Category Archives: Knowledge Management

Guest Post: A knowledge management system capable of blinking red

Inattention to critical knowledge is an old problem. Lessons are forgotten, near misses are ignored, caution is dismissed, disasters result. Titanic. Bhopal. AIG. Katrina. Fukushima. And on and on.

Knowledge Management (KM) is supposed to make the right information available to the right people at the right time in the right form—and to the best level of certainty possible—for making the most appropriate decisions when and where they are needed. KM should also direct the attention of decision makers to critical information and help them make sense of it. The bigger the stakes, the more situational awareness and mindfulness are needed. Continue reading

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Guest Post: The dirty dozen: twelve ways to fail at effective decision making

In the course of my decision analysis, analytics, and intelligence work for businesses and industry, I have identified a set of common points of failure in a typical decision engineering initiative.  These characterize the “hidden traps”, where decision makers often struggle to preserve the integrity of the Decision Engineering life cycle.

Below is the chain of those failure points. I have listed them in a sequence in which I have found them to typically occur.  Each one encompass the preceding two points in the list.

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Predictive analytics is not enough

The idea of predictive analytics can seem like magic: how, really, can a computer predict the future? Yet we’ve seen a lot of success based on this advanced technology in recent years, from Netflix to Amazon, Google, and more. These companies mine a massive amount of data every day for patterns, and it drives massive revenues.

However, for a widespread class of situations, predictive analytics alone aren’t enough. Consider the decision model below, which I introduced in my last post. The blue graphs on the right-hand side are based on predictive analytics, but they are only building blocks in the full model.  They are not enough on their own.

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What’s really frightening about Artificial Intelligence? It’s not what you think.

OK, I’ll admit it. AI scares me.  But not for the usual reasons: I’m not too concerned about robots taking over the earth or even the Singularity, as are many of my friends.  What does frighten me is the distraction that AI represents from the problems that matter.  The ones that need our judgment, our ethics, our humanity, our instincts, our rational subconscious, where we keep humans in the loop. These problems are best solved through a collaboration, where we use computer help where it’s best applied, giving us better data, evidence-based analysis, “moneyballing” governmentAnne Milgram’s great work fighting crime with data, or Ruth Fisher’s game-theoretic analysis of everything from the dynamics of the U.S. health care system to cap-and-trade carbon schemes.

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