For the purposes of this post, let information mean stuff that resolves uncertainty. Different people have different relationships with information in part because they have different relationships with uncertainty.  Data scientists, those who turn data into product (Loukides), have a different relationship with information than, say, the median store manager or the median politician. Some data scientists focus on creating new information. Some invent new sensors. Some deploy existing sensors to new places. Some combine sensors in different ways. They have very good reasons for doing that.  They aren’t the only people who deploy sensors. Camera operators deploy sensors at certain times to capture sounds and images. Some data scientists create new information using nothing but their minds and their[…]

Walter Gretzky is credited with the quote: “Go to where the puck is going, not where it has been.” Walter used socratic questioning to teach his son, Wayne, hockey strategy. Here’s the full context from Wayne’s perspective: Him: “Where do you skate?” Me: “To where the puck is going, not where it’s been.” Him: “Where’s the last place a guy looks before he passes it?” Me: “The guy he’s passing to.” Him: “Which means…” Me: “Get over there and intercept it.” Him: “If you get cut off, what are you gonna do?” Me: “Peel.” Him: “Which way?” Me: “Away from the guy, not towards him.” (Gretzy, Reilly, Gretzky: An Autobiography p. 88) Puck On To win a game of ice[…]

Is what is happening in analytics, in industry, an evolution or a revolution? What is Analytics is the science of data analysis. Those who practice analytics self-identify as analyst, digital analyst, marketing scientist, data engineer, researcher, among many others. Tukey (1962, The Future of Data Analysis, The Annals of Mathematical Statistics, (33), 1) called them all practitioners. The goal of the practitioner depends on their context. That context largely, but not always, depends on the state of knowledge, state of the culture, or sometimes, normatively, the state of maturity, of the group they belong to. Large organizations can have a large amount of difference within them. It’s not uncommon for an operations department to be extremely mature and for its[…]

In this post, I’ll outline some of the best parts about product managing data science. Data science is the creation of product from data, requiring a blend of the skills of technology, statistics, and business. Product Management brings and keeps product in the world, requiring a blend of the skills of technology, user experience, and business. All of the challenges of product management appear in data science. And then some. The Knowledge Funnel The Knowledge Funnel is a concept introduced by Roger Martin in Design of Business: Why Design Thinking is the Next Competitive Advantage (2009). At the top of the funnel, you got mysteries. It would seem that there are an uncountable number of mysteries. In the middle, you have heuristics,[…]

Can meetings be more productive? The BBC’s Sean Coughlan wrote a piece entitled “Pointless work meetings really a form of therapy” and it struck a chord. I shared that out on Friday, November 15, 2019. It’s a short press summary of what Patrik Hall co-authored in a book. The press doesn’t say what that book is. So I wrote Patrik. The book is called Mötesboken : tolkningar av arbetslivets sammanträden och rosévinsmingel. His co-author, Malin Akerstrom, wrote a related paper – The Merry Go Round of Meetings: Embracing Meetings in a Swedish Youth Care Project. It is worth a read. I have a few thoughts. There are (at least) two forms of technology: physical technology and social technology. Physical technology[…]

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[…]