The term "information overload" was first coined by Alvin Toffler in 1970, when Richard Nixon was President. It is now widely viewed as the chief curse of modernity.

It's all too obvious. We humans are flooded with more information than we can handle. Thanks to new technology, a fire hose of data is coming at us at a rate that exceeds our ability to make sense of it. As a result, we're often paralyzed in the face of so much information, and unable to make the right decisions.

A swarm of sages have been standing ready to advise us on how to manage this information overload. Their message is invariably: simplify. Reduce the amount of information we deal with so that it can be understandable by a human brain. Instead of trying to make sense of everything, focus on what's important. For example: look for important leading indicators that give you a head's up about the future, and put less important lagging indicators to one side.

That's certainly sage advice. Leading indicators are important for evaluating what's immediately ahead.  And focusing on them can certainly simplify decision-making. But what about the information you put aside? Those lagging indicators you ignored may tell you things you desperately need to know. And other information from the data fire hose may point to a future success…or a disastrous failure.

Fortunately, there's another way to deal with information overload…although it seems counterintuitive at first glance. Instead of gathering just a little stream of information from the fire hose, gather all the information you possibly can. It will be too much for your poor human brain to handle, so get computers to help you. Then train the computers to pick out patterns in the flood of data. Teach them what's important and what's not. And let them help you simplify your decision-making.

This is not news. Algorithms let humans and computers learn from each other. The computers give us lots of information, and we tell them what's important in it.

But although humans and computers have been talking to each for decades, we're crossing into an interesting new phase…one where humans, computers and machines all speak the same language. The latest buzzy term for all this is "the internet of things," which doesn't convey the full dimensions of the revolution just around the corner.

Ever since the industrial revolution, machines were dumb muscular things that did our bidding. But lately, we've been adding computers to machines, making them a lot smarter.

Connecting a computer to a machine allows it to tell us things that were previously impossible.  If you connect a few dozen sensors to a jet engine, for example, it will spew out a terabyte of information in the course of a flight. That's more information than we humans can make sense of. But if we write an algorithm that analyzes that terabyte, we may learn that one part or another is vibrating the wrong way and needs immediate replacement. That certainly simplifies the decision on when to send that engine back to the shop, doesn't it?

So, cutting back on information isn't always the best move.

The new model for human/computer/machine interaction is:

  • Hook up computers to machines
  • Collect the information that spews out
  • Teach the computers to help humans simplify the decision-making process

Information isn't necessarily the curse of modernity. It can be part of the solution to our problems. In fact, more data, not less, can be the key to helping us simplify decision-making.

Information overload? Bring it on!

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