• Beyond Data (part 2): Big-O snowballs, copycats, and super levers

    As computer scientists, one of our first lessons is about “big O” complexity of software. It’s used to understand the expected time for a program to run. “Big O” theory tells us that it’s the order of magnitude of a system—like O(n^2) or O(n)— that matters, much more than smaller factors. To understand the parts of a computer program that dominate the time required to run it, we know not to focus on the tiny parts of the system that irrelevant to the overall behavior. Measuring the performance of a line of code to the fourth decimal place, when it only runs a ten-thousandth of the time that other code does, is wasted time.

    As introduced in Beyond Data Part 1, we need to apply the same kind of insight to analyzing data, whether it be for business intelligence, decision support, dashboards, or other systems.

    Think about your company as a snowball rolling downhill.  If you start five balls down a slope, they’ll all roll a little while, but some will happen to hit stickier snow, and get a little bit bigger than the others.  Those heavier snowballs will have just a bit more momentum, so they’ll roll a bit further, and use up all the snow, gaining a bit more size in the process. Which makes them heavier, which makes them roll faster.  You get the idea: in general the fortunate few snowballs will end up much bigger, leaving the smaller ones stuck at the top of the hill and others much bigger and much faster. Continue reading

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  • Beyond Data (part 1): J. K. Rowling, Shakespeare, and the Sorcerer’s Decision

    I rode the train on a beautiful spring day through New England.  Arriving at the station, a few guys from our partner company greeted me happily; we were going to win this one.  We chatted about the customer on the drive across town: “They’re really turning over a new leaf”…”Very innovative”…”Hungry for new ideas”.

    When we arrived, it was a big meeting: folks from several departments, some who hadn’t met each other before.  The IT guys carried stacks of paper: the data model…the spreadsheets.  As usual, the first part of the meeting was a data dump, syncing up.  And, as usually happens, it was mostly about data.  Our customer took us through a great database schema on the whiteboard…his colleague handed out reports.  Great!

    'The decision is only as good as the data that supports it.' It was bound to be said. Click To Tweet

    Because every organization we work with does sooner or later.  But it’s not always true. Continue reading

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  • Decision lever visualization: an animated review

    Since thinking so much about levers in the last two posts, I’ve also been pondering the variety of levers I’ve built and seen, and the different purposes they serve in a decision model.   In particular, given that our goal is for models to be as easy to understand as possible to facilitate collaborative team alignment, I think that some principles are emerging.  Here are a few ideas. Continue reading

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  • Top-ten decision lever best practices

    “We need to win more work,” says the CEO.  “Can you think through how we could lower our prices to become more attractive to our customers?”

    A good decision engineer can’t help but ask “the lever question” at this point: “Ma’am, are we only to consider pricing, or would you be open to other approaches to winning more work?”  Confirming the scope of levers allowed like this is the most fundamental decision lever best practice, but there are many more.

    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.

    Continue reading

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  • Decision: I do not think it means what you think it means

    We use the word “decision” to mean two very different things.  If I say “I’ve decided that the moon is made of green cheese”, or “I’ve decided that the economy will deteriorate next year”, these statements aren’t necessarily about actions I’m going to take.  If, instead, I say, “I’ve decided to go to go to graduate school” or “I’ve decided to institute a new policy”, that’s fundamentally different.

    How?  The first kind of decision leads to a fact, either well-supported or not.  It is, essentially, using data and expertise, following its implications (deductively, inductively, or otherwise), and leading to a conclusion (which may have more or less justification: to fit this category it doesn’t have to be right). Continue reading

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  • The Decision Intelligence Ecosystem

    It’s been an incredible last few weeks in the Decision Intelligence world, as we’re seeing an ecosystem emerging with new vendors, articles in IEEE, the New York Times, at HBR, and much more. I’ve taken a first shot, in the graphic below, of mapping the ecosystem. It’s not at all complete, so please send me entries for new nodes. I’ll also be tweaking the graphic going forward to make it easier to navigate. Enjoy! Continue reading

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  • Baby steps

    It’s not easy selling into an emerging market, no matter how important it is.  We won two projects in the last week or so.  In both cases, the customer hadn’t heard  of decision intelligence before talking with us.  In one, they were looking for data analysis to guide a marketing investment; in the other, the question was to determine the effectiveness of various college course offerings. Continue reading

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  • About Levers

    I’m going to write a series of posts about the core elements of a decision model.  This one’s about Levers: simulations of things you can change as you make the decision.   We might also have called levers choices.

    It can be confusing: you’d think that a decision model would produce the choices as output, not as input.  Because it’s supposed to tell us what decision to make, right?  But things are a little backwards: the right decision is the one for which the levers will set in motion a chain of events, that in the future will lead to a desired outcome.  So from this point of view, the action of a lever belongs at the beginning of a decision model, not the end. Continue reading

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  • Counterfactual blindness and the invisible things

    This is an article about invisible things that matter.  Because these things are invisible, it’s going to be harder going than usual.   Because they matter, it’s worth it.

    There’s a story a friend told me last week about a plumber.  A women calls him in an emergency; there’s water everywhere.  He arrives quickly, takes 10 minutes to fix the problem, and hands the woman a bill for $100.

    “A hundred dollars!” she exclaims, “But you only took ten minutes!”.

    “Ah, says the plumber.  You didn’t pay me for the ten minutes.  You paid me for my thirty years of experience knowing exactly what to do.”

    This thirty years: that’s an invisible thing. Without it, the flood would have been a disaster. Continue reading

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