What happens when you take something and place it in an art gallery? Does the value change? Do having freshly contructed-and-painted walls, just-so setting, and sublime lighting change it? What about the other pieces in the gallery? As an example, let’s use Fountain, the famous twentieth century dada sculpture by Marcel Duchamp. Technically speaking, Fountain was a urinal rotated 90 degrees, signed ‘R. Mutt,’ and placed on a pedestal. The piece caused a scandal and a great amount of subsequent discussion. Surely, a receptacle wasn’t art! But it was. Why? Context.
By situating a “readymade” toilet into a gallery amidst conventionally assumed-to-be-art pieces, a lowly receptacle becomes a sculpture. The presentation and context, along with discussion, completes the conversion. It only requires new methods of perceiving. A shift in seeing changes a common object into something much larger—possibly the centerpiece of a movement. Once you gain the special way of seeing and of processing context then a whole new world opens. The same is true of data.
Are you producing data when you get a job? Lose a job? Buy milk at the store? Balk at rising prices or thrill at lowered prices? It seems terribly abstract to consider that by living your daily life you produce endless data streams. Millions of other people, living their lives, also produce data. Then, by combining and presenting this data in a new context it becomes information just as an everyday object set on a pedestal becomes art—the everyday combined and newly presented turns into something new and wonderful. It produces new ways of seeing.
People can have world-changing, visceral reactions to a single point of data. Stock prices rise and fall from a quarterly earnings figure—a single number. People feel bolstered or disheartened by new unemployment figures. Without a well-designed context a single data point is one person gaining or losing a job, buying a car, building a home. When a whole nation engages in a behavior and it’s turned into data we can finally see the forest for the trees.
Consider the Data360 Employment Report. On page one we have unemployment figures. Looking at unemployment over time we find that things are rough, but the unemployment rate dropped four tenths of a percent this month and that’s good. Moving on to page two, though, a whole new context is produced. Since 2008 the ratio of employed and total adults has reduced considerably. The labor force participation rate, or the number of people working or trying to find work, also decreased. So the unemployment rate may have gone down because fewer people are part of the employment pool. This is a possible conclusion produced from the juxtaposition of these graphs.
Moving on to page three the total nonfarm payroll data provides a whole new context. Looking at this graph it is clear that although unemployment is extremely high, there are still more total people on payrolls. There are many factors that can be brought up for contextualization: vast population growth between the beginning of the chart and now, possible changes in reporting over time, etc. Then the whole game is changed once again by the payroll change graph on the right-hand side. The payroll alone could produce a very rosy picture, but the month-to-month change from those data points produces a whole other way of seeing. Although the absolute numbers climb ever upward, the past few years of monthly change produces a starkly different picture. Then, on page four, the context shifts once again.
Ponder the graph of people unemployed 27 weeks and over. Really look at it. It is a powerful image—the number of people unemployed more than six months has skyrocketed in the past few years. Alone it tells a story, but it also frames every other graph seen previously. The unemployment rate seems heavier. The worker-to-population ratio decline seems steeper. It all feels much more real. That’s where the danger lies.
By the time these figures were presented the entire employment situation was thoroughly contextualized by other data—which pieces of the employment puzzles increased or decreased, but also how one data set informs another. Seen context-free, figures can be misleading, but by viewing this and our other reports the data is contextualized with other relevant data. Looking at one data point out of context is not especially useful, but placing it in a chart surrounded by informing, complementary figures helps produce understanding of the whole situation—the 360 degree view, if you will.
Curated data is carefully set into each report-as-gallery and by studying each graph and each page context is produced and informed opinions can be generated. What might seem as an isolated and common event in daily life, when set together carefully and without bias may become extraordinary. When trying to understand the larger context of data come to Data360, a carefully crafted data repository designed to help show every angle of data.
-Daniel Saniski, Data360