Data Audiences

Data Audiences

Understanding who will read and use a data visualisation is fundamental to how it should be designed. The sociocultural context of consumption directly informs production. Effective data communication is not simply a technical matter of choosing the right chart type — it requires thinking carefully about the audience: their knowledge, their context, their values, and how they are likely to interpret what they see.

Context of Production

Infographics and data visualisations are produced by a range of people with different roles and expertise. The production context typically involves three types of contributors:

• Designers — graphic designers and UX designers who make visual and aesthetic decisions
• Programmers and Data Analysts — data scientists, computer programmers, and data analysts who process and structure data
• Communicators — journalists, marketers, and communications professionals who shape narrative and purpose

Understanding the underlying goal of a visualisation is essential: Is it for internal organisational communication? Is it intended to persuade? Is it a news story? Is it meant to educate? The goal shapes every subsequent decision about form, audience, and message. Without clarity on purpose, data visualisations risk being poorly targeted or misleading.

Why Make an Infographic?

Different purposes generate different design and communication choices. Common goals include:

• Internal communication — sharing data within organisations to support decision-making
• Persuasion or rhetoric — using data to make an argument or advocate for a position
• News storytelling — presenting data as evidence within a journalistic narrative
• Education — helping audiences understand a complex topic through visual explanation

Understanding the underlying goal also means being transparent about it — audiences who know why a visualisation was made are better equipped to evaluate it critically.

Types of Audiences

Drawing on audience studies and media theory, we can identify multiple distinct ways of understanding who reads a data visualisation. Adapted from Marwick and boyd (2011), these include:

• Writer's audience — the imagined reader in the mind of the creator during the production process; shapes initial design choices
• Broadcast audience — the mass audience typical of one-to-many communication (e.g. a chart published in a newspaper)
• Networked audience — audiences connected to each other through participatory culture, fandom, or social media networks; they share and remix content
• Fictionalised or invoked audience — a tailored imagined audience constructed through specific design choices; the message is shaped to suit this imagined reader
• Interpretative community — groups who share common interpretive frameworks and cultural codes; they decode messages in similar ways

Encoding and Decoding

Stuart Hall's (1973) encoding/decoding model is foundational for understanding how meaning flows (and fails to flow) between producers and audiences. Hall argues that the meaning of a text — including a data visualisation — is not automatically transferred from producer to reader. Producers encode meaning through design choices; audiences decode that meaning through their own cultural frameworks, social positions, and prior knowledge.

The same data visualisation can be decoded in dominant, negotiated, or oppositional ways depending on the viewer's context. This means that an apparently neutral or objective chart can be interpreted in radically different ways by different audiences — and that producers always have a responsibility to consider how their choices will be received.

Social Semiotics

Social semiotics examines how signs and symbols carry meaning in social contexts. Applied to data visualisation, it asks: Who made the rules of visual design? How did those rules come to be? Whose interests do they serve? And how might they be changed? (Kennedy et al., 2016).

Aiello (2020) explains that sign-making in data visualisation is regulated by semiotic regimes — social practices governed by authority, expertise, or conformity in particular contexts. This means that visual conventions for presenting data are not natural or inevitable; they are products of specific cultural and historical forces. Social semiotics provides tools for analysing and questioning these conventions.

Data Visualisation Conventions and the Aura of Objectivity

Kennedy, Hill, Aiello, and Allen (2016) identify four key conventions that work together to create an impression of objectivity in data visualisations — what they call an aura of objectivity:

• Two-dimensional viewpoints — flat, 2D perspectives create a sense of objectivity by offering a detached, apparently neutral, 'god-like' view of the data; they appear less manipulated than 3D or illustrative forms
• Shapes and lines — geometric shapes and clean lines create symbolic order through repetition and visual hierarchy; they signal precision and control
• Clean layouts — minimal, well-organised designs draw on the heritage of modern graphic design to create readability and an impression of simplicity; clutter is read as imprecision, cleanliness as accuracy
• Data sources — including citations, links to raw data, or methodological notes creates a sense of transparency and legitimacy; audiences interpret this as evidence that the data is trustworthy

These conventions can make data visualisations appear objective even when they are not. Understanding how the aura of objectivity is constructed is essential for critical visual literacy — it enables audiences to interrogate the choices behind any visualisation rather than accepting it as neutral truth.