The Slippery Slope of Unbridled Semantics

A recent article titled “The Sleeper Future of Data Visualization? Photography” extends the definition of data visualization to a new extreme. Proposing photography as the future of data visualization is an example of the slippery slope down which we descend when we allow the meanings of important terms to morph without constraint. Not long ago I expressed my concern that a necklace made of various ornaments, designed to represent daily weather conditions, was being promoted as an example of data visualization. The term “data visualization” was initially coined to describe something in particular: the visual display of quantitative data. Although one may argue that data of any type (including individual pixels of a digital photograph) and anything that can be seen (including a necklace) qualify as data visualization, by allowing the term to morph in this manner we reduce its usefulness. Photographs can serve as a powerful form of communication, but do they belong in the same category as statistical graphs? A necklace with a string of beads and bangles that represent the last few days of weather might delight, but no one with any sense would argue that it will ever be used for the analysis or communication of data. Yes, this is an issue of semantics. I cringe, however, whenever I hear someone say, “This disagreement is merely semantic.” Merely semantic?! There is nothing mere about differences contained in conflicting meanings.

When I warn against the promiscuous morphing of the terms, I’m often accused of a purist’s rigidness, but that’s a red herring. When I argue for clear definitions, I am fighting to prevent something meaningful and important from degenerating into confusion. Data visualization exists to clarify information. Let’s not allow its definition to contribute to the very murkiness that it emerged to combat. We already have a term for the images that we capture with cameras: they’re called photographs. We have a term for a finely crafted necklace: it’s a piece of art. If that necklace in some manner conveys data, call it data art if you wish, but please don’t create confusion by calling it data visualization.

Aside from the danger of describing photography as data visualization, the article exhibits other sloppy thinking. It promotes a new book titled “Photo Viz” by Nicholas Felton. Here’s a bit of the article, including a few words from Felton himself:

Every data visualization you’ve ever seen is a lie. At least in part. Any graph or chart represents layers and layers of abstraction…Which is why data-viz guru Nicholas Felton…is suddenly so interested in photography. And what started as a collection of seemingly random photos he saved in a desktop folder has become a curated photography book.

“Photo viz for me, in its briefest terms, is visualization done with photography or based on photography,” Felton says. And that means it’s visualization created without layers of abstraction, because every data point in an image is really just a photon hitting your camera sensor.

Abstraction is not a problem that should be eliminated from graphs. Even though millions of photonic data points might be recorded in a digital photograph, they do not represent millions of useful facts. Photos and graphs are apples and oranges. By definition, an individual item of data is a fact. Photos do not contain data in the same sense as graphs do. A fact that appears in a graph, such as a sales value of $382,304, is quite different from an individual pixel in a photo. Graphs are abstractions for a very good reason. We don’t want millions of data points in a graph; we only want the data that’s needed for the task at hand.

In the following example of photography as data visualization from Felton’s book, the image is wonderfully illustrative and potentially informative.

Although useful, this montage of photographs that illustrates a surfing maneuver is not an example of data visualization. We can applaud such uses of photography without blurring the lines between photographic illustration and data visualization.

A graph is abstract in another sense as well—one that is even more fundamental: a graph is a visual representation of abstract data. Unlike a photo, which represents physical data, graphs give visual form to something that lacks physical form and is in that sense abstract. Financial data is abstract; a flower is physical. I wouldn’t use a photo to represent quantitative data, nor would I use a graph to represent a flower.

How we classify things, each with its kin, matters. Just because a gorilla sometimes stands on two legs, we don’t call him a man.

Take care,


10 Comments on “The Slippery Slope of Unbridled Semantics”

By Nate. March 31st, 2016 at 12:31 pm

Can we rail against the terms “data science” and “big data” next please ;-)

By Stephen Few. March 31st, 2016 at 12:51 pm


I’ve railed against “data science” and “big data” many times, but for somewhat different reasons. These terms were coined for marketing purposes, not to provide a definition that was lacking. “Data visualization” has been a useful term for many years. It means something, or at least it has historically. My concern is that it will cease to be useful as people use it to indiscriminately describe anything that they wish. Data science and big data are both terms that have never possessed an agreed-upon definition. They are nothing but sexy new terms for data analysis. If you were called a data analyst in the past, you are now likely to prefer the term data scientist. If you worked in the field of business intelligence in the past, you are now likely to describe your field as big data. Vendors and thought leaders love to slap new labels on things ever once in a while to generate new interest. I, on the other hand, am very happy to admit that I do work that people have been doing for a long time. I try to set myself apart by doing it well, not by deceitfully giving it a sexy new name.

By Andrew Craft. March 31st, 2016 at 2:15 pm

“Photo Viz” sounds silly and redundant, as if there were ways to consume a photo other than visually.

How exactly would the concept – “visualization done with photography” – be applied by practitioners? I mean, do we take pictures of our production plants performing well when we have a good month, and performing poorly when we have a bad month? Maybe record a 90 day time-lapse, if time is a variable? Who’s going to look at the pictures/videos and make sense of them? Or is sense-making simply not a priority in visualization for people like Felton?

Maybe the photos should be representative of the problem. Like, “Sales are down a bit this quarter, I’m off to find a trainwreck because I need an accurate photo visualization to depict where our business is heading- wait, let me check Google Images first…”

It’s all just a slippery slope toward Meme Viz.

By David. March 31st, 2016 at 6:16 pm

Agree with your argument.

Minor point – a graph can represent physical data via maps can it not?

By Stephen Few. March 31st, 2016 at 6:40 pm


A graph may represent quantitative values on a map, but without those abstract values, it is just a map, not a graph. The physical data in that case is context, not the primary data. Similarly, I could place quantitative values at particular locations on a photograph of a human body, in which case the photographic image of something physical would serve as contect for the abstract values. In that case a photograph could be included in a graph, but by itself it would not constitute a data visualization. It would be a rare case, however, that would lead me to use a photograph as the backdrop for a data visualization. In the example that I gave, a simple line drawing of a human body would usually work better than a photograph, just as a map, which is an abstraction, almost always works better than an aerial photograph for geo-spatial data visualizations.

By David. March 31st, 2016 at 8:22 pm

Thanks Steve – your explanation makes sense.

By Nate. April 1st, 2016 at 7:34 am

Stephen – Agreed with your above comments.

One case where data is combined with photographs (or even video) is in the telling of a story for a specific purpose. I’ve seen the multi-page magazine ads that show a “typical workplace” with little bubbles of data everywhere, often advertising for a IT company. TV commercials that show kids playing while dad works from home, with a statistic displayed after each cut.

Of course this isn’t data visualization – though sometimes small line charts will be used, and of course the pie chart is a favorite, since it is one of the few things that says “business” to most laymen & marketers. It’s the *use* of statistics and possibly a few visualizations to communicate the message in tandem with narrative & visual storytelling. Instead of allowing the data to do the talking, it makes implications that the data tells “this” story.

By Stephen Few. April 1st, 2016 at 8:16 am


Photographs may indeed be used in collaboration with data visualizations to tell a story, and doing this can be quite effective at times. This, however, does not make a photograph a data visualization any more than it makes a data visualization a photograph, as you point out.

By Dave C. April 13th, 2016 at 3:53 am

I find it interesting that the photo montage has been created with the latest images further back in order to more clearly show the sequence of events. Surely this is a perfect example of the ‘layers of abstraction’ that Felton claims to avoid?

Given that the wave appears not to have moved during the flip, the photo cannot be said to be a true representation of events and has been selectively constructed. It is therefore a deliberately false representation.

By kris erickson. April 13th, 2016 at 10:05 am

Let’s further erode the meaning of it even further. Isn’t everything we see data that our eyes deliver to our brains? Therefore everything we see is data visualization!

@Dave C. I’m very taken aback by their statement “Every data visualization you’ve ever seen is a lie. At least in part.” What does that even mean? ” Any graph or chart represents layers and layers of abstraction…”

An abstraction isn’t a lie, it’s an abstraction. A cartoon isn’t a ‘lie’ because it doesn’t represent a human figure ‘correctly’. A picture isn’t a ‘lie’ of a physical 3D object, it’s a 2D abstration of a 3D object.

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