zondag 4 januari 2015

10 LESSONS FROM DATA JOURNALISM TRAINING

In the nineties of the past century it was called Computer Assisted Research and Reporting; now it is called Data Journalism. Since I early retired from the School of Journalism about three years ago, I decided to focus on training working journalists in data journalism. The Netherlands is a bit small for these activities and most of my work is in Europe, the borders of Europe and Sub-Sahara Africa. Training takes a couple of days from a 3 days minimum to 5 days maximum. Looking back over the past year I come to the following conclusions. Published at Paul Bradshaw Online Journalism Blog

  1. The interest in training is substantial. In 2014 I visited 10 countries, had 60 training days and trained about 150 journalists. On average this is about one training per month, but one has to bare in mind that traveling takes a lot of time, each course needs preparation and at the end I have to write a trainer's report. For each training I create a folder in the cloud with examples and assignments related to the country I am working. One cannot do a training in Dar es Salaam using examples of Dutch hospitals. You must have local data and maps.
  2. Participants to the course are always working journalists coming from print, hard copy and on line, but TV as well. They know how to tell a news story. And that is exactly what they want to leave behind. They want to go deeper than text and a photograph, more research and more backgrounding. Data journalism is an eye opener, providing a new perspective on the profession. News stories are not re written press releases, but stories based on data, not re-active reporting but pro-active reporting.
  3. What Word is for text is Excel for numbers. Data journalism without spreadsheets is impossible. And that is often the first challenge. Simply because you choose journalism as a profession because you like writing and nor doing calculations. And now the numbers are back. Of course it is not rocket science, but it is about simple things like percentages and averages, or making a top ten, ordering from high to low. However moving to statistics, calculating a relationship between variables in a cross table, becomes sometimes too difficult. Generally I leave this topic to a more advanced training session. Scraping data with for example Outwit-Hub is like fishing the meat balls from of the html-soup. Scraping a table from a web page is for most participants not too difficult. Scraping a large number of web pages using a script or a scraper is only workable for those who have some affinity with spreadsheets and have some knowledge about html.
  4. Finding data is especially outside Europe a problem. In the Netherlands it is relatively simple to find data about mayors and municipalities, quality of hospitals, or crime in cities. Moving outside the Netherlands creates trouble. Sometimes small; in Belgium for example names of municipalities are in Dutch and French, this difference in spelling could become a hindrance for mapping. On the other hand in Tbilisi Georgia there is no database with crime records or violence against women. If you want to produce a homicide map you have to collect the data yourself. Sometimes the data are available, but they are in pdf format. A crime reporter from Capetown for example gets every year the crime figures from the police stations. More than a 100 pages in pdf. Impossible to write to write the story unless you put the numbers in a spreadsheet. Sometimes numbers are politically sensitive. When an analysis on local level shows that the winning candidate(with more than 75% of the votes national) is not popular everywhere, a training could be canceled. Sometimes there are no data at all or they are completely incredible. Analysis of traffic accidents becomes impossible when the police is using traffic fines to generate an extra income. In that case you have to go back to international data about a country, using data from the Worldbank, IMF, WHO or data from ratings agencies like S&P. However a solution could be simple. Grahamstown, South Africa has lots of trouble with the supply of water to the town. A small group of participants in a training decided to collect their own data. They visited 10-15 restaurants in the city and recorded how many days there was no water, and of course what the economic consequences were. A beautiful pin point map with data was the outcome.
  5. Visualizations are important. We don't print tables in a newspaper. Visualizations in Excel or Google Excel are interesting, However Datawrapper or Tableau work easier and simpler. Visualizations are not only for on line, but also for the hardcopy edition. So you must be able to export the visualization to a format (and resolution), which can be used in print. Producing visualizations in Tableau and Datawrapper makes your data public, because you have to upload the data to the server. That has not always your preference. Only Datawrapper has the possibility to install the program on your own server.
  6. Making maps is  an issue of its own. Using Google FT it is possible to produce two different type of maps: pinpoint maps and polygon maps. Making a map for the spread of HIV-Aids over districts in Tanzania is relatively simple. If you have the data and the map. But where is a map of districts in Tanzania in Google format. And secondly how could you produce a map for on line and for print which are exactly the same? The solution is to work with an open source mapping program like QGIS. Connect you data and map in Qgis, produce a map and export this map in a print format; next export the map in Google format and publish that map in Google FT. For most participants this is too much, again a new program with a mysterious interface. Best solution is to make a division of labor, and teach only this mapping program to specialists, who are working closely together to designer and info graphic editors.
  7. Although we are working with data the result must be story; it is journalism and not computer technology. The question is how to personalize your data; finding persons which illustrate your analysis? I noted that only a minority of the participants are blogging. They know how to do a story, but have not much experience in writing for on line. This makes it also difficult to explain the use of 'embedded links', to add maps for example to a story.
  8. A training must be more than a collection of tricks. I use the concept of 'story idea' to bring in more journalism in the training. At the start of the training every participant should bring an idea for a data journalism story. During the training they get time to work on their topic: collecting, analyzing, visualizing and writing the story. At the end they present their findings to the whole group.
  9. At the end of the training comes the question how are we going to do this in the news room? How can we implement data journalism in the day to day production? I pay attention to various models for introducing data journalism; ranging from data journalism projects to ToT(training of trainers) to spread the message. However on top of my list is always: continue practicing, otherwise your skills will be lost within a month after the training.
  10. Funding  data journalism training is coming from two sources: NGO's or media companies. When funding is coming from NGO's you generally have a group of journalists coming from a wide range of media. There is not much to say about implementation or follow-up. Generally the outcome of their story ideas work as showcase; showing how data journalism could be done. When working with media companies directly, the situation is different. The involvement of management and editor is larger; the journalists in the training know each other. The production during the training will be published, and immediately after the training projects will be started based on time and money. If the projects get stuck, or they need more skills, I am again invited to do some (re) training or consultancy. In the end it is a two-tier process. When a journalist takes part in an NGO training and they are really interest in data journalism an invitation for an in-company training will follow.

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