Better analysis begins with better data.
Practical explanations of messy-data problems, repetitive analyst work, data quality and reproducible EazyDataFix workflows—written for people who work with real datasets.
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How to Preserve Row Identity Through Data Cleaning
A practical method for tracking source rows, explaining exclusions and verifying that cleaning has not silently changed dataset coverage.
More practical guides
How to Diagnose Missing Values Created by a Data Join
A practical method for distinguishing genuine nulls from failed key matches, inconsistent identifiers and unintended row loss after a data join.
How to Build a Data Quality Issue Register That Leads to Action
Turn scattered data problems into a reviewable issue register with clear evidence, severity, ownership, decisions and verification steps.
Create a Reviewable Notebook Checkpoint with EazyDataFix
Use an EazyDataFix RunResult to turn a one-off notebook cell into a reviewable checkpoint with a quality score and structured cleaning change log.
How to Set Plausible Data Ranges Without Hiding Errors
Learn how to define, test and document plausible numeric ranges while keeping unusual but valid records visible for investigation.
How to Clean Placeholder Missing Values Safely
Placeholder values such as “N/A,” zero, dashes and sentinel dates can distort analysis. Learn how to identify and clean them without erasing legitimate data.
How to Investigate Duplicate IDs Without Losing Valid Data
Duplicate IDs do not always represent duplicate records. Learn how to classify repeated identifiers, resolve conflicts safely and verify the result.
Preview Data Cleaning Changes with EazyDataFix
Use EazyDataFix dry-run mode to inspect proposed cleaning results and a structured change log before accepting changes to a dataset.