US, Europe, same story

My hypothesis was that American newsrooms were more organized in the way they dealt with data journalism. To test this hypothesis, I asked several data journalists if their company maintained a distinct accounting for data-driven projects. After all, if you want to know how profitable your data-driven operation is, you have to know how much you spend on it in the first place. I sent out a short questionnaire to 22 fellow data journalists among my Twitter followers. 18 answered, from as many newsrooms in 12 countries ^[1].

Even though this falls short of statistical significance, the Anglo-Saxon divide I was hoping for failed to materialize. It turns out that most newsrooms do track their data journalism expenditures, in the US as in the rest of the world.

CAR culture

Another hypothesis I formulated was that US newsrooms had a history of computer-assisted reporting (CAR) and that decision-makers had some experience in setting up a data-driven reporting operation (or at least knew where to look for). I did not run another survey to test this but I emailed Philip Meyer, author of Precision Journalism and CAR superstar. CAR did play a role to explain the current situation, he wrote, and added that American journalists benefit from strong countrywide organizations like IRE and NICAR, which, together with the experience-sharing programs of large news companies such as Knight and McClatchy, helped spread data-driven journalism. So far, Europe has no EU-wide organization that could stand the comparison.

Meyer added that journalism schools had close links to the news industry, which pushed them to teach skills in demand on the job market. Although it would take lots of data to prove that European journalism schools are lagging behind the needs of the market, it is true that CAR classes are a novelty here.

However, the lack of skills among European management and journalists is only part of the story.

It's the venture capital

When a US news nerd goes entrepreneurial, chances are she will focus on building a prototype, raise funds and deliver a product. Publish2, a publishing platform, Spot.us, a now-defunct crowdfunding solution or Backfence, a failed hyperlocal news portal, were all started by journalists.

In Europe you build a prototype, find no funds to raise and go into services to keep developing your product. Vizzuality in Madrid operates CartoDB on this model, as does OpenDataCity in Berlin with Lokaler (both are mapping solutions). Scoopinion, a news start-up financed by a Finnish grant, is a notable exception. Datawrapper, a visualization service for journalists, was started thanks to the help of ABZV, a German foundation. It is now maintained in part by Journalism++, a network of data journalism agencies.

The lack of venture capital, together with the lack of in-house skills, explains why Europe has much more data journalism agencies than the US. These agencies, in turn, help create the demand for their products.

No golden eggs

I then asked my fellow data journalists if they thought they were profitable ^[2]. Most agree that their apps will not dramatically impact the P&L [profit and loss statement] of their companies. So much for the idea that data was going to save the newspapers industry.

Respondents thought that the best possible sources of revenues were sponsoring and paid-for applications, before advertising. This is surprising given that 14 out of the 18 newsrooms surveyed still rely on advertising as a major source of income. The discrepancy shows that journalists are well aware of the need for new business models. Several respondents insisted on the fact that data journalism should be seen alongside investigative reporting as an image-enhancing endeavor rather than a cash cow.

Not everyone can invest in 'image'

Indeed, advertising is a poor way to monetize labor-intensive content. BuzzFeed, which is rumored to be profitable, will probably never put a team of several reporters to work 4 months on a data-driven story. Unless it is committed to quality journalism like ProPublica or ICIJ (of Offshore Leaks fame), few newsrooms have the willingness or resources to invest significantly in data-driven investigations. News companies evolving in competitive markets like the Guardian and the New-York Times or public broadcasters with deep pockets like the BBC or France Télévisions also set up teams of developers to work alongside journalists.

Non-profit outlets rely on public service information to justify their subsidy or grants. Companies that are competing for readers and advertisers have an incentive to nurture respectable brands by investing in prestige projects: they command a premium on advertising. The situation is quite different in most other newsrooms that still think of themselves as monopoly operations, for which image is much less of a driver of advertising prices.

It would be foolish to assume that there is a strong demand from newsrooms. Data journalists do not come in an plush and affluent environment where they can easily sell their creations.

Developers with journalism in mind

Lorenz Matzat of OpenDataCity, for instance, explains that they are diversifying away from service for newspaper companies. "We are trying to be a publisher on our own, organizing our own ads or sponsors for a data journalism product", he said in an email interview. Journalism++ follows the same path. The Stockholm chapter of the brand works predominantly with Swedish and Finnish media outlets, but the Paris chapter moved away from working with media outlets and diversified its sources of revenues (non-TV media companies account for less than 7% of its income).

Journalism is a mindset rather than a clearly defined job. Developers of all stripes actually go into journalism once in a while. The best example of this trend is Pitch Interactive's drone story. The data visualization studio does client work for media companies and others. But in this case, they simply wanted to shock and alert people on the problem. They wanted to tell their own story.

Everyone is a media. This is also true for data journalism: Every team of nerds can become a data journalism outlet.

Corporate data

After image project and self-financed stories, the third and most promising source of funding for data journalism lies in corporate data. Companies have a lot of exclusive data to mine. From this trove, they could tell a number of stories. OK Cupid, a dating website, started to analyze its own database to explain patterns in US sexual behavior. The results were published on a blog, OK Trends. It did not reveal data from the marketing or finance department that could have jeopardized the firm, nor did it use private data from clients, as Bloomberg allegedly did. The stories were so good and so widely read that at some point they were responsible for over half of the leads on the site. A typical lead in dating costs between 2 and 5 dollars. Do the math and you will see that data journalism was immensely profitable for the dating website.

Other examples abound. The Facebook Data Science team, for instance, regularly publishes articles that give insights on current developments, like same-sex marriage or sports. Twitter hired DataDrivenJournalism.net's board member and former Guardian data journalist-in-chief Simon Rogers as Data Editor.

It is just a matter of time before gas utility companies mine smart-meters data and tell us about who's most sensitive to cold. Or before transportation companies tell us which destinations are becoming more hip based on customer data.

[1]: from the newsrooms of The Center for Investigative Reporting, La Nacion (Argentina), Der Standard, The Age, Zeit Online, Helsingin Sanomat, Le Monde, AskMedia, Wired Italy, Nu.nl, Financial Times, BBC World Service, Media Wales, Bloomberg, ProPublica, The Washington Post, L'Avenir and NZZ.

[2]: 2 of the 3 non-profits in the sample answered this question as well, probably keeping in mind that 'profit' in their case translated into 'impact'.

But that’s not enough. People leave their first data training session excited -  vowing to delve into the data as soon as they can and as often as possible.

Then work gets in the way. Many, probably the majority never get around to it. They forget what they learned, and exploring data gets put back in the “too difficult” or “one day, when I have time” trays.

At the same time, editors, who’ve had little opportunity to see data journalism for themselves, are almost certainly going to associate the word “data” with graphs and statistics. I know from my own experience that editors will tend to steer their online journalists to my courses, neglecting their beat reporters. Or they need help interpreting statistics.

Carts and horses are being put in the wrong order here. “Computer-aided-reporting” doesn’t turn someone into a reporter, any more than handing someone a pen makes them a reporter. The age-old qualities of curiosity, persistence and the ability to tell a story have not gone away just because datasets became available!

Datasets have often been compared to knowledgeable interviewees – ask the right questions, and you will get good answers. Data journalism is not something special  - it’s just what Philip Meyer called "precision journalism".  Great, well investigated stories start with hunches or tips. But now data is a witness. As Aron Pilhofer of the New York Times told the School of Data journalism in Perugia, you just need to look at a data-based story which won a Pulitzer Prize this year.

The Sun-Sentinel reporters used data to prove a case – that off-duty police officers were driving at great speeds on Florida’s toll roads, and getting away with it. They used data showing the times certain cars passed toll booths to calculate and prove what speeds the policemen were driving at (90-130mph if you don’t have time to read the article).

Without data – no story. Without access to data – no story. But this is not a piece about statistics. It needs no graphics, just words. The difference is that just a few years ago the reporters would not have been able to do this story.  The traces we all leave in public places (and the openness of the USA’s FOIA) made this story possible. 

There’s a clear message  – training in computer-assisted reporting, and data alone cannot find or write a story for you. Whether you’re a newbie or a senior editor you need to decide what the story could be –then ask not what you can do with data, ask what data can do for you!  When there’s a story to be told, the technology will help you work out what it is. Not the other way around.

“Since the rise of ubiquitous sensing and computing, we are spewing out vast amounts of data about almost every aspect of our world. [...] But non-profits and social organisations [often lack] the technical know-how or resources to work with data scientists. We figured we could combine our skills with their expertise to tackle truly meaningful problems in the hopes of using data to drive a smarter world”, says Porway.

Data Scientists with a Social Mission

The data scientist launched DataKind in 2011, posting on his blog an invitation calling for others in his field to join the initiative, dubbed at the time Data Without Borders. Even though a relatively short time has passed since then, DataKind has already managed to garner great interest, both from social organisations and data scientists, as well as from third parties such as the United Nations, the World Bank, and the American Administration. 

Working almost exclusively with volunteers from the data science community, DataKind offers two types of collaborative frames: DataDives and DataCorps. The first are modeled according to the hackathon format - weekend events in which DataKind’s volunteer teams work with social organisations, elaborating punctual solutions and ideas to punctual problems and needs. The DataCorps revolve around bigger, long-term projects, bringing together larger teams of data scientists, developers, and designers, who work closely with one organisation. Finally, DataKind also provides data literacy consulting services, through workshops and trainings.

NYC Gov DataDive. Image by DataKind

The lack of financial compensation does not seem to hinder DataKind's capacity to enlist data scientists willing to join the cause, as Porway proudly points out: “Up until now, 1.500 data scientists have signed up to volunteer. In practice, we've held six events that have had about 50 to 100 data scientists volunteering at each”. 

Porway assigns DataKind’s recruiting success to the organisation’s strong network in the scientific community, as well as to positive reviews in media publications such as The Economist, National Geographic, and Forbes. But he also believes a major driving force behind DataKind’s success rests in the scientists own ambition to get involved in social action.

Meeting All Needs

The organisations that approach DataKind may differ greatly from one another, but their needs are often similar. They seek assistance in creating data visualisation, building predictive analytics systems, and planning data collection strategies. The data NGO seldom refuses appeals from organisations. As long as a social group has good data, a pertinent question, and good support on behalf of both the management and the practitioners in the organisation to engage on the data science path, DataKind is keen to collaborate. If one of these elements is missing from the pitch, DataKind encourages the group to review it and guides the process, until the group can assemble all of the needed elements.

Porway is convinced the power of data can also be profitable for journalists and media organisations. “Almost every professional field is currently going through a ‘data moment’, similar to the ‘computer moment’ that washed over every discipline in the '90s. Journalism is no exception. Data can be used to generate leads, tell stories, or be used as a source of evidence, greater than any other source journalists ever had access to before. I think that we can help combine data science and journalism through healthy collaborations between the two groups, as well as through stronger data journalism education programs in high schools and universities”, he notes.

At this moment, DataKind is envisioning spreading its network throughout the world. The first international chapter was established last month in the UK. Porway describes his team’s future ambitions in terms that sound almost missionary: “We are going to be rolling out more DataCorps projects, more DataDives. We intend to build chapters that will empower our international community, so there's really no limit to the ways in which we'll be growing as an organisation”. To achieve this, the data NGO also intends to continually re-evaluate the needs and capacities of the data and social sector communities, in order to readjust its strategies and the tools it offers. “We may not know what we'll look like five years down the road, but we'll still be completely committed to helping bring data skills to the social sector in any way we can”, Porway concludes.

A total of 15,000 Euros (around $19,000) will be awarded to eight winning projects in June.

This year's edition received over 300 applications from all over the world. The shortlisted candidates represent 19 different countries, including Argentina, Austria, Brazil, Chile, France, Hong Kong, Hungary, Kenya, Romania, UK, United States, and Venezuela. Applicants ranged from major media groups, regional newspapers, and research groups. The finalists were chosen by an international pre-jury.

Below is the list of nominees for each category of the competition.

Data Journalism Website or Section

• La Nacion, Data Section in lanacion.com, Argentina
• The Global Mail, Powerhouse, Australia
• O Eco / International Center For Journalists, Gustavo Faleiros, Brasil
• Poderopedia.org, Poderopedia: Who is who in business and politics in Chile, Chile
• Thomson Reuters, Connected China, Hong Kong
• The Asahi Shimbun, Damage Estimation for the Nankai Trough Earthquake, Japan
• Internews Network, Data Dredger, Kenya
• The Guardian, Guardian Data, UK
• The Guardian, London 2012 Olympics data, UK
• Media Wales, WalesOnline Datastore, United Kingdom
• The Texas Tribune, TribData, United States
• ProPublica, ProPublica Nerd Blog, United States
• Knight-Mozilla OpenNews, Source: Journalism code and the people who make it, USA
• KCETLink, Ballot Brief: Election 2012, USA

Data Storytelling in Big Media

• Editora Abril, Network Of Scandals, Brasil
• The Globe and Mail, Young adults really do have it tougher, Canada
• Berliner Morgenpost, Flugrouten-Radar / Flight Paths Tracker Berlin/Brandenburg, Germany
• Süddeutsche.de/ Südddeutsche Zeitung, DataGraph / Europa-Atlas, Germany
• Neue Zürcher Zeitung, «No time for anger», Switzerland
• The Guardian, Special report: How the world runs on migrants' money (Text story + Interactive), UK
• Financial Times, How fast is the London fire brigade?, United Kingdom
• Guardian, US Gay rights state by state, United States
• The Chronicle of Higher Education, Women as Academic Authors, 1665-2010, United States
• ProPublica, Exit polls 2012: How the vote has shifted, United States
• Bloomberg, Visual Data Bloomberg Billionaires Index, USA

Data Storytelling in Small Media

• Freelance, Lower Saxony State Election Coverage, Deutschland
• Freelance, The Art market for Dummies, France
• Kiln, Better or Worse, UK
• BBC, Census shows rise in foreign-born, UK
• Financial Times, The global trade in resources, United Kingdom
• DC Action for Children, DC KIDS COUNT Data Took: E-Data Book, United States
• The Texas Tribune, Texas Tribune Lawmaker Explorer, United States
• ProPublica, Segregation Maps, United States

Data-Driven Applications 

• Fairfax Media, Mapping Australia's Census, Australia
• The Globe and Mail, The data behind R.A. Dickey: A pitch-by-pitch breakdown of his 2012 season, Canada
• WeDoData, Le Pariteur, France
• ZEIT ONLINE, Unemployment Rates, Germany
• Das Cloud, Macroscop, Romania
• De Volkskrant, Mapping the crisis: localizing the bankruptcy, unemployment and housing prices, The Netherlands
• BBC Visual, Journalism Great British class calculator, UK
• BBC World Service, Athletes Like You, United Kingdom
• The Texas Tribune Texas Tribune Schools Explorer United States
• ProPublica, Message Machine, United States
• The MIT Tech, Religion at MIT, United States
• Mother Jones, America Under the Gun: A Special Report on the Rise of Mass Shootings, United States
• The Seattle Times, The Budget Game, United States
• Associated Press, Associated Press Elections News Apps, USA
• Newsweek / The Daily Beast, This Is Your Rep On Guns, USA
• Diario El Nacional, Tweetometro Presidenciales 2012, Venezuela

Data-Driven Investigative Journalism in Small Media

• Global News, Hooked: Canada's pill problem, Canada
• Kreativ Magazin / Kreativ Online, The Media Industry under the Orban-regime, Hungary
• Investigative Reporting Project, Italy Gaza's gas: the EU's burned millions, Italy
• Wired Italy, #Checkyourhospital, Italy
• Stuff.co.nz, Fairfax Media New Zealand, School Report, New Zealand
• The Detail, How integrated are Northern Ireland's schools?, Northern Ireland
• Philippine Center for Investigative Journalism, The Wealth of the "Gods of Faura",  Philippines
• Media Wales, Children in Care, United Kingdom
• Department of Agricultural & Applied Economics, University of Wisconsin-Madison Who's Pulling the Strings of D.C. Puppet Corporations?, USA
• The New York, World Dividing Lines: Gerrymandering and New York Districts, USA
• New England Center for Investigative Reporting, Power Players: Massachusetts corporate titans bankroll key races as restraints vanish on political spending, USA

Data-Driven Investigative Journalism in Big Media

• La Nacion (Argentina), Argentina's Senate Expenses 2004-2013, Argentina
• Global News, The Gardiner - Trouble Overhead, Canada
• Il Fatto Quotidiano, Pro-life doctors and the state of maternity services in Italy, Italy
• Financial Times, How good are English schools?, UK
• The Guardian, The business of US food aid, UK
• BBC News, Visual Journalism team Sharp fall in young police officers, United Kingdom
• Los Angeles Times, Life on the line: 911 breakdowns at LAFD, United States
• La Nacion (Costa Rica), Patterns of exclusion from higher education in Costa Rica, Costa Rica
• Associated Press, Ryan asked for federal help as he championed cuts, United States
• Mother Jones, America Under the Gun: A Special Report on the Rise of Mass Shootings, United States
• Center for Public Integrity, Cracking the Codes, USA
• Center for Public Integrity, Skin & Bone: The Shadowy Trade in Human Body Parts, USA

The winners will be announced during the Global Editors Network News Summit in Paris, 19 – 21 June. More information can be found on the organiser's website. 

In this post we will be listing links shared during this training event. The list will be updated as the sessions progress. If you have links shared during the sessions that we missed, post them in the comments section and we will update the list.

Video recordings 

• Panel 1: The State of Data Journalism in 2013
• Panel 2: Data and Investigations: Collaborating Across Borders
• Panel 3: Data Journalism in Southern Europe
• Panel 4: Covering Emergencies in the Age of Big Data
• Workshop 1: Excel for Journalists, with Steve Doig
• Workshop 2: Social Network Analysis for Journalists Using the Twitter API, with Michael Bauer
• Workshop 3: Making Visualisations - A Survival Guide, with Gregor Aisch
• Workshop 4: Data Visualisation, Maps and Timelines on a Shoestring, with Gregor Aisch

Slides, tutorials, articles

• Tutorial and data files from Workshop 1: Excel for Journalists, with Steve Doig (the tutorial is also available in HTML here)
• Slides from Workshop 1: Excel for Journalists, with Steve Doig
• Tutorial from Workshop 2: Social Network Analysis for Journalists Using the Twitter API, with Michael Bauer
• Walkthrough, data and tools from Workshop 2: Social Network Analysis for Journalists Using the Twitter API, with Michael Bauer 
• Tips, links and reading recommendations from Workshop 3: Making Data Visualisations: A Survival Guide, with Gregor Aisch
• Slides from Workshop 3: Making Data Visualisations: A Survival Guide, with Gregor Aisch
• How to Create Maps with QGIS - a tutorial from Workshop 4: Data Visualisation, Maps and Timelines on a Shoestring, with Gregor Aisch
• How to Analyse Datasets with Tableau Public - a tutorial from Workshop 4: Data Visualisation, Maps and Timelines on a Shoestring, with Gregor Aisch
• How to Create Charts with Datawrapper - a tutorial from Workshop 4: Data Visualisation, Maps and Timelines on a Shoestring, with Gregor Aisch
• Data journalism in 2013: lots to be excited about - article about Panel 1: The State of Data Journalism in 2013 by Editors Weblog
• Article about Panel 2: Data and Investigations: Collaborating Across Borders by journalism.co.uk
• Article in Fast Company about Workshop 3: Making Data Visualisations: A Survival Guide
• Article about Workshop 1: Excel for Journalists by journalism.co.uk

Tools and other resources

• Source, an index of news developer source code, code walkthroughs and project breakdowns from journalist-coders
• School of Data - online tutorials for working with data
• The Data Journalism Handbook - reference book about how to use data to improve the news authored by 70 data journalism practitioners and advocated
• Open Refine (for data cleaning)
• Gephi (for graph visualisations)
• Hashtagify (visualisation of Twitter hashtags related to a particular #tag)
• Investigative Dashboard (methodologies, resources, and links for journalists to track money, shareholders, and company ownership across international borders)
• Tabula (open-source application that allows users to upload PDFs and extract the data in them in CSV format)
• Topsy (social media analysis tool mentioned in the panel on covering emergencies)
• DataSift (platform that allows users to filter insights from social media and news sources, mentioned in panel on covering emergencies)
• Storyful (service that mines social media to discover content relevant for news organisations and verifies it)
• GeoFeedia (tool that enables location-based search for social media content) 
• Spokeo (organises information about people from public sources and make it available for search)
• The Tor project (free software that helps defend against network surveillance and censorship)
• Follow the Money: A Digital Guide for Tracking Corruption (a handbook for investigative journalists that shows methods, tips and tricks for for tracking companies across borders)

Projects and organisations

• Organized Crime and Corruption Reporting Project (regional group of non-profit investigative centers and independent media stretching from Eastern Europe to Central Asia)
• Wobbing.eu (network of European journalists focused on Freedom of Information)
• Offshore Leaks (investigation into the people behind companies in tax havens worldwide)
• International Consortium of Investigative Journalists
• FarmSubsidy.org
• FishSubsidy.org
• ProPublica (US-based investigative journalism outlet)
• NPR Elections Big Board
• Investigative Reporters and Editors (US-based association of investigative journalists)
• Datajournalism.it (Italian website dedicated to data journalism)
• WNYC Data News Team

We posted photos from this event here. 

The Data Journalism Handbook is a free, open-source book that aims to help journalists use data to improve journalism. It provides inspiring examples from news organisations across the world and a collection of tips and techniques from leading journalists, professors, software developers, and data analysts for how to get started with data journalism.

Screenshot from the online Spanish edition of The Data Journalism Handbook

The translation was produced by our data team in the Argentinean news agency La Nación. With the summer holiday season in the middle of the translation process, the work took us around five months. We hired an external translator but the data journalism world was new to him, so we held several meetings to polish expressions and adjust vocabulary to a wider Spanish audience. Gaston Roitberg (senior content editor) and Guadalupe López (social media expert and blogs coordinator) helped with the final editing. We worked with Microsoft Word, Google Docs and Notepad ++. I typed myself the code required, comparing the English version hosted in GitHub with the chapters in Spanish edited in Notepad ++. For the online publishing work in ASCII and Python we received invaluable help from Manuel Aristaran, Knight Mozilla Open News Fellow in our newsroom, to whom we are greatly indebted.

We also translated two illustrations: "The Data Journalism Handbook At A Glance" and the Guardian Datablog workflow - the Datablog's editor, Simon Rogers, provided us with the original Illustrator file and we worked over it.

The Data Journalism Handbook at a Glance - Screenshot from the online Spanish edition

The Guardian Datablog Workflow - Screenshot from the online Spanish edition

We believe the handbook is a great resource for the Spanish-speaking community interested in this emerging field. I myself read the entire book many, many times and every time I read it, I discover something new! The diversity of authors sharing their best stories and practices is a permanent inspiration for our work in Argentina. I often find myself saying during our brainstormings, “I think I read something similar in the handbook, let me check!”

Both the original English version and the Spanish translation are freely available online under a Creative Commons Attribution-ShareAlike license, which means that they can be freely downloaded, shared and built upon. The Spanish version of The Data Journalism Handbook can be found at the Interactives section of the La Nación website and there will be a permanent banner in our main data section linking to it. The English version can be found at datajournalismhandbook.org.

Three other translations, into Arabic, Chinese and Portuguese, are in progress and will be published later this year. The book has already been translated into Russian.

Data journalism is big in Brazil right now, and there is a fair number of reporters sharing techniques and discussing the recently sanctioned Freedom of Information law. One of them is CAR pioneer José Roberto de Toledo, who writes Vox Pública and is the head of the data team at O Estado de São Paulo. Marcelo Soares, another CAR evangelist, works at Folha de São Paulo and is the author of Afinal de Contas. Knight Fellow Gustavo Faleiros, creator of InfoAmazonia, a mash-up of satellite imaging and news articles about devastation in the rainforest, is embedded in Folha de São Paulo and edits the FolhaSPDados weblog.

Since the untimely death of blogrolls, unfortunately, it has become increasingly difficult to let the Web do the research job. Scanning Twitter and Facebook timelines to find references without coding abilities, on the other hand, is very time-consuming. The simplest solution, then, is to crowdsource this list. So, the spreadsheet is now open for everyone to chip in their references. To my surprise, it has grown from 10 do 50 entries in just a few days. Also, Chris Kirk has suggested the addition of a “language” category, and Liliana Bounegru reminded me of making it available under an open license.

So, please, share your references, and let’s crowdsource a public domain database of websites and blogs about data journalism.

Reports made much of how Eliot has grown his expertise “in his bedroom in Leicester” in England; he did so by focusing on one subject almost obsessively – the type of arms being used in Syria and where they come from, and he did it by close examination of videos posted on YouTube.

Some reports referred to Higgins in the same section as pieces on data journalism. Although this work is hard to pigeonhole, it is a reminder to anyone who wants to work with data that – apart from specialising in one area – organizing one’s data is always vital.

Unless you’re blessed with total recall, it’s no good being able to identify an RPG launcher from a single frame of video, unless you know how you’re going to organise the data you collect.

Working on a more mundane story, you might be pulling together a set of responses from a survey, or a group of responses from local councils, say, to a Freedom of Information request.

You’ve got to think ahead to how you’re going to use the data once you’ve collected it.

You need to set out the answers in such a way that you can sort and add them quickly and easily. You can make life much easier for yourself if you decide well in advance what your final spreadsheet might look like while you’re working on the questions.

For example – will you want to sort the data by place name and postcode, or just postcode? Decide at the start what data need its own column and save yourself the trouble of creating and putting data into separate categories after the results come in.  

Even more fundamentally – do you want to spend hours copying and pasting the answers into your spreadsheet? Probably not. Google documents – now known as Google Drive, is a great tool for doing exactly this: you design a spreadsheet and then it helps you generate a document which will populate the spreadsheet as each respondent completes it.

Some people send out actual spreadsheets instead of documents, and ask their respondents to fill in the blanks. This will ensures they get the answers they need in the cells they want them in, and it saves huge amounts of time - instead of finding the answers spread through the text in various places, possibly not in the order you wanted it, you can just copy and paste it from the individual responses into one master sheet. And it may well be useful reassurance for the organisation you’re polling to know exactly what you do and don’t want.

I suspect most of the growing band of data journalism trainers have their own favourite spreadsheet on which to break people in gently. I use the summary of donations to UK political parties at the 2010 General Election. It’s not big enough to be intimidating, the data is pretty clean, and there are some fun questions to be answered. When training in the classroom I soon found that it was more fun to let people loose on the questions rather than try to show them the answers first. After the session they are given a copy to work on at home and refresh their memory of how they got their answers (one of several bits of good practice I learned at the NICAR bootcamp and wished I’d thought of first!)

The questions and suggested methodology are given below if you want to have a go now. Answers next week.

Another great way of getting started is to look at the Guardian’s Datablog; choose a story that interests you, but don’t read more than the headline. Download the data – there’s always a link to it. Then see what stories you can find in the data (including the one the Guardian wrote) before returning to their blog to see how your findings compare with theirs.

Finally – a useful reminder of two things I did this week. Working in Serbia, and briefing some young Serbian journalists about the possibilities of data, I used my new favourite Google operator “inurl:hrvatska” to find the word meaning Croatia in any url on the Serbian domain (site:rs) and limited results to filetype:xls – the shortish list of results included a complete list of projects building cooperation between Serbia and Croatia, with the names of the experts on both sides of the border. Then we filtered using Excel’s text filter – which works well in an inflected language like Serbian, where names will change according to the context, but you can quite easily ask for text containing the key part of the name which will not change – so Split can be Splita or Splitu, but you can search for anything containing the string “Split”. There wasn’t a single number in the spreadsheet, just words. Another useful reminder that data journalism doesn’t have to be scarily numerical!

Questions about the political donations spreadsheet:

1. What was the largest single donation?
2. Who donated it? Which party received it?
3. Did trade unions give only to Labour?
4. What was the total value of donations for the election?
5. What was the biggest non-cash donation? Who received it? How much money was involved in donating helicopter flights?
6. Which party got the most? Which party saw a sudden increase in donations in week?
7. Did anyone donate more than once?