Multimedia Content Creation

Infographic & Data Storytelling

Numbers that click into a shape someone remembers: accurate, annotated, and readable whether or not the reader can see it.

Data means nothing to a reader who can't picture it. Infographic & Data Storytelling turns a dataset, comparison, or process into a chart and narrative someone can take in at a glance, with accurate encoding, plain annotations, and alt text that carries the same story to a screen reader.

You walk away with a chart whose numbers check out, an encoding that does not mislead, alt text that tells the same story, and every size your channels need.

Turn your data into a story
Analyst arranging chart panels and captions around a central data story

Some of the 500+ brands we've worked with

See all references
  • Lexus
  • Findeks
  • Yemek.com
  • MNG Kargo
  • Sina Pırlanta
  • DYO
  • Eureko Sigorta

How we run it

How we build the story around the numbers

We start from what the data says, then find the shape that says it clearly.

  1. Get clear on the one thing this should teach someone

    A chart trying to say five things says none of them well, so we pick the single comparison, trend, or relationship the visual needs to carry.

    One sentence describing the core takeaway.

    AI assist
    Agents can reconcile the source figures, recompute the derived values, and flag rows that do not add up. Whether the underlying number is trustworthy in the first place is a judgment made with whoever owns it.
    Human gate
    An analyst signs off on the one-sentence takeaway before anyone sketches a chart.

  2. Check the data itself

    We check units, sample size, time period, and what's excluded. That confirms what the numbers can honestly support before anyone starts designing.

    A verified data table with sources and caveats attached.

    AI assist
    A messy spreadsheet takes less time to normalize when an agent flags mismatched units. The analyst still interprets the numbers.
    Human gate
    A person who knows the data confirms the sample size and caveats before design starts. A cleaned-up spreadsheet isn't the same as a verified one.

  3. Choose an honest visual encoding

    The chart type has to match the claim: a bar chart for comparison, a line for change over time. It also has to work without relying on color alone.

    A visual draft with the encoding decision explained.

    AI assist
    Agents can render the same data in several encodings so the options are visible side by side. Which encoding is honest, including for readers who cannot distinguish the colours, is decided by someone who understands the data.
    Human gate
    A designer checks the chosen encoding against the claim it's making before it moves to annotation.

  4. Write the annotations and alt text together

    The labels, callouts, and alt text get drafted at the same time, so someone using a screen reader gets the same story as someone looking at the image.

    A near-final version with full annotation and alt text.

    AI assist
    Agents can draft annotations and a first alt-text pass from the chart's structure. Whether the alt text carries the same takeaway as the visual is confirmed by reading it without the image.
    Human gate
    An editor reads the alt text against the finished visual to confirm a screen reader gets the same story.

  5. Ship the sizes each channel needs

    One master version becomes the crops and formats each channel uses: social, deck, report, without breaking the story along the way.

    The final asset set plus a short note on where each version fits.

    AI assist
    Agents can export each channel size and check that labels remain legible after scaling. Which crops are acceptable is a design call, because a cropped axis can change what the chart says.
    Human gate
    Someone checks every exported crop still carries the story before it ships to a channel.

The chart still needs someone who understands the data

A model may accept a truncated axis, a cherry-picked date range, or a color choice that hides a pattern from colorblind readers because the result looks tidy. We use it for the repetitive work around the decision, such as cleaning a spreadsheet, trying five layouts, and drafting annotations. Then someone who understands the data checks what each visual choice implies. If the honest version is plainer, that is the version we publish.

What you get

What lands in your hands

You get the finished visual, but also the data underneath it and the reasoning that shaped it.

  • an audit report with flagged rows

    The verified data table

    Every number behind the visual, with its source and any limitation noted, so nobody has to take the chart on faith.

  • an infographic panel with charted data

    The finished infographic

    The core visual asset, encoded honestly and annotated in plain language, ready to publish.

  • caption and alt-text notes beside a media frame

    Full alt text and accessible copy

    A written version of the same story for screen readers and anyone browsing with images off.

  • one source document fanning into smaller formats

    Channel-sized exports

    The crops and formats you'll post: social card, deck slide, print, all pulled from one master file.

We call it done when: the numbers check out, the encoding doesn't mislead, the alt text tells the same story, and you have every size your channels need.

Who it fits

The dataset needs a clearer shape

This fits a number, comparison, or process that's real but invisible until someone draws it. If the data itself still needs validating, that's a separate job that comes first.

A good fit when

  • You have real numbers, survey results, usage data, a trend line, but nobody outside the team can picture what they mean.
  • A process or comparison takes three paragraphs to explain, but one accurately labelled chart would let readers see the relationship at a glance.
  • Readers need to save, cite, or reuse the finding, but a text-only explanation gives them no standalone visual with a source and clear takeaway.

Better handled as other work when

  • The data hasn't been checked yet. Sourcing and validating the numbers comes first, as its own step.
  • Honest encoding would undermine the point the chart is meant to make, because the result only looks strong on a flattering axis, date range, or comparison.
  • The research question, sample, or survey still needs designing, so there is no verified finding for an infographic to explain yet.

People who write and ship this content

Some of these consultants write the pages themselves. Others plan distribution or check how a piece performs once it's live, so the team below covers more than one stage of the work.

Content we've produced on this topic

Articleby Samet ÖzsüleymanPulling, Visualizing and Interpreting Data Using Search Console API with RArticleby Zafer YıldızFree Google Analytics 4 Looker Studio Template for Everyone

Tools we use

Tools behind this work

  • Datawrapper

    builds the chart itself with a real alt-text field attached, not added after

  • WAVE

    the final check on the embedded asset, not the source chart file

  • OpenRefine

    cleans and clusters the raw dataset before a single shape gets chosen

  • Coblis

    shows how the finished chart actually looks under color vision deficiency

  • Canva

    produces the per-channel sizes from the one approved chart, nothing redrawn

Start the work

Got a dataset nobody outside the team understands?

We can start with the dataset, the audience, and the point they need to grasp. From there, we choose the clearest honest shape for the story.
Talk to a Content Marketing specialist
Two people shaking hands on the start of the work

Before we start

Frequently Asked Questions

Both. We choose the chart type and encoding alongside the visual design. Separating those decisions tends to produce a pretty chart. It may also say the wrong thing.