- Quote: "Data journalism or data-driven journalism (DDJ) is journalism based on the filtering and analysis of large data sets for the purpose of creating or elevating a news story."
Understanding how to use data to uncover and report on stories.
Data sources and collection methods: Understanding where to find relevant data and how to collect it is a key aspect of data journalism. This includes data scraping, FOI requests, surveys, and more.
Data cleaning and preparation: Before data can be analyzed, it often needs to be cleaned, corrected, and formatted in a way that facilitates analysis.
Statistical analysis techniques: Knowing how to apply basic statistical concepts like correlation, regression, and hypothesis testing can be invaluable in understanding and communicating data-driven stories.
Data visualization tools and techniques: Effective data visualization allows journalists to make complex information more accessible and understandable to their audience. This includes chart types, color schemes, and layout principles.
Ethics and privacy considerations: Data journalism often involves sensitive or private information, and ethical concerns must be taken into account when collecting, analyzing, and presenting data.
Social media and open source intelligence (OSINT): Social media platforms and other online sources can be a rich source of data for journalists investigating stories. OSINT techniques help gather insights from open source data on a variety of topics.
Storytelling and communication: Good data journalism requires strong communication skills to convey complex information to readers using clear, concise language and compelling visuals.
Collaboration and project management: Data journalism often requires teamwork across multiple disciplines, including data analysis, writing, design, and social media promotion.
Law and regulation: Data journalists must be familiar with laws and regulations governing data privacy, access to public records, and other issues that may affect their work.
Emerging technologies and trends: The world of data journalism is constantly evolving, with new tools, techniques, and trends emerging all the time. Staying up to date on these developments is essential for success in the field.
Infographics: These visually appealing representations of data help tell a story without relying on complex numerical analysis. They are designed to be easily digestible by a wider audience, and often published as standalone pieces using data derived from research.
Data visualization: Similar to infographics, data visualization uses charts, maps, and graphs to display information in an easy-to-understand way. This type of data journalism is all about presenting data in a way that is visually engaging and interactive to readers.
Interactive storytelling: This involves using multimedia elements such as videos, images, and audio to tell a story while simultaneously presenting relevant data. Interactive storytelling is often used to help readers engage with and understand complex data sets.
Social media analytics: This type of data journalism involves tracking and analyzing social media data to uncover trends or patterns. Social media analytics can also be used to provide context to a news story or to monitor public opinion on a particular topic.
Newsroom analytics: Analytics tools can be used by newsrooms to track website traffic, reader engagement, and social media shares. This helps journalists understand how readers are consuming their content, and which topics are resonating with their audience.
Computer-assisted reporting: This involves using software and statistical analysis to crunch large data sets to uncover stories that would not have been visible otherwise. This type of data journalism may include database investigations, data mining, and scraping.
Long-form data journalism: These are in-depth, data-driven stories that use complex data sets to tell a story in a compelling and engaging way. Long-form data journalism is often published as a series of articles or multimedia pieces over an extended period of time.
- Quote: "Data journalism reflects the increased role of numerical data in the production and distribution of information in the digital era."
- Quote: "It involves a blending of journalism with other fields such as data visualization, computer science, and statistics, 'an overlapping set of competencies drawn from disparate fields'."
- Quote: "Many data-driven stories begin with newly available resources such as open source software, open access publishing and open data, while others are products of public records requests or leaked materials."
- Quote: "This approach to journalism builds on older practices, most notably computer-assisted reporting (CAR) a label used mainly in the US for decades."
- Quote: "Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings."
- Quote: "The findings from data can be transformed into any form of journalistic writing."
- Quote: "Visualizations can be used to create a clear understanding of a complex situation."
- Quote: "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."
- Quote: "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."
- Quote: "The process builds on the growing availability of open data that is freely available online and analyzed with open source tools."
- Quote: "Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings."
- Quote: "Other labels for partially similar approaches are 'precision journalism', based on a book by Philipp Meyer, published in 1972, where he advocated the use of techniques from social sciences in researching stories."
- Quote: "Telling stories based on the data is the primary goal."
- Quote: "This approach to journalism builds on older practices, most notably computer-assisted reporting (CAR) a label used mainly in the US for decades."
- Quote: "Many data-driven stories begin with newly available resources such as open source software."
- Quote: "Data-driven journalism might help to put journalists into a role relevant for society in a new way."
- Quote: "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."
- Quote: "Visualizations can be used to create a clear understanding of a complex situation."
- Quote: "Many data-driven stories begin with newly available resources such as open access publishing."