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[…]
Category: Data Driven Culture
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[…]
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[…]
What a fantastic read from Camuffo, Cordova and Gambardella! If you haven’t read A Scientific Approach to Entrepreneurial Experimentation, you’re missing out. It’s a great read. And not only because it reinforces my own preexisting biases, but also because there are challenging bits in there. The core finding is “We find that entrepreneurs that behave like scientists perform better, pivot to a greater extent to a new idea, and do not dropout less than the control group in the early stages of the startup.” The authors focus on a key behaviour that scientists exhibit. A scientist has two types of skepticism – skepticism that something is true, and skepticism that something is not true. Those represent two types of error, helpfully[…]
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[…]
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:[…]
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[…]
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[…]
Jon Evans wrote a piece for Techcrunch entitled: After the end of the startup era. In it, Evans writes: We live in a new world now, and it favors the big, not the small. The pendulum has already begun to swing back. Big businesses and executives, rather than startups and entrepreneurs, will own the next decade; today’s graduates are much more likely to work for Mark Zuckerberg than follow in his footsteps. And, Because we’ve all lived through back-to-back massive worldwide hardware revolutions — the growth of the Internet, and the adoption of smartphones — we erroneously assume another one is around the corner, and once again, a few kids in a garage can write a little software to take[…]
The other I likened the process for taking apart a Job To Be Done to taking a part a lobster. There’s a very effective way to decompose any problem with enough energy. And then I watched The Founder on Netflix and admired the McDonald brothers using a classic technique in management science to refine a system on a tennis court. And I loved it. They really refined hamburger and frenched fry delivery. And then this morning I read that Andrew Ng in working on a new coursera course for AI. And I’m thankful for his initiative and optimism. Out of those three threads, this one post. The Assembly Line The assembly line was an American invention for Americans. It could[…]