Reliable and readable infographics

How can you turn business data into an infographic in which every figure and every visual representation can be verified?

1960 € per session, excl. VAT

In short

This seven-hour day, excluding breaks, structures the production of a professional infographic, from the source dossier to final approval. Generative AI helps prioritise messages and propose a composition; participants check calculations, visual representations, rights and readability before preparing a deliverable ready for distribution.

Why this training?

An infographic can look convincing while distorting the data: a missing denominator, a disproportionate area, an ambiguous period or an overly assertive title. Its quality depends as much on the traceability of the information as on its appearance. AI speeds up proposals for messages and compositions, but is not a source of evidence. Separating generation, independent calculation and human validation makes it possible to identify errors before they become visual arguments. Running case study: The fictitious organisation « Avelune Réemploi » wants to present its quarterly collection results. A fictitious training dossier brings together volumes collected, material destinations and internal comments. Participants must produce an infographic for partners without turning an estimate into an established result. Transfer to the workplace: Reuse the verification dossier for a forthcoming professional publication: designate the data owner, repeat the checks after each change and obtain editorial approval before distribution.

Objectives of this training

  • Select sourced information suited to the recipient.
  • Recalculate the selected figures, proportions and differences.
  • Build a visual hierarchy without distorting the data.
  • Produce an AI-assisted infographic with checked text.
  • Assess the compliance, readability and accuracy of the deliverable.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Communications officers and editorial managers producing illustrated reports.
  • Business analysts and project managers presenting indicators to non-specialist audiences.

Prior skills

  • Be able to formulate and improve a request to generative AI.
  • Work with percentages in a spreadsheet and edit a simple layout.
  • Know the intended audiences and approval processes for your publications.

What you will work on

  1. Define the message and supporting evidence

    Objective: Define an editorial promise that can be substantiated. Audience, channel, main message; distinction between observation, estimation and interpretation.

  2. Check the selected data

    Objective: Establish a numerical basis independent of the generated responses. Totals, percentages, missing values, rounding and comparability of periods.

  3. Choose honest representations

    Objective: Match each message to an appropriate graphical form. Comparison, distribution, change over time; axes, areas, pictograms and limitations of decorative charts.

  4. Compose a verifiable version

    Objective: Assemble an infographic whose elements remain verifiable. Typographic hierarchy, contrast, density; limitations of text and figures embedded in generated images.

  5. Examine rights and transparency

    Objective: Identify obstacles to responsible distribution. Licences, attribution, consent from people depicted, synthetic content notices depending on context; risks of deepfakes and false visual testimonials.

  6. Test and validate the publication

    Objective: Measure quality and resolve remaining defects. Peer review, export checks, traceability of corrections and approval for distribution.

What you take away

  • Editorial brief and source matrix.
  • Calculation table and correction log.
  • Finalised infographic with sources, notices and a text alternative.
  • Completed validation checklist and version log.

Method and assessment

  • Short input sessions followed by immediate practical work on the running case study.
  • Individual work followed by work in pairs, with peer review of the outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a tool authorised by your organisation and a layout environment. Bring a usable set of visual identity guidelines and, optionally, an anonymised document that may be shared.

Observable criteria: Every published figure refers to an identified source and a reproducible calculation. ; Units, periods, denominators and rounding are made explicit. ; No inconsistency remains between data, titles and visual proportions. ; Rights, necessary consents and applicable notices are documented. ; The reading test enables the main message and associated figures to be identified without ambiguity..

Frequently asked questions

Do you need to know how to draw?

No; being able to adjust a simple layout is sufficient to work on hierarchy and charts.

Can AI verify its own figures?

It can suggest checks, but figures are recalculated independently and checked against the sources.

Can internal data be used?

The exercises use fictitious or anonymised cases, only in authorised environments.

Does a generated illustration remove the need to check rights?

No. Terms of use, resemblances, consents and applicable notices still need to be examined before publication.

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