What is data journalism?
Journalism in which the analysis of data is the reporting method — finding stories in datasets, verifying claims against them, and presenting findings in ways readers can interrogate.
What distinguishes it from reporting that mentions numbers. In conventional reporting, data supports a story found by other means. In data journalism, the dataset is the source — the story emerges from obtaining, cleaning and analysing it, and would not exist otherwise.
The typical workflow:
Obtaining data, frequently through freedom of information requests, scraping published sources, leaks, or purchasing.
Cleaning, which is genuinely the bulk of the work — inconsistent formats, duplicate records, missing values, and categories that changed definition partway through a series.
Analysis, looking for patterns, outliers and comparisons.
Verification, including seeking explanations from the data's owners before publication. This is the step that separates it from dashboard-building.
Presentation, through charts, maps and sometimes interactive tools letting readers find their own local figure.
What it is good at:
Finding stories that are invisible individually. A pattern across thousands of records is not visible to anyone experiencing one record.
Establishing scale, turning anecdote into evidence.
Holding institutions to account using their own published figures.
Personalisation — a national finding rendered as "in your area", which is among the most effective forms of public-interest journalism because it connects a statistic to the reader's own circumstances.
The characteristic pitfalls:
Correlation presented as causation, which is the most common failure.
Data quality assumed rather than checked. Official datasets contain errors, definitional changes and gaps, and a finding may be an artefact of collection rather than reality.
Denominator errors — raw counts where rates are needed, so the largest area always looks worst.
Cherry-picked timeframes.
Over-interpreting small numbers at local level.
Why it matters now. Institutions publish enormous quantities of data, and most of it is never examined. Scrutiny capacity, not data availability, is the constraint — which is why data journalism teams, and collaborations that share analysis across outlets, have become more significant.