One of the endearing characteristics of Poker is how it unapologetically embraces the power and value of lying. Unlike every other game in a casino, understanding the laws of probability are only part of the game. To be a really good poker player, you have to be both a really convincing bluffer, and an astute enough observer of your opponents to know when they are bluffing themselves.

Of course all poker players know this, so they do their best to avoid showing their “tells”; those unconscious behavioral ticks that might communicate how good or bad their cards really are to the rest of the players. They might drum their fingers, wrinkle their brow, play with their chips, change their breathing, the list goes on. The specific cards they’re holding are the key data their opponents want to access, but there’s real value in hiding the exact nature of that data for as long as possible.

If all you do in a poker game is stare at the back of your opponent’s hand and add up the cards visible on the table and in your own hand, you’re never going to notice that your opponent puts his hand in front of his mouth when he’s holding good cards. Once you figure that out, you’re going to look for that first, and then use the other obvious data as support. Read more about poker tells.

Fortunately, other complex systems of data have their own unintended, but revealing, “tells” themselves. Just like in poker, the key is in being observant and clever enough to know what to look for, and where to look for it. As you may have expected by now, these tells usually depend upon having heavy doses of noise built into them. At first glance, that noise might seem completely irrelevant to the topic of interest, but in fact is tied so tightly to an underlying truth that, it can provide a valuable shortcut to the nature of the systems we’re trying to understand. That’s why I’m so interested in them. I’ve collected a few of these here, but would love to know about any others that you use yourself, or have heard of.

1. The Economist Magazine’s Big Mac Index

In a globally integrated economy, the true relative value of currencies is a crucial piece of data, and an incredibly complex one. Those who trade currencies looking for arbitrage opportunities are always looking for even the most fleeting edge in understanding this complexity in real time, and investors looking for longer-term trends can crunch all sorts of government and international trade data to figure it out. Or, you can just track the price of a McDonald’s Big Mac around the world. That’s what The Economist magazine did back in 1986, and they’ve kept it up ever since (check out the link here). Once the chuckling subsided, many financial analysts recognized the wisdom and power behind the metric, and it has since generated genuine respect for its accuracy.

The insight behind it is in recognizing how unique a product a Big Mac is, and how its price is tied to so many important and complex metrics. Unlike most other globally available consumer products, it must be made locally. With the exception of beef-averse India, it is made with the same recipe and ingredients, according to a strict protocol, using local labor, on-site in the restaurant. That one sentence necessarily combines variables such as agricultural production and commodity prices, the efficiency of transportation and supply networks, wage rates, real estate values, energy costs, marketing and media costs, local and national taxation policies and rates, inflation and competitive pressures, and on and on. And, it’s a consumer product that can be purchased often, so it needs to adapt to changing conditions far more frequently than durable goods like cars or housing. All of these factors are then represented, completely unintentionally but out of necessity, in the price of that humble Big Mac.

The Chinese government might come up with all kinds of official rebuttals to international demands to more accurately value its currency, but the Big Mac Index is telling a different story. You just can’t deny the authority of what the world is willing to pay for two all beef patties special sauce lettuce cheese pickles onions on a sesame seed bun.

2. Corrugated Box Sales

Closer to home, there are all sorts of attempts made to figure out how the economy is going, or not going, in a given year. These are usually a combination of extrapolations of previous quarterly performance, and a tangle of expert expectations, predictions, and surveys of intangibles like consumer confidence. Of course, these work to some extent, but many are based on perceptions and public statements that are not always truthful, for the sake of either political spin, or to influence investor confidence. It all gets complicated and contradictory very quickly.

One simple metric that has withstood the test of time is the humble cardboard box order. Tracking sales of corrugated boxes turns out to be an elegant indicator of a wide range of economic factors, incorporating both the precision needed for running intricate supply chains, and the intangibles of sentiments like confidence, fear, and risk management.

Its accuracy is traced to both the ubiquity of corrugated boxes (80% of all non-durable goods are shipped in them) across manufacturers and retailers, and in their position as a cost center for these businesses. You cannot ship merchandise, and therefore book sales, without boxes, no matter what your product inventory is like. So, running out of boxes would be a horrible mistake to make. Yet, it costs money to buy and store excess boxes, so companies are reluctant to order more than they really need. So, orders for boxes neatly represent the market for, well, almost everything, as well as the intangibles of forward-looking expectations in exactly the time frame that producers and retailers are willing to commit money towards them.

I’ve been running market research projects for 27 years, but I can’t think of a survey question that would deliver that same combination of information so succinctly and truthfully.

3. Shoes, Not Eyes, Are The Windows To The Soul.

Customer demographics are an important metric for any business to understand, but even when you can afford to generate or buy that kind of data, it often offers little true insight to what those actual people are really like. And many times, small and medium-size businesses really can’t afford to spend money on that data.

If your target audience is visible at a specific place, such as shopping malls, event venues, or other public spaces, you can learn a great deal about them by simply parking yourself in a central location and watching nothing but their shoes go by. Shoes are another one of those data-intensive “tells” that can speak volumes to an astute observer. Again, it’s because of all the noise-like data that is unintentionally revealed through a person’s choice of shoe.

Shoes are one accessory that are so common that their absence in many situations is itself a powerful indicator; if you’re not at a beach or pool, not having shoes on in public is either an indication of a willful statement of independence, or an unfortunate indicator of genuine poverty. It’s one reason why having to take your shoes off for airport security feels like such an intrusion beyond its inconvenience.

But for the most part, the attention people do or do not pay to their shoes is a reliable reflection of their attitudes towards fashion vs comfort, their income level, their evaluation of the importance of the location or event they are attending, even their relationship status (for instance, tall women wearing spike heels are usually already in a relationship; they’re not worried about being taller than the men they might run into). Like many of these kinds of metrics, they are best interpreted by those with a really deep understanding of the metric itself; the more you know about shoes, the more you can know about people by looking only at their shoes.

In each of these examples, the common element is the counter-intuitive step of deliberately moving your focus away from what you really want to see. It’s like those often maddening 3D posters, where the only way to see the hidden image is to un-focus your eyes while continuing stare right at the image. (like poor Mr. Pitt was trying to do in an episode of Seinfeld). The picture is in there, both hidden and defined by the noise that surrounds it.

If you do develop your own metrics like this, you need to consider the risk in sharing it. As would be the case with figuring out a “tell” in your poker opponent, revealing the metric could destroy its effectiveness. The operative skill in this approach is in the ability to notice, and that ability depends on a willingness to pay attention to noise in a broader context, in allowing the mindfulness required to recognize the patterns to be accessed, and in having the freedom to fail for a long time before that expertise is developed.

So, consider what you are already an expert about, even if it might seem to have nothing to do with the topic you want to understand. How does that expertise allow you to recognize and interpret what someone else would completely miss? That’s what we’d love to know. Please post your own Noisy Dirty Shortcuts in the comment area below.

Original Link: http://method.com/ideas/10×10/the-art-of-noise