Innovation Is The Art Of Understanding Noise And Signal16 June, 2015 / Articles
Business runs on the assumption of predictability, which is itself grounded in the notion of having good historical information from which to predict. All human organizations work to enhance their ability to harvest information, filter that information down to what is “important,” and then channel that information into production processes that conform to a planned outcome. This has in turn led to an ever-growing hunger for more information, more data, and more input. And as the availability of data has increased exponentially, there is a growing desire to more clearly separate signal from noise, in order to more closely attend to what is relevant, rather than what is incidental. Which seems to make a lot of sense.
Alas, not only is this a fool’s errand, it is anathema to innovative thinking.
The danger in conflating information with prediction or control is a simple one: The stronger your desire to predict and control, the more likely you are to misinterpret data, the more likely you are to make bad choices about what is noise and what is signal. Looking at data solely through the lens of prediction is almost certain to distort understanding and insight. When prediction is the most powerful force driving your leadership decisions and your day-to-day management approach, certainty becomes your primary currency, above all others. Information then becomes the place you go to hunt for things to support what you have very likely already decided is true or important, rather than what you might discover to be true or important. This being the case, your organization, even when it seems secure and successful and productive, is likely to be on a quick path to irrelevance, because almost certainly you will miss critical, differentiating opportunities to be innovative. Your focus on the signal, in service to prediction, means that you will lose sight of the the rich value inherent in the noise.
As an example, think for a moment about the early days of rock music. Decades ago, manufacturers of guitar amplifiers worked hard to make the signal emanating from amplifiers very, very pure. The goal was to recreate a guitar’s analog signal with as much fidelity as possible . . . more signal, less noise. Then, one day, someone turned their amp up really loud, and pushed the speakers and the electronics way, way past tolerance. The result was a horribly distorted, randomized chunk of noise, with just the vestige of a signal getting through the system. Essentially, this was like an organization suddenly bereft of plans, structure, limits or much of anything. Just pure noise.
If you were listening for the guitar signal you already had in mind — the very same thing as looking at, say, financial data with only your planned strategic goal in mind — you’d have heard only noise: In the case of the amp, audio noise; in the case of a business, data noise. That being the case, you’d have set about trying to fix the noise, so that it would produce the signal you expected. But in the case of guitar amplification, someone heard the noise, and simply decided that the noise should be the signal. And what happened then was the birth of modern rock music. The noise got a label — distortion, overdrive, fuzz, and so on; the noise became the signal. What followed was decades of innovative, breakthrough music and sounds, much of which came from an ability to separate from predicted outcomes, to listen to what’s simply there, rather than what was intended to be there.
There is a lesson here for anyone who is concerned with how thinking relates to innovative organizations.
In an interesting way, causality is always an act of looking back, not looking forward. Most, maybe all, organizations seek to minimize organizational noise by means of agendas, plans, structure, and clarity; and they operate as if this will somehow make the future more predictable, more controlled. The old saying applies that business does not like surprises, so leadership works hard to make sure that every action A is directly, causally linked to a future outcome B. Information that supports this chain of causality is accepted; that which does not is deemed noise. Information, actions, reports, even organizational narratives over time become fine-tuned to match up with this false causality of events, and things begin to appear organizationally tidy and predictable and manageable. All signal. No noise.
Inevitably, at some point — much like early guitar amp manufacturers — the drive for organizational fidelity, for pure, noiseless signals passing through the entity, becomes so sterile that it has no ability to create anything new, or interesting, or . . . innovative. The organization transitions inevitably from an analog state (which at least has the possibility of creating “meaningful noise”) to a digital state, where it is nothing more than the aggregated sum of a bunch of tightly controlled information streams. Functional and efficient, perhaps; but bereft of any possibility of being innovative.
All of which leads to an interesting question: How is it that you distinguish noise from signal in organizations? And, perhaps more important with regard to innovation, how do you decide what is noise and what is signal? Signal is not an externally verifiable fact; it’s a bias in how we think, usually unconscious and unintentional. Both signal and noise are rich, robust inputs, and there is great risk in excluding one in favor of the other. So, the difference between an innovative way of thinking and a non-innovative way of thinking might be simply the ability to attend to information in an open, curious and creative way, and to avoid seeing information as either this or that, noise or signal.
Of course, at some point every individual, every organization decides that information X is important and Y is not. Fair enough. This is fine and these kinds of decisions are what make the railroads run.
Beware though, if you really want to be innovative. Pay attention. Because while X may get your daily work done, your future may be in Y.