Silicon Valley State of Mind, a blog by John Weathington, "The Science of Success"
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    Welcome to a Silicon Valley State of Mind, thoughts tips and advice based on the consulting work of John Weathington, "Silicon Valley's Top Information Strategist."

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Silicon Valley State of Mind

Tips, thoughts, and advice based on the consulting work of John Weathington, "The Science of Success."

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Archive, May 2013. Switch to list view

    Posted by on in Strategy

    Happy Tuesday, folks! I hope everyone had a fun and relaxing Memorial Day weekend. I sure did; Kim and I lounged around all weekend, usually doing something close to nothing. If you know me at all, you know cooking was on the agenda; however, this year I went a little mellow compared to some of the other holidays. I need to drop some pounds, so I started on Atkins a few weeks back. It’s going pretty well so far; I’m down about 7 or 8 pounds. One adjustment that I thought was going to be tough was eating more vegetables; however, it has pleasantly turned out well. The key I’ve found is this: the right ingredients make all the difference—that’s true with diets and it’s true with strategy.

    One thing I’ve really grown to love is tomatoes. Before starting the diet, I would rarely eat tomatoes; however, now I eat two to three every day. And now that I’m a tomato connoisseur, I’ve noticed that not all tomatoes are created equal. Sure, Roma tomatoes will not taste like Beefsteak tomatoes. What matters more though is where I get the tomatoes from. The tomatoes from Safeway aren’t as good as the same type of tomatoes from Whole Foods; and these aren’t as good as the same type of tomatoes from Windmill Farms (they carry a lot of fresh produce from local farmers). The tomatoes on the vine at Windmill Farms are awesome and the same type of tomatoes from Safeway are barely okay, even though they look similar.

    Selecting people for your strategy—whether they’re full-time employees or consultants—is like selecting tomatoes. The talent differential between average, good, and great is sizable; and looks can be deceiving. It astounds me every time I come across someone from a big-name firm like McKinsey, Deloitte, or Accenture, who doesn’t know the difference between strategy and long-range planning. Or, an Executive Vice President with an MBA who cannot make a decision.

    One of the critical elements of executing a successful strategy, is making sure you have the right people on your team. Selection is crucial—you don’t want to end up with a bunch of sour tomatoes.

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    Posted by on in Leadership

    I had power issues again this weekend. No, I wasn’t suffering through one of San Ramon’s infamous blackouts, this time I was dealing with the power in my car—the battery was dead. And, unfortunately, the battery charger was no help this time, because the battery was completely dead—flatlined. This happened because nobody drives that car, and as you may know, a car that stays idle for too long will have performance problems when you call on it to work. The same is true for data scientists on your big data team—you must keep them busy solving important problems.

    First of all, data scientists are very expensive resources, so it’s just irresponsible to hire a few, just to have them watering the plants while you figure out what you want to do with them. More importantly, idle data scientists need to stay busy with challenging and exciting work, or they’ll lose enthusiasm for what you’re trying to accomplish. And if this period of inactivity is extended, it’s hard to engender urgency when it’s time to get serious.

    This is a bigger responsibility than you might expect. It’s common for me to see idle data scientists while the leadership struggles to get their plans in place. This is a very bad situation. Thoroughbred horses are bred to run, and if you don’t keep them moving, they’ll lose their edge. Data scientists are a particular breed of analyst—not unlike a thoroughbred. Some business analysts are okay with just a moderate amount of activity, but data scientists thrive on solving problems, and get distracted and demoralized when they don’t have a big problem to solve. In the same way you must keep a thoroughbred moving, or a car running, you must keep a data scientist analyzing, or they’ll lose their edge.

    The only thing left to do with my car this weekend was to call AAA and have them replace the battery. You don’t want to get into a situation where your big data’s power source needs to be replaced. Make sure they always have something important to do.

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    Posted by on in Information Exploitation

    Banks need an overhaul in their lending practices and I think big data can help. There’s little chance I can get a bank loan right now (not that I need one) even though I’m probably one of the lowest risks in the country. I say that because I emerged from the financial meltdown of 2008 without missing a single payment on anything: loans, credit cards, office rent—even the gardener got paid on time. Compare that to all the FICO superstars that collapsed after two months of no work. Consumer behavior is not easy to model, but if your business relies on it, you better be good at it.

    Banks lost a tremendous amount of money because they relied on dubious and ineffective scoring models and now they’re not sure who to lend to. This is bad news for banks—lending money is how they stay in business. I never understood why lending institutions—with all their core competence in analysis—would rely so heavily on FICO scores and lightweight scoring instruments. For instance, I can’t understand how two years of tax returns demonstrates your ability to pay on a 30-year mortgage; however, this still seems to be the gold standard for income verification. And don’t get me started on FICO; I’ve seen my credit score swing 121 points over the last five year period. First, they say I’m a very high risk—then they say I’m a very low risk. All the while, I really haven’t changed a bit.

    My advice for banks is to bring their core competence for understanding consumer behavior in-house and reinvent their lending model. Big data and predictive analytics are in a place right now where very sophisticated modeling can be done on consumer behavior. Throw away the arbitrary rules of thumb and forget about FICO—it’s not effective. And even if a new, fancy consumer behavior modeling company opened its doors, why would you outsource something that’s so important to your survival?

    Exigent innovation is painful; however, what’s the alternative? The good news for banks is that big data presents an opportunity to pull out of this mess. The question is whether they see it.

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