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The Fundamentals of Data-driven Storytelling
The Fundamentals of Data-driven Storytelling
  • About this course
    • Course Introduction
  • Module 1 - Find
    • 1.1 How to Find Data for Storytelling and journalism
      • Starting with a question
      • Open data portals and platforms
      • Other sources of data
    • 1.2 How to get better data from a Goolge Search
      • Searching for filetypes and formats
      • More on Advanced Search operators
      • Other common Google Search operators
    • 1.3 Sourcing your own data
      • Creating a Google Form for Research
      • Creating a questionnaire with TypeForm
      • Using quizzes and comments as a sources of data
  • Module 2 - Get
    • 2.1 Turning websites and PDFs into machine readable data
      • Scraping data with Tabula
    • 2.2 An introduction to spreadsheet software
      • Google Sheets, Microsoft Excel and Libre Office Calc.
      • Finding your way around a spreadsheet
      • Simple web scraping with Google Sheets
  • Module 3 - Verify
    • 3.1 Can I use this data in my work?
      • Initial steps for verification
      • What do these column headings mean?
  • Module 4 - Clean
    • 4.1 What to do with disorganised data?
      • Why is clean data important?
      • Keep your data organised
      • Cleaning data cheatsheet
  • Module 5 - Analyse
    • 5.1 What is the story within the data?
      • Spreadsheet rows, columns, cells and tabs
        • Spreadsheet formats, forumlas and essential shortcuts
          • Using the VLOOKUP Function
            • Combine Data From Multiple Spreadsheets
    • 5.2 How to turn numbers into stories
  • Module 6 - Visualise
    • 6.1 Ways we visualise data
    • 6.2 Why we visualize Data
    • 6.3 How to visualise data
  • Course Testing & Feedback
    • ⏱️Quick course exam
    • 🎓Extended course exam
    • 📝Survey and feedback
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  1. Module 1 - Find
  2. 1.1 How to Find Data for Storytelling and journalism

Starting with a question

Previous1.1 How to Find Data for Storytelling and journalismNextOpen data portals and platforms

Last updated 2 years ago

"Always start with a question! Without a question to answer, there can be no meaningful analysis."

It's rare for us to start hunting for data without a question in mind. Even professional data analysts don't usually take a dataset and start looking for patterns without an idea of what they want to know first. This doesn't mean starting with a preconceived idea of what the answer to the question is, just that you have a starting point for inquiry.

A question defines the objective behind the 'wrangling' of the data.

The right question will help you to define:

  • What is the story you have to tell?

  • What is the problem you want to solve?

  • Define the issue you are addressing

  • What is the subject of your exploration?

  • What is the topic of your conversation

Read more on the subject of questions

In the scientific method, the starting question is known as the hypothesis. For example, "Water boils at 60 degrees Celsius" is a hypothesis that can be proved right or wrong with an experiment. You can rephrase this as a yes or no question: "Does water boil at 60 degrees?" Journalists also start with hypothesis or question, such as "There was corruption in this procurement process" or "Which states recorded the highest proportion of votes for Peter Obi?". For more on the use of questions and hypothesis to lead investigative stories, we recommend reading the excellent (and free) .

Once you have framed your initial question, you can use it to inform where you should look for data. A question on inflation in Africa, for example, could take you to or .

Story-based Inquiry by Mark Lee Hunter
The World Bank
Africa Data Hub