Data Journalism Heist

Good data journalism often starts with a modest question and a useful spreadsheet. In Data Journalism Heist, Paul Bradshaw lays out a brisk, approachable way to find data, spot a story within it, and report the result carefully. The emphasis is on everyday reporting—not only the headline-making investigation.

Bradshaw frames the process as a heist with an important rule: nobody gets hurt. That gives this short guide its pace and its purpose, keeping the focus on practical techniques and responsible storytelling.

Start by finding the data

The opening section is a scouting mission through potential sources: open-data portals, statistical agencies, Freedom of Information resources, and local-government records. A spending dataset provides a concrete starting point, while the wider approach can be applied to other recurring public data.

Find the story without getting lost in the rows

Once a dataset is in hand, the book turns to questions journalists can ask of it: what stands out, what has changed, whose experience is reflected in the figures, and whether a claim is supported. Bradshaw introduces spreadsheet methods—including pivot tables and filters—to help readers examine information and narrow in on promising leads.

From data to responsible reporting

The aim is not analysis for its own sake. The book connects finding and examining data with getting the story out, while keeping accuracy and potential impact in view. Its compact, introductory treatment makes the subject feel like a set of learnable reporting moves rather than an intimidating technical specialty.

A useful first step for data-curious journalists

Readers looking for an accessible entry into data-led reporting will find a focused guide to locating public information, asking sharper questions of a spreadsheet, and turning a promising pattern into a story worth pursuing. It’s a quick read with a clear invitation: start small, work carefully, and see what the data can reveal.