Data-driven journalism is a journalistic process based on analyzing and filtering large data sets for the purpose of creating a new story. Data-driven journalism deals with open data that is freely available online and analyzed with open source tools.[1] Data-driven journalism strives to reach new levels of service for the public, helping consumers, managers, politicians to understand patterns and make decisions based on the findings. As such, data driven journalism might help to put journalists into a role relevant for society in a new way.
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According to information architect and multimedia journalist Mirko Lorenz, data-driven journalism is a workflow that consists of the following elements: digging deep into data by scraping, cleansing and structuring it, filtering by mining for specific information, visualizing and making a story.[2] This process can be extended to provide information results that cater to individual interests and the broader public.
Data journalism trainer and writer Paul Bradshaw describes the process of data-driven journalism in a similar manner: data must be found, which may require specialized skills like MySQL or Python, then interrogated, for which understanding of jargon and statistics is necessary, and finally visualized and mashed with the aid of open source tools.[3]
Telling stories based on the data is the primary goal. The findings from data can be transformed into any form of journalistic writing. Visualizations can be used to create a clear understanding of a complex situation. Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development. This connection between data and story can be viewed as a "new arc" trying to span the gap between developments that are relevant, but poorly understood, to a story that is verifiable, trustworthy, relevant and easy to remember.
Based on the perspective of looking deeper into facts and drivers of events, there is a suggested change in media strategies: In this view the idea is to move "from attention to trust". The creation of attention, which has been a pillar of media business models has lost its relevance because reports of new events are often faster distributed via new platforms such as Twitter than through traditional media channels. On the other hand, trust can be understood as a scarce resource. While distributing information is much easier and faster via the web, the abundance of offerings creates costs to verify and check the content of any story create an opportunity. The view to transform media companies into trusted data hubs has been described in an article cross-published in February 2011 on Owni.eu[4] and Nieman Lab.[5]
The process to transform raw data into stories is aking to a refinement and transformation. The main goal is to extract information recipients can act upon. The task of a data journalist is to extract what is hidden. This approach can be applied to almost any context, such as finances, health, environment or other areas of public interest.
In 2011, Paul Bradshaw introduced a model, he called "The Inverted Pyramid of Data Journalism".
In order to achieve this, the process should be split up into several steps. While the steps leading to results can differ, a basic distinction can be made by looking at six phases:
Data can be obtained directly from governmental databases such as data.gov, data.gov.uk and World Bank Data API[6] but also by placing Freedom of Information requests to government agencies; some requests are made and aggregated on websites like the UK's What Do They Know. While there is a worldwide trend towards opening data, there are national differences as to what extend that information is freely available in usable formats. If the data is in a webpage, scrapers are used to generate a spreadsheet. Examples of scrapers are: ScraperWiki, Firefox plugin OutWit Hub or Needlebase. In other cases OCR-Software can be used to get data from PDFs.
• How to analyze unfamiliar data: circle, dive, and riff[7]
Usually data is not in a format that is easy to visualize. Examples being that there are too many data points or that the rows and columns need to be sorted differently. Another issue is that once investigated many datasets need to be cleaned, structured and transformed. Various open source tools like Google Refine, Data Wrangler and Google Spreadsheets[8] allow uploading, extracting or formatting data.
To visualize data in the form of graphs and charts, applications such as Many Eyes or Tableau Public are available. Yahoo! Pipes and Open Heat Map[9] are examples of tools that enable the creation of maps based on data spreadsheets. The number of options and platforms is expanding. Some new offerings provide options to search, display and embed data, an example being Timetric.[10]
To create meaningful and relevant visualizations, journalists use a growing number of tools. There are by now, several descriptions what to look for and how to do it. Most notable published articles are:
As of 2011, the use of HTML 5 libraries using the canvas tag is gaining in popularity. There are numerous libraries enabling to graph data in a growing variety of forms. One example here would be RGraph.[13] As of 2011 there is a growing list of JavaScript libraries allowing to visualize data.
There are different options to publish data and visualizations. A basic approach is to attach the data to single stories, similar to embedding web videos. More advanced concepts allow to create single dossiers, e.g. to display a number of visualizations, articles and links to the data on one page. Often such specials have to be coded individually, as many Content Management Systems are designed to display single posts based on the date of publication.
Providing access to existing data is another phase, which is gaining importance. Think of the sites as "marketplaces" (commercial or not), where datasets can be found easily by others. Especially of the insights for an article where gained from Open Data, journalists should provide a link to the data they used for others to investigate (potentially starting another cycle of interogation, leading to new insights).
Providing access to data and enabling groups to discuss what information could be extracted is the main idea behind Buzzdata,[14] a site using the concepts of social media such as sharing and following to create a community for data investigations.
Other platforms (which can be used both to gather or to distribute data):
A final step of the process is to measure how often a dataset or visualization is viewed.
In the context of data-driven journalism the extend of such tracking, such as collecting user data or any other information that could be used for marketing reasons or other uses beyond the control of the user, should be viewed as problematic. One newer, non-intrusive option to measure usage, is a lightweight tracker called PixelPing. The tracker is the result of a project by ProPublica and DocumentCloud.[18] There is a corresponding backend solution to collect the data. The software is open source and can be downloaded via GitHub.[19]
There is a growing list of examples how data-driven journalism can be applied:
Other prominent uses of data driven journalism is related to the release by whistle-blower organization WikiLeaks of the Afghan War Diary, a compendium of 91,000 secret military reports covering the war in Afghanistan from 2004 to 2010.[21] Three global broadsheets, namely The Guardian, The New York Times and Der Spiegel, dedicated extensive sections[22][23][24] to the documents; The Guardian's reporting included an interactive map pointing out the type, location and casualties caused by 16,000 IED attacks,[25] The New York Times published a selection of reports that permits rolling over underlined text to reveal explanations of military terms,[26] while Der Spiegel provided hybrid visualizations (containing both graphs and maps) on topics like the number deaths related to insurgent bomb attacks.[27]
There is an open and helpful community of data-journalists, willing to share how raw data can be turned into good stories. Below is a selected list of posts worth reading.
For each phase there are now different tools. Each tool might have certain features that are helpful to solve a specific problem working with data. Beginners interested in data-driven journalism should explore the options in order to have a stack of tools ready on their computers to work with data. From these resources journalists should step by step gain experience and create their own "tool stack" to become productive.
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