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Hear, hear, Chris!

Since every decision is by definition made from the Best Available Data at the time, we all make B.A.D. decisions all the time. Analysis of decision outcomes is a logical step to enhance the leverage of good BAD decisions. (Yes, that really does make sense.)

Identifying the types of decisions that have delivered optimum positive outcomes will indeed vastly improve the probability that the Best Available Data will be far more excellent in quality than ever before.

Same process could identify the contrary type, too. What are the characteristics of criteria that have generated highly negative outcomes? Then, avoid them.

Great stuff ...

Thank you for your post. Keep it up.

Thank you for your nice post. Keep it up.


Another great post.

Of course, implicit in the expression "relevant data" is the concept of accurate and current data - two attributes that cannot be taken for granted.

In addition, I never fail to be amazed by the propensity of people to have a wealth of relevant data and still make clear deleterious decisions - even knowingly deleterious decisions. (There's a book to this effect by David Maister called "Strategy and the Fat Smoker".)

Finally, there is a real likelihood of analysis paralysis that can accompany more data, even more analyzed data.

Overall, I suspect that there is some cosmic equation whereby the product of data x decision-making insights tends to be a constant!

What? Even in the (future) face of hordes of relevant (and accurate and current) data you're worried that "other factors" could somehow encroach and snatch decision-making defeat from the jaws of presumably guaranteed victory?

Hmmm, sounds like you just may be advocating turning over most of the decision-making to the machines . . .

It worked out okay for Skynet, mostly.

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