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.
Featured article
How to Distinguish Structural Missingness from Data Quality Gaps
Learn how to separate expected, structurally absent values from genuine data quality gaps before cleaning, reporting or escalating an issue.
More practical guides
How to Audit Rounding and Precision Loss in Numeric Data
A practical workflow for detecting premature rounding, inconsistent decimal precision and numeric representation problems before they distort analysis.
How to Audit Time Zone Assumptions Before Daily Aggregation
A practical method for identifying ambiguous timestamp semantics, testing reporting boundaries and preventing misleading daily aggregates.
How to Prevent Many-to-Many Join Explosions
A join can complete without errors and still multiply rows, inflate totals and distort downstream analysis. Learn how to test key cardinality and review the result before trusting it.
Review Headcount Cleaning Proposals Before Reconciliation
Use an EazyDataFix dry run to inspect proposed employee-data changes before reconciling headcount or altering a source export.
How to Audit Spreadsheet Auto-Conversion Before Analysis
Learn how to detect dates, identifiers and numeric values that spreadsheet software may have silently reformatted before the data reaches your analysis.
Assess a Web Events CSV Before Funnel Analysis
Use an EazyDataFix run to assess a web events CSV, inspect proposed cleaning changes and identify questions that must be resolved before funnel analysis.
How to Validate Cross-Field Rules Before Analysis
Learn how to find contradictions between related columns, define testable business rules and review exceptions without silently discarding valid records.
How to Audit Reference-Code Coverage Before Reporting
Learn how to find unmapped, blank, duplicated and outdated reference codes before joins turn them into missing labels or misleading report categories.
Turn a Dry Run into Data Cleaning Acceptance Criteria
Use an EazyDataFix dry run to define clear acceptance criteria for proposed cleaning changes before anyone approves a modified dataset.
Validate a Replacement CSV Against a Baseline Contract
Use an inferred baseline contract and explicit quality rules to review a replacement CSV before it enters an established reporting workflow.
How to Trace Unexpected Row Count Changes in a Data Pipeline
A practical method for locating unexplained row gains or losses, identifying the responsible operation and deciding whether the change is valid.
How to Validate Data Grain Before Aggregation
Learn how to identify a dataset’s true grain, detect duplicate business keys and prevent inflated totals before calculating reports or KPIs.
Interpret an EazyDataFix Quality Score in Context
Use an EazyDataFix run to review a quality score alongside its structured change log instead of treating one number as an automatic approval.
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.
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.