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Artists turn data into experience by choosing what information to use, how to transform it, and what viewers will see, hear, or move through. Refik Anadol’s Unsupervised used machine learning to interpret data from MoMA’s collection and generate changing visual forms; Charles Sandison’s Figurehead 2.0 responds to museum visitors’ movement. These works show that data is a starting point for many different artistic methods, not one genre or technique.
What is data-driven art?
Data-driven art uses information as material, input, structure, or subject. The data might come from a museum collection, historical records, environmental information, or another source. Artists can process it in different ways and present the result as a digital canvas, sculpture, projection, or immersive room.
The Blanton Museum of Art’s exhibition Run the Code: Data-Driven Art Decoded emphasizes that artists use data differently. Data art should not be equated with generative AI: machine learning is one possible approach, not a requirement. Nor does data speak for itself. Artists choose what to include, how to transform it, and how to frame the result.
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A useful way to understand a data-driven artwork is to follow the choices that shape it. This is a practical framework drawn from the documented examples, not a universal recipe.
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- Select the source. Decide what information matters and consider what its source represents, including its limits.
- Choose a transformation. The artist might use an algorithm to interpret a collection, or arrange and animate historical words and images.
- Choose a sensory form and space. Information can become a changing screen animation, a projection, or another physical or digital presentation.
- Set the audience’s role. Viewers may observe the work, or their movement or other responses may affect it.
Two examples: generated forms and responsive installation
| Work | Source and transformation | Presentation and audience role |
|---|---|---|
| Unsupervised, Refik Anadol | Machine learning interprets and transforms data from MoMA’s collection to generate visual forms. | MoMA records custom software, a generative AI algorithm, real-time digital animation on an LED screen, and sound. The animation is changing and has no predetermined outcome; viewers encounter it as an installation. |
| Figurehead 2.0, Charles Sandison | A responsive algorithm combines words and images from eighteenth-century ship captains’ logs with museumgoers’ bodily movement. | The Peabody Essex Museum describes it as a site-specific projection. Visitors’ movement is part of the work’s system. |
Refik Anadol’s Unsupervised at MoMA
MoMA describes Unsupervised as an installation in which Anadol used machine learning to interpret and transform data from the museum’s collection. The resulting forms could exist in the archive but did not, according to MoMA’s description. Rather than illustrating a fixed dataset, the work produces a changing animation without a predetermined outcome.
MoMA’s exhibition record lists the work’s medium as custom software, a generative AI algorithm, real-time digital animation on an LED screen, and sound. The New York presentation ran from November 19, 2022, to October 29, 2023. Those dates document that presentation; they do not mean the installation remains on view. For discussion of Anadol’s process and the concept behind the work, see MoMA’s interview, “Refik Anadol on AI, Algorithms, and the Machine as Witness.”
Visitors become part of Figurehead 2.0
The Peabody Essex Museum describes Charles Sandison’s Figurehead 2.0 as a site-specific projection using words and images from eighteenth-century ship captains’ logs. Its responsive algorithm also incorporates museumgoers’ bodily movement, bringing historical source material into relation with visitors in the present.
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This is a specific example of audience participation, not evidence that every interactive artwork collects personal data. Its key design choice is that movement affects the installation.
Comparing data-driven artworks
When looking at more than one work, compare the concrete choices that shape the experience rather than treating all data art as the same kind of technology.
| Question | What to look for |
|---|---|
| What is the source? | A museum collection, historical records, environmental information, or another source—and what that source can and cannot represent. |
| How is it transformed? | For example, direct visualization, algorithmic interpretation, or generative output. |
| How does it change over time? | Is the presentation fixed, changing, or generated in real time? |
| What does the audience do? | Do visitors observe, or does their movement or response affect the work? |
| How is it presented? | Is it shown on an LED screen, projected into a space, or presented in another form documented for that work? |
These comparison questions synthesize documented examples; they are not an official classification system. They help distinguish what the data represents from what the artist’s method and presentation make possible.
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Projection and immersive presentation
Projection can help turn a digital artwork into an environment, but equipment and venue matter. In its 2026 report about DATALAND, Epson describes 4K large-venue projectors being used for the installation. That is a manufacturer’s account of a major-venue context, not an independent equipment evaluation or a recommendation for a home setup.
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What data can and cannot tell viewers
Data-driven art involves choices about source, transformation, presentation, and audience. A dataset may be incomplete or limited in what it represents; an algorithm’s output is shaped by how it is used; and a presentation can emphasize some aspects while leaving others out. A visually compelling result should not be mistaken for a neutral or complete account of its source.
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Artists work with varied source materials and outcomes. WIPO’s profile of Anadol describes a practice drawing on image, sound, text, and climate information, with outcomes that include digital canvases, data sculptures, and immersive rooms. That range is a reminder that data can be material for very different artistic experiences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.FAQ
How do artists use data to make art?
Artists select information, transform it through a chosen method, and present the result in a sensory form. The result might be a generated animation, a projection, a sculpture, or an immersive environment.
What is data-driven art?
Data-driven art uses information as a meaningful input, material, structure, or subject. It is a broad practice, not a single medium or method.
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How does AI turn data into an immersive art experience?
In Unsupervised, Refik Anadol used machine learning to interpret and transform data from MoMA’s collection into changing visual forms. MoMA records the work as real-time digital animation on an LED screen, with sound.
How do digital art installations respond to visitors?
Some installations use visitor movement or another response as an input. In the Peabody Essex Museum’s account of Figurehead 2.0, Charles Sandison’s responsive algorithm incorporates museumgoers’ bodily movement into a projection based on historical words and images.
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