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[…]

The Knowledge Funnel is a concept introduced by Roger Martin in Design of Business: Why Design Thinking is the Next Competitive Advantage (2009). The book is excellent and worth a read. There are mysteries at the top of the funnel. Mysteries are the unknown. They’re the known unknowns and the unknown unknowns. It’s knowledge that the organization doesn’t have. In the centre of the funnel, you have heuristics. These are rules of thumb. They aren’t quite always precise, and aren’t always quite reliable. Heuristics are just predictive enough to be useful. It’s knowledge that is known to the firm. At the bottom of the funnel you have algorithms. This is knowledge that is standardized and optimized enough to be run[…]

On the walk in, I thought of Livy, of networks, and of hierarchies. I thought of Samuel Doe, the Liberian that brought an end to the brutal True Whig Party and brought in his own brutal regime, and of Emperor Bokassa, who once spent a third of his nations budget on his coronation. They each grew up physically, yet never had a chance to develop fully as whole people. And because they were leaders, the societies they led never got the chance to develop either. Are systems really that sensitive to leadership? Can it really be that institutions are that sensitive to the development of their leaders? The thought rocked me for a few minutes, and then subsided into an[…]

Suppose the following scenario: Series A or B; A data science firm (narrow machine intelligence, applied machine intelligence, general machine intelligence, predictive or prescriptive analytics, software or hardware); Technical CEO / Co-Founder; Chief Marketing Officer (CMO) just hired; What might the CEO-CMO relationship look like? The relationship could be great. If there’s one stereotype about data science CEO’s, it’s that they like incentives to be aligned. The CMO would likely be brought on to focus on growth. If revenue grows, valuation grows, and collective comp would grow. There might be points of friction. From the CMO’s Perspective: Why is the CEO constantly at me about metrics all the time? Why is the CEO always on about non-working dollars? (Why don’t[…]

Previously, I asked what kind of leader you wanted to be. In it, I struggled with the question of the tradeoffs of misrepresenting ground truth. Suppose you work at the WWE and you need to make a business decision. Somebody literally believes that it’s all real. You need to make a business decision about a contract renewal. What kind of harm are you doing to them, and to yourself, in going along with their belief, something that you know yourself not to be true? Absurd? Yes. Beliefs are absurd things. Later that month, I was asked why I was so sure that WWE wasn’t real? Why was my version of ground truth any more legitimate than somebody else’s ground truth?[…]

What causes conversion? Demand. It’s a simple answer and worthy of unpacking.¬† You could thank Claude C. Hopkins for the simple answer. Hopkins wrote two books towards the end of his life – Scientific Advertising and My Life In Advertising. He seemed to regret his experiences as an agency president, and left some direct advice on how master marketers should think of their choices. In his last decade of life, Hopkins marketed his marketing expertise. Instead of continuing to take on all the risk of marketing product on behalf of somebody else (and maybe getting paid if the product sold), he set up a system where products would be pitched to him. If the product was good, he’d take a[…]

The whole thing, all of it, depends on optimism. Optimistic expectation is a natural force generated by humans and amplified by the networks that humans create. At the core of the entire traditional liberal paradigm, since the enlightenment, is the expectation that things will be better in the future. If things get better at a rate of just 2% per year, compounded annually, things get twice as good in just 35 years. If things get better at just a little bit more than that, 3.6%, things get twice as good in just 20 years. We’ve come to expect things to become better, dependably, and predictably. The enlightenment is an important event to call out. It’s way easier to shrug ones[…]

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[…]

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[…]