The main point is that it’s worth trying to predict technology triggers and asking what those triggers mean. There is value in answering the question so what? A secondary argument is that questions beginning with what if? can be very interesting, but far less reliable than so what? What is a Technology Trigger? The term Technology Trigger is from Gartner’s Hype Cycle. They defined it as: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven. Gartner Research The term has been deprecated in favour of the term innovation trigger. However, as an owner of the hardcover book Managing The Hype Cycle (2008), I[…]

The inspiration for this post is John Cutler‘s excellent twitter thread on prioritization. It’s well worth the read. This post builds on that inspiration using Roger Martin’s concept of the The Knowledge Funnel. One big takeaway of John Cutler’s thread is when deciding the sequence of what to do in product management, consider the big picture and think of the impact of what you will do next on what you will know next. What I like about Roger Martin’s concept on knowledge funnels: consider the big picture and think of what you know about value. Product management and data science is all about managing the knowledge funnel. Your ability to manage this funnel is predictive your ability, and those you[…]

There are many calls to break up tech. Break up what, exactly? Regulate tech? Regulate what? There’s a lot of polarization about what to do about Facebook, Amazon, Apple, and Google. That polarization is in part driven by anger. Dig a bit deeper and see fear. Maybe you’re feeling it. Here’s how I see it. The Assumptions People are heterogenous. Peoples’ beliefs are heterogenous. Peoples’ willingness to believe are heterogenous. Peoples’ inventiveness and imagination are heterogenous. Peoples’ willingness to tell or repeat stories are heterogenous. Peoples’ susceptibility to stories, and to storytellers, are heterogenous. Peoples’ need to belong are heterogenous. People form networks because they need to belong. Information (Gossip, facts, stories) is transmitted along those networks. These variables (information,[…]

It seems like a lot of people value certainty. People buy a lot of products and stories for certainty. Insurance. Investment advice. Forecasts. Indulgences.Many entrepreneurs, in particular those in data science, sell certainty. What else is an F1 score other than a measure of certainty on some level? Given some inputs, our machine transforms them some way, which produces some statement about the past, present, or future, with some quantifiable amount of certainty, so that you can do something with confidence (or feel more secure). We sell certainty. And yet isn’t it curious about how much insecurity we’re creating while we do so? It has always been easier to sample data from the past, pull a heuristic from it, and[…]

You build three machines when you build a startup. Your ability to build these three machines is the Great Filter to your life in the business universe. This post is an effort to describe why some startups fail, why some are small, and why others grow big. The Great Filter The Great Filter refers to a concept that Robin Hanson came up with to explain why we don’t see any evidence of intelligent life in the Universe. One can get a better sense of different scenarios when one considers how many things need to be true for intelligence to emerge, and assigns probability to them. If it’s the case that the coincidences required for life to occur are exceptionally rare, then[…]

My contemporaneous notes from a particular INFORMS Marketing Science Conference six years ago feature the letters W, T, and F scrawled in the margins a few times. I learned of a deeper problem lurking in the way we were using the crosstab to identify segmentation. In this post, I’ll unpack a heap of jargon and lay the concern bare. To the twenty or so marketing scientists in the room at the time, I read concern on the faces of about a dozen. It was a atypical because typically that community doesn’t get concerned about too much. One leader remarked that most in industry were not even executing basic segmentation on their users, so it wasn’t a huge industrial concern, but[…]

Teams, in software engineering, form because of success. Without success, the firm wouldn’t be cursed with the problem of having so much talent to have to organize in some way. A founder can easily reduce the complexity in their human organization, and their lives, by simply not hiring any more than seven technologists to work with them on their mission. For some, this is viable. For others, this is not. Teams emerge in response to scale. They are either formed as by product of centralized hierarchical command structure, or they emerge as a product of network cohesion/polarization. To the extent that either formation is aligned with the vision, goal, mission, or purpose of the organizational chrome is a function of[…]

Imagine with me: what if novels were written like software. Sometimes it’s useful to approach absurdity and look inside. There might be treasure there. I’ll define software as an executable, a set of instructions, that are interpreted by a machine for some reason. As a data scientist, I think of software as a product, and I think, constantly, of turning data into product. I think of data as inertia and all the code around it as flexible. I worry a lot about the people that use the software (if anybody) and think of them as heterogenous segments. I think of a novel as an executable, a set of instructions, that are interpreted by a human brain for some reason. As[…]

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[…]