User-centric data publishing (Alpha)
  • User-centric data publishing
    • Introduction
    • Who is this toolkit for?
    • How to use this toolkit
    • Dictionary of data terms
  • Contents
  • Section 1. Building the foundation for open data
    • A basic introduction to open data
    • Understanding our rights to access data
    • Open data maturity
      • Resources: Open data maturity
    • Ethics and transparency
  • Section 2. Planning for impactful open data initiatives
    • An introduction to the Data Landscape Playbook
    • Play one: Explore the problem and how data can address it
    • Play two: Map the data ecosystem
    • Play three: Assess the policy, regulatory and ethical context
    • Play four: Assess the existing data infrastructure
    • Play five: Plan for impact when designing your data initiative
  • Section 3. A user-centric approach to publishing
    • Understanding the user journey
      • The use case
      • Understanding different user needs
      • Targeting intended audiences
    • Engaging effectively with data users
      • Two-way communication and feedback
      • From data to story
    • Building communities around open data use
      • Characteristics of an open data user community
        • Purpose
        • Community enabler(s)
        • Collaborative method
        • Other observations
      • The current landscape of open data user communities
      • Engagement with data communities
    • Resources: User-centric publishing
  • Section 4: Publishing guidance for new data publishers
    • Open data licensing
    • The FAIR principles of data access
      • FAIR data assessment tools
    • Data quality and metadata
      • Tools and frameworks to help you assess open data quality
    • Publishing data on the web
  • Thank you
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  1. Section 3. A user-centric approach to publishing
  2. Engaging effectively with data users

From data to story

How can you publish data in a format that meets the needs of your users?

PreviousTwo-way communication and feedbackNextBuilding communities around open data use

Last updated 2 years ago

While some users may want raw data (actual figures that can be used to create bespoke graphs and charts for example), others, particularly non-analysts, those with limited analytical skills or those with limited time for analysis, may want headlines, insights or stories, alongside (or instead of) actual raw data.

From data to story

Turning raw data into something we can use to make a decision or form an opinion means deciding what to show, what to emphasise, and what to filter out.

  • Data = a number, statistic or measure (quantitative); a fact, an opinion or experience (qualitative)

  • Metric = data with specific parameters, type of measurement

  • Information = metrics with context

  • Insight = information with implications

  • Story = connected insights

The value of data increases as it turns into a story, because a story helps connect the dots between and paint a picture with single units of data. Those who work with data must be able to communicate why it is important or useful, otherwise the value is lost.

For more information about developing data stories, try this: a four step approach to .

storytelling with data