David Mcandless is an author, data-journalist, and information designer. Based in London, he started his career as a writer for a video game magazine in the late 80s. Over the course of 25 years, he has worked as a journalist, conceptual copywriter, web editor, creative director, and comedy writer.
In recent times, information and large chunks of data seem to be attracting more attention to its worth. Its value can be attributed to its usefulness in finding and predicting patterns. It is treated as a pseudo-commodity, trading data can be used to replenish and fortify an entity's’ knowledge on a particular subject matter, making them more informed about their environment, which lets them plan their actions accordingly.
David Mccandless recognizes how much data can be a ubiquitous resource. But to him, data doesn’t only hold the value in itself, the real value comes from the interpretations you can extract from it. This is why he says that data is more akin to “soil” rather than “oil”. There are a lot of operations you can do to move, extract, and manipulate data, but you can also extract metadata from the data itself, which can be closer to what kind of data is actually needed for whatever purposes the user is inclined to have.
If you were to look at most of his work, you would see that minimalism is a good way of describing David’s style of graphic design. The shapes that are present in the frame are composed of single, solid colours, one unique colour is assigned to each shape. The background is also a single solid colour. With no extra visual eye candy, the artwork has a clean work, and the viewer is drawn to focus on the space that the design manipulates. Which is important because his designs make use of spatial representation in order to convey what piece of data has how much weight in comparison to its other entries.
Spreadsheets, bar graphs, and pie charts are the dominant forms of expressing data nowadays. But these forms data presentation don’t actually do a good job of making the viewer understand the relation of the values with each other. If you looked at say, the spreadsheet, it presents all the values and labels in a table of rectangular cells that are mostly equivalent in space and distance with each other. This is good at conserving the space when presenting a lot of data, but the viewer is left with trying to interpret the numerical values with each other, the numbers in the cells only have meaning because of their text labels which are found in the same row or column.
The solution that David seems to have come up with can be best explained with one of his own works. Here, a diagram show the bandwidth or throughput of the human senses in comparison to various computer communication interfaces.
We can see by this design that the sense of sight has the most attention when it is processed in the brain. In comparison to other senses, we are very good at taking in visual data. David McCandless takes advantage of this in his designs. We could spend a good portion of our intellectual energy into interpreting a spreadsheet, trying to calculate the numerical distributions, relations, and patterns. But a better way would be to reassemble all the data into a format that maximizes the user’s potential understanding.
This same design holds up to that mantra The bandwidth of every sense is distributed as a fraction of the area around the entire graphic. Every bandwidth area has one thing in common, they are all connected to the bottom right corner. Their perimeters then grow and expand to the top left corner or until they hit their spatial limit. Their spatial expansion conforms to their respective values and differences to each other. Although this design doesn’t tell us the exact numbers of speed for the human senses, we can immediately have a good judgement of their ratios to each other.
David shows that the data itself is not relevant, it’s the interpretation of the data. Finding the patterns in the relevant information is easier once we visualize it. But to visualize it, the data has to be manipulated in such a way that the data is assorted in a visual coherent pattern. We have to find and bring out the smaller patterns in data in order to find the larger patterns.
