort results.
Resource guide / Data preparation
Prepare messy information without erasing its history
A practical guide to profiling, mapping, standardizing, and deduplicating data before it is analyzed or verified.
Working principle: Structure comes first. Every cleanup decision should remain reviewable, reversible, and connected to the original value.
Why preparation matters
Clean-looking data can still carry hidden errors
Preparation is not cosmetic formatting. It establishes whether records mean the same thing and whether comparisons are valid.
Definition problems
Columns with similar labels may use different units, time windows, populations, or business rules. A shared label does not guarantee a shared definition.
Preparation workflow
A traceable path from source files to analysis-ready data
Inventory the sources
Record file, system, owner, date range, access limits, and intended use before transforming a value.
Profile structure and quality
Identify types, missingness, uniqueness, distributions, formatting patterns, and obvious anomalies.
Define a target schema
Document field meaning, accepted values, units, identifiers, and how unknown or conflicting values will be represented.
Normalize with lineage
Standardize dates, units, names, and categories while preserving original values and transformation notes.
Resolve duplicates carefully
Use explicit match rules, confidence thresholds, and a review queue for uncertain entity links.
Publish an exception report
Deliver the prepared dataset with unresolved issues, assumptions, exclusions, and quality checks.
Review checklist
Questions to answer before analysis begins
Are meanings explicit?
Every material field should have a definition, unit, time basis, and accountable owner.
- Separate unknown from zero or not applicable
- Preserve original values
- Record derived-field formulas
Are matches defensible?
Entity resolution needs observable evidence and a path for humans to reject uncertain merges.
- Use stable identifiers when available
- Show why records matched
- Retain rejected alternatives
Can another analyst repeat it?
The input version, rules, transformations, and exceptions should be sufficient to reproduce the output.
- Version source files and rules
- Record row-level exceptions
- Define refresh ownership
A practical next step
Bring a real question. Leave with a clearer evidence path.
Show us the dataset, claim, report, or market question your team needs to trust. We will map the relevant workflow and the review points it requires.