# Quartz/bad-data-guide

An exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.

Repository: https://github.com/Quartz/bad-data-guide
Canonical: https://ross.abutalabs.com/products/bad-data-guide
License Family: other
Topics: qz-things, data, guide, documentation
Last push: 2021-09-20T21:15:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3946, "days_push": 1808, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4127, forks 397 (observed 2026-08-28T04:08:36.044699+00:00)

## What it is
A reference guide from Quartz cataloguing common problems found in real-world datasets, with suggested resolutions organized by who is best positioned to fix each issue. It is aimed primarily at journalists and data analysts working with messy data.

## Use cases
- identify why a dataset has missing values encoded as zeros
- learn how to handle inconsistent date formats in a spreadsheet
- decide whether a dataset is trustworthy enough to use in a report
- check for duplicate rows and inconsistent spelling before analysis
- document data provenance for a data-driven story
- train a newsroom team on data quality pitfalls

## When to choose
- you work with messy real-world data and need a checklist of quality problems
- you are a journalist or analyst evaluating a new dataset
- you want teaching material about data cleaning and quality

## When to avoid
- you need software or tooling to clean data automatically
- you want a technical library with code for data validation
- you need an actively updated resource - the guide has not seen recent releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, data-science
- domain: data-science, tutorials, education
- platform: cross-platform
- tags: data-quality, data-cleaning, journalism, guide, reference

## Member repositories
- Quartz/bad-data-guide (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:36.044699+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:04.078341+00:00, confidence not recorded.
  - readme: https://github.com/Quartz/bad-data-guide (fetched 2026-08-28T04:08:36.044699+00:00, sha 26b8b9f863cc)
- Data as of 2026-08-30T08:39:29.467469+00:00.