This is a dense post. Feldman and March, in 1981, wrote “Information in Organizations as Signal and Symbol”. And it makes good predictions about what a management scientist type would say about the purpose of information in an organization. Indeed, just last month, I hyped Carl Anderson’s 2015 original position yet again, in the framing of information as assisting learning. Feldman and March are cited by another piece that’s been weighing heavily since February. Alvesson and Spicer’s 2012 hit “A Stupidity-Based Theory of Organizations” explains why seemingly intelligent people pretend to be dumber than they are. Please don’t misinterpret this passage. It’s not the case that everybody is stupid. Sometimes people act dumber because they have to go-along-to-get-along. Are you[…]

This post describes a fast follow startup and the implication for how that startup learns. Define Startup A startup is a market hypothesis looking for validation. It’s an organization in search of a business. If they’ve accepted funding, then it’s a group of people looking for a liquidity event. Define Follow Follow means imitation. It means that an entrepreneur or a herd entrepreneurs have been observed pursuing a particular product-solution-market fit, or a hypothesis, and some founder wants to join the herd. Define Fast Fast means that the organization is imitating fast enough to nip at the heals of the lead innovator. It is imitating fast enough to be contention of overtaking the leader, or close enough to experience a[…]

Data scientists spend so much time focused on learning: both machine learning and human learning. A machine can learn. A data scientist spends a lot of time just trying to persuade a machine to learn. It just takes a lot of labelled data. What about collections of people? Organizations can learn too. It’s just that the data isn’t all labelled well. Why Organizational Learning is Important I was so impressed with Carl Anderson’s synthesis two years ago, about Data Driven Cultures, that I unpacked it and¬†applied it to startups and strategy. Coming back to it now, in 2018, a lot of what he was saying is purely about learning. Carl Anderson, 2015, described a data driven culture as on that:[…]

Who do you trust to manage your attention? Because now that the news cycle has surfaced Cambridge Analytica issue¬†– that’s the real thesis question. Let me explain. How the Newsfeed manages your attention I really can’t understate just how powerful amplified engagement really is. When you overlay the like/share verbs on top of a network of individuals who all have something in common, or who procure people who have something in common, you get some pretty strong effects. Don’t believe me? Just check out the clothing in your drawers and the items in your fridge. You, my friend, are an outcome of considerable social contagion effects. Facebook’s newsfeed algorithm shelters you from a power law distribution of content that the[…]

There’s a quote from The Office (US) [Season 6, episodes 5/6, “Launch Party”]: Michael: Okay, okay, what’s better? A medium amount of good pizza? Or all you can eat of pretty good pizza? All: Medium amount of good pizza. Kevin: Oh no, it’s bad. It’s real bad. It’s like eating a hot circle of garbage. The launch in that episode was the ill fated “Dunder Mifflin Infinity”, and while the reference in the passage is to the pizza that Michael Scott had ordered, it may as well been referring to the website. For many reasons, people tend to build all you can eat hot circles of garbage, instead of a medium amount of pretty good pizza. Minimum Viable Product and[…]

Do you like new technology? Chances are that if you’re reading this space, you do. I like new technology too. I don’t like hype as much. I get suspicious when people go out of their way to inflate expectations deliberately in advance of a promise that they know, full well, it can’t deliver. Whether you’re buying for yourself, your home, or your organization, you want to invest in technology that’s likely to have a return, but not such a diminished return that you derive absolutely no competitive advantage or learning from it. There’s a balance there between the fear of losing too much and the greed of unfair advantage. To understand why these feeling develop, it helps to understand why[…]

You may have been to a conference. Ever wonder why they’re the way they are? The Conference Market(s) Different people hire a conference to do different jobs. For some, a conference is a chance to learn, be exposed to new ideas, and exit a comfort zone. Or, to enter a comfort zone to be exposed to new ideas and feel safe enough to learn. For some, it’s entirely about networking with colleagues, or recruiting, or to be recruited. For others, a conference is a chance to spam people with signing authority with their marketing messages. Or to upsell. Or to crossell. Or to retain. For others still, a conference is a reason to visit a city. To get the hell[…]

I was 28 and sleepless when I encountered a marketing version of the logistic function. It was beautiful. It’s one of those things you’re taught about in one context, and when you’re shown it from another angle, it expands your mind. It was like discovering Pi for the first time. I could use it to check the assumptions of a market penetration forecast, and substitute my own estimates for others. I felt empowered and delirious from being able to produce a solid forecast. It became a tool as useful as btau or the crosstab. There’s a part of that math, a variable called saturation, that worried me from the outset. Saturation is the maximum percentage of adoption that a market[…]

It was a treat to see these three – Yoshua Bengio, Yann Lecun, and Geoffrey Hinton – for an afternoon. Easily the best three consecutive hours I’ve ever seen at a conference. They remarked that Canada continues to invest in primary research. And this is a strength. Much of the exploratory work these three executed in the 80’s, 90’s and naughties was foundational to industrial applications which came after. Much of reinforcement and deep learning has moved on into industrial application. For the three grandfathers of deep learning, all of these algorithms and methods move into the realm of solved problems. For those of us in industry, there remains a lot of work to realize the benefits of deep learning.[…]

Some people want just one number. Some people want all the numbers. For best results, seek balance. One Number It is very possible to summarize the performance of a business or an organization in a single number. There are two main ways to do so. One is selection. One is indexing. In selection, you pick the most important metric, and you focus on it. It requires discipline and comes at the cost of myopia. In indexing, you pick the most salient metrics and you combine them into a single number. It requires no discipline and comes at the cost of boiling the ocean to the point that all the rocks bleed their salts into the atmosphere. When it comes to[…]