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Updating the Inverted Pyramid of Data Journalism

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This post was originally published by Paul Bradshaw in the Online Journalism Blog and is reprinted here with permission.

Image: Paul Bradshaw/Online Journalism Blog

It’s over a decade since I published the Inverted Pyramid of Data Journalism. The model has been translated into multiple languagestaught all over the world, and included in a number of books and research papers. But in that time the model has also developed and changed through discussion and teaching, so here’s a round-up of everything I’ve written or recommended on the different stages — along with a revised model in English (shown above; versions have been published before in GermanRussian and Ukrainian!).

The most basic change to the Inverted Pyramid of Data Journalism is the recognition of a stage that precedes all others — idea generation — labeled ‘Conceive’ in the diagram above.

This is often a major stumbling block to people starting out with data journalism, and I’ve written a lot about it in recent years (see below for a full list).

The second major change is to make questioning more explicit as a process that (should) take place through all stages — not just in data analysis but in the way we question our sources, our ideas, and the reliability of the data itself.

Alongside the updated pyramid I’ve been using for the past few years I also wanted to round up links to a number of resources that relate to each stage. Here they are…

Stage 1: Conceive

Data journalism ideas can range from the simplest angles for turning around stories quickly from new datasets to in-depth investigations. The following links cover both situations, and map out the different pathways that journalists follow to get there.

Stage 2: Compile

Data for a story can come from a variety of sources. The links below cover a range of scenarios, from identifying regular sources of data and APIs, to compiling data yourself through data entry or scraping, to using FOI or company accounts, and treating text as data.

Stage 3: Clean

Data cleaning can take up a disproportionate amount of time in a data project (although not the widely reported 80% factoid) — and yet it’s the area that’s perhaps least written about. Hadley Wickham’Tidy Data is the exception to the rule here, while below I’ve listed some posts and a video which cover this stage.

Stage 4: Context

I should probably write more about putting data into context, but the two main places where I have are:


Stage 5: Combine

Providing context often means combining datasets. And the most common way of doing so is a spreadsheet function called VLOOKUP (or, increasingly, XLOOKUP). Last month I published this extract from my book “Finding Stories In Spreadsheets” on this process which walks through combining two datasets, and includes an embedded video walkthrough.

Questioning (at every stage)

Throughout all those stages — and the ‘communicate’ stage below — there is, as I said, questioning. Here are some posts that particularly relate to that:

Stage 6: Communicate

The ‘communicate’ stage of journalism can go in a number of directions, from data visualization to TV, and from short news updates to longform narrative journalism. Here are posts where I’ve explored a particular dimension of the storytelling stage.

Paul Bradshaw leads both the MA in Data Journalism and the MA in Multiplatform and Mobile Journalism at Birmingham City University, and works as a consultant data journalist in the BBC England data unit.

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