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3.2 Using Data Journalism Techniques to Advance Data Literacies

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This guide adapts and expands introductory material on data journalism into a practical resource for educators, learners, and practitioners seeking to develop data literacies through hands-on, inquiry-driven approaches.

Adaptation note: This guide is adapted from ILDA – “¿Qué es el Periodismo de Datos?” and enriched with concepts from the Data Journalism Handbook. https://datajournalism.com/read/handbook/two/working-with-data/experiencing-data/data-methods-in-journalism

What is Data Journalism?

Data journalism is best understood as an evolution of traditional journalism in a digital age. Rather than relying solely on interviews, observations, or anecdotal evidence, it uses data as both a source and a lens for storytelling. Data represents real-world phenomena—people, behaviours, institutions—and journalists analyse it to uncover patterns, trends, and hidden issues. Technology plays a key role by enabling journalists to process large datasets, automate analysis, and present findings through compelling visual formats such as maps, charts, and interactive graphics. However, data alone is not enough: meaningful data journalism combines analytical insights with contextual reporting, ensuring that stories remain grounded in human experience and public interest. Ultimately, it is journalism that is evidence-driven, investigative, and enhanced by digital tools. See

Core definitions

  • Data journalism is journalism that uses data, technology, and analysis to find, understand, and tell stories
  • It treats data as information about people, communities, and systems, not just numbers
  • It is a form of reporting that uses quantitative data to uncover trends, patterns, and insights that might otherwise be hidden
  • It relies on empirical evidence (data) rather than anecdotal reporting, offering a more objective perspective

At its core, it transforms raw data into meaningful narratives that are accessible to wider audiences.

Key Principle

Data journalists act as a bridge between data and the public.

Why Use Data Journalism for Data Literacies?

Skill Description
Critical thinking Asking questions of data and interpreting meaning
Data handling Cleaning, structuring, and analysing datasets
Communication Presenting findings clearly through stories
Visual literacy Understanding charts, maps, and graphics

DJ process

flowchart TD
    A["Define Question<br/>Frame the problem"] --> B["Collect Data<br/>Gather relevant datasets"]
    B --> C["Clean Data<br/>Prepare and structure data"]
    C --> D["Analyse Data<br/>Explore patterns and trends"]
    D --> E["Interpret Findings<br/>Develop insights"]
    E --> F["Visualise<br/>Represent data visually"]
    F --> G["Tell Story<br/>Communicate findings"]

    %% Soft gradient-inspired pastel styling
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    classDef step2 fill:#E1F5FE,stroke:#81D4FA,color:#000;
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Techniques, Skills and Tools in Data Journalism

Core Techniques

Category Technique Description
Core Technique Data collection Collecting datasets from sources such as open data portals and FOI requests (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)
Core Technique Data analysis Examining data to find patterns, trends, and anomalies (see https://infogram.com/blog/data-journalism-definition-examples/)
Core Technique Data cleaning Preparing and fixing messy, incomplete, or inconsistent data (see https://ejc.net/resources/doing-journalism-with-data-first-steps-skills-and-tools)
Core Technique Story discovery Identifying newsworthy insights and angles from datasets (see https://ejc.net/resources/doing-journalism-with-data-first-steps-skills-and-tools)
Core Technique Data visualisation Presenting data using charts, maps, and dashboards (see https://infogram.com/blog/data-journalism-definition-examples/)
Core Technique Combining data with reporting Integrating data with interviews, context, and local knowledge (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)

Key Skills

Skill Area Skill Description
Key Skill Data analysis Ability to interpret large datasets and identify meaningful patterns (see https://infogram.com/blog/data-journalism-definition-examples/)
Key Skill Visualisation Presenting data through charts, graphs, maps, and infographics (see https://infogram.com/blog/data-journalism-definition-examples/)
Key Skill Coding (optional) Automating tasks, scraping data, and cleaning datasets (see https://infogram.com/blog/data-journalism-definition-examples/)
Key Skill Critical thinking Evaluating data quality, bias, and ethical considerations (see https://infogram.com/blog/data-journalism-definition-examples/)
Key Skill Communication & storytelling Turning complex data into clear, engaging narratives (see https://infogram.com/blog/data-journalism-definition-examples/)

Tools (Examples Mentioned or Implied)

Category Tool Type Examples / Description
Data Analysis & Handling Spreadsheets Excel, Google Sheets
Data Analysis & Handling Coding tools Python, R, scraping scripts (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)
Data Visualisation Visualisation platforms Infogram (charts, maps, dashboards) (see https://infogram.com/blog/data-journalism-definition-examples/)
Data Visualisation Interactive media Infographics, interactive charts and graphics (see https://infogram.com/blog/data-journalism-definition-examples/)
Data Sources Open data platforms https://data.gov/ (see https://infogram.com/blog/data-journalism-definition-examples/)
Data Sources Global datasets https://data.worldbank.org/ (see https://infogram.com/blog/data-journalism-definition-examples/)
Data Sources Public records FOI (Freedom of Information) requests (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)
Newsroom Tools & Techniques Automation Bots for automated reporting (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)
Newsroom Tools & Techniques Data extraction Web scraping tools (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)
Newsroom Tools & Techniques Participation Crowdsourcing data collection (see https://www.thebureauinvestigates.com/explainers/what-is-data-journalism)

Step-by-Step Practical Workflow

Define a Question

  • What issue are you investigating?
  • What do you want to understand or explain?

Example:

  • How has air pollution changed over time in my city?

Collect Data

Good data should be:

  • Relevant to social or economic topics
  • From reliable sources
  • Reusable (open licences)

Sources include:

  • Open data portals
  • Government datasets
  • Research repositories

Clean and Prepare Data

Checklist:

  • Are dates consistent?
  • Are there missing values?
  • Are columns structured correctly?
Issue Action
Missing values Fill, remove, or explain
Inconsistent formats Standardise
Duplicate entries Remove

Analyse Data

Common techniques:

  • Sorting (rankings)
  • Filtering (focus on subsets)
  • Aggregation (pivot tables)

Enrich Data

Enhancement strategies:

  • Add geographic coordinates
  • Combine datasets
  • Create indicators (rates, averages)

Visualise Data

Goal Recommended Visual
Compare values Bar chart
Show proportions Pie chart
Show trends Line graph
Show spatial data Map

Tell the Story

A good data story should:

  • Be accurate and transparent
  • Cite sources and methods
  • Be tailored to the audience
  • Use engaging narratives
flowchart LR
    A["Define Question<br/>What do you want to investigate?"] --> B["Collect Data<br/>Find relevant, reliable, reusable data"]
    B --> C["Clean & Prepare Data<br/>Fix formats, missing values, structure"]
    C --> D["Analyse Data<br/>Sort, filter, aggregate"]
    D --> E["Enrich Data<br/>Combine datasets, add context"]
    E --> F["Visualise Data<br/>Charts, maps, graphs"]
    F --> G["Tell the Story<br/>Communicate insights clearly"]

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    class C step2;
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    class F step4;
    class G step5;
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Tell the Story and Apply Learning

Telling the story is the culminating stage of the data journalism workflow. At this point, the goal is not simply to present results, but to translate data-driven insights into meaningful narratives that audiences can understand, question, and act upon.

A strong data story connects evidence with context. It explains not only what the data shows, but also why it matters and who it affects. In educational settings, this stage is particularly powerful, as it supports learners in moving from analysis to communication and reflection.

A good data story should:

  • Be accurate and transparent
  • Clearly cite sources and methods
  • Be tailored to the intended audience
  • Use engaging and accessible narratives

Beyond communication, storytelling opens up opportunities for applied learning, where learners actively use data to explore real-world issues.

Applied Learning Activities

These activities help bridge data analysis and real-world application, reinforcing both technical and critical data literacy skills.

Activity 1: Comparing Regions

Learners investigate how a particular issue varies across different locations.

  • Select a social issue (e.g., unemployment, pollution, healthcare access)
  • Compare across geographical regions
  • Standardise the data (e.g., per capita or percentage measures)
  • Interpret differences and consider underlying causes

Activity 2: Tracking Change Over Time

Learners explore how a phenomenon evolves, developing temporal understanding of data.

  • Analyse trends over time (e.g., vaccination rates, employment levels)
  • Identify patterns, spikes, or declines
  • Detect anomalies or unexpected changes
  • Reflect on possible explanations (policy, events, external factors)

Activity 3: Mapping Local Issues

Learners connect data to place-based contexts, making learning more relevant and participatory.

  • Map environmental, social, or infrastructure challenges
  • Use geographic data to visualise distribution
  • Engage with community knowledge or citizen-generated data
  • Reflect on local impact and potential interventions
flowchart LR
    Q([Ask Question]) --> D[/Data Collection/]
    D --> P[Preparation]
    P --> A["(Analysis)"]
    A --> C{Critical Interpretation}
    C --> V["[Visualisation]"]
    V --> S[/Storytelling/]
    S --> R([Reflection & Literacy Development])

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    class Q,R q;
    class D,S d;
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Examples of Data Journalism

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