Our VP of Product Management said something to the effect of, “This is a great idea, Lee!”…and he couldn’t have been more supportive in his tone.
In September (or October) of 2010, at one of our quarterly marketing offsite meetings, I pitched an idea.
“This was the ugly part of the stats-vs.-tradition debate in baseball: Rather than a conversation about the best way to make baseball decisions, it had become an argument, in which it increasingly felt as if the purpose was to score points by humiliating one’s opponent. So far as I could tell, nobody had ever changed his mind about anything in an argument. Dale Carnegie was way ahead of me: ‘If you tell them they are wrong,’ he wrote, ‘do you make them want to agree with you? Never! For you have struck a direct blow at their intelligence, judgment, pride and self respect. That will make them want to strike back. But it will never make them want to change their minds. If you are going to prove anything, do it so subtly that no one will feel that you are doing it.'” (The Only Rule Is It Has To Work)
A while back, I told the story behind Sandvine’s most popular corporate video. The goal of that video was to show prospective customers – who already know what Sandvine does – why they should choose Sandvine as their solution vendor.
This time around, I figured I’d go behind-the-scenes of an entirely different kind of video: one that seeks to highlight and explain a technical differentiator.
“Nobody cares. And they are right not to care. A great reason for failing won’t preserve one dollar for your investors, won’t save one employee’s job, or get you one new customer. It especially won’t make you feel one bit better when you shut down your company and declare bankruptcy. All the mental energy you use to elaborate your misery would be far better used trying to find the one seemingly impossible way out of your current mess.” (The Hard Thing About Hard Things)
“All these activities in a well-run traditional organization offer incremental benefit for incremental effort. As long as we are executing the plan well, hard work yields results. However, these tools for product improvement do not work the same way for startups. If you are building the wrong thing, optimizing the product or its marketing will not yield significant results. A startup has to measure progress against a high bar: evidence that a sustainable business can be built around its products or services. That’s a standard that can be assessed only if a startup has made clear, tangible predictions ahead of time.” (The Lean Startup)
As luck would have it, I’d scored my first freelance gig by doing nothing and then having a five minute conversation. But here’s the twist: this industry was completely new to me, and the nature of the project and subject matter only had a passing resemblance to anything I’d addressed before.
A couple of weeks ago I did my first freelance gig; it was a neat experience, so I figured why not write a bit about it?
“Predictive models are, increasingly, the tools we will be relying on to run our institutions, deploy our resources, and manage our lives. But … these models are constructed not just from data but from the choices we make about which data to pay attention to – and which to leave out. These choices are not just about logistics, profits, and efficiency. They are fundamentally moral. If we back away from them and treat mathematical models as a neutral and inevitable force, like the weather or the tide, we abdicate our responsibility.” (Weapons of Math Destruction)
On the recommendation of my former colleague Dan Deeth, I spent this morning down at the Tannery listening to Deloitte’s Duncan Stewart (@dunstewart) prognosticate on Technology, Media, and Telecommunications.
Dan was unable to attend, as he was in Barcelona this week at Mobile World Congress, and he tells me that Duncan’s been a long-time reader of the Global Internet Phenomena reports that Dan and I wrote. So hey, why not make the fifteen-minute drive to Kitchener?
Coincidentally, the timing comes on the heels of several posts on this blog about the challenges of and best practices for prognostication, so I was in the right frame of mind – skeptical, but interested – for this event.
Of course, I took notes.
For those who don’t know, Masters of Scale is, “an original podcast hosted by LinkedIn Co-Founder and Greylock Partner Reid Hoffman showing how companies grow from zero to a gazillion. The series unfolds like a music-infused detective story as Hoffman tests his theories with famous founders. Masters of Scale is the first American media program to commit to a 50-50 gender balance for guests.”
[That last part isn’t expecially relevant to this post, but I felt that it was important to include, and kudos to the team for committing to that balance.]
I started listening to Masters of Scale at the strong recommendation of Dave Caputo; I’ve listened to a few episodes so far, and I have mixed feelings…which I explain below.
“In so many other high-stakes endeavors, forecasters are groping in the dark. They have no idea how good their forecasts could become. At best, they have vague hunches. More often than not, forecasts are made and then . . . nothing. Accuracy is seldom determined after the fact and is almost never done with sufficient regularity and rigor that conclusions can be drawn. The reason? Mostly it’s a demand-side problem: The consumers of forecasting – governments, business, and the public – don’t demand evidence of accuracy. So there is no measurement.” (Superforecasting)
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