Category: Data Science

The Tools That Shape Us

Posted on by Christopher Berry

Paul Graham wrote on May 9:  “Observation suggests that people are switching to using ChatGPT to write things for them with almost indecent haste. Most people hate to write as much as they hate math. Way more than admit it. Within a year the median piece of writing could be by AI. … I warn you now, this is going to have unfortunate consequences, just as switching to living in suburbia and driving everywhere did. When you lose the ability to write, you also lose some of your ability to think. I don’t have the slightest hope of averting this switch. I often tell startups it’s safe to bet on laziness, and this is one of the biggest bets on[…]

On Synthetic Media, Synthetic Audiences, And Facts

Posted on by Christopher Berry

Generative Pre-trained Transformers (GPTs) have captured the public’s imagination. There’s a lot of fear. The relevance of that fear, as always, depends on who you are. The technology first caused a surge of panic in December 2022 among some communications professionals. They fear a surge of supply, a massive increase in synthetic media, along with all of the misinformation that goes along with it. Because attention is inelastic, the price for content will collapse, and it’ll take their wages down with it. They aren’t the only ones likely to be affected [1]. It’s kind of curious that you aren’t reading too much publicly from developers and their experiences with the technology. GPT’s are a tool. A good GPT is capable[…]

The Distinctions Among Information Creation, Transformation, Gathering, and Transmission and What It Means For Data Science

Posted on by Christopher Berry

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[…]

The meaning of skate to where the puck is going, not where it as been

Posted on by Christopher Berry

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[…]

Deep Learning: Evolution or Revolution?

Posted on by Christopher Berry

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[…]

Product Managing Data Science

Posted on by Christopher Berry

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,[…]

The Technology of Productive Meetings

Posted on by Christopher Berry

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[…]

Predicting Technology Triggers

Posted on by Christopher Berry

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[…]

Prioritizing Knowledge

Posted on by Christopher Berry

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[…]

Behold the awesome power of homophily

Posted on by Christopher Berry

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,[…]