In this post, I will unpack the concept of Convenient Reasoning and link it to managerial judgement and the spirited defense of Gut. I really haven’t challenged these assumptions in a few months, so, if you dislike what you read, give me a shout. I’ll spend too much time over the next 45 days repeating the orthodox line of scientific management and continuous learning in digital. It’ll be a great opportunity to unpack some language and really tone down the information density. Gut Just as I translate the word ‘leverage’ into ‘use’, I translate the word ‘gut’ into ‘my feelings about expectations’. Or, put more derisively, ‘muh feelz’. I’m indebted to James G. March for highlighting the difference between expectations,[…]
Tag: opinion
Analytics in 2014. What a year. We hit peak Data Science hype in October. We hit peak Data Science sometime over the summer. This has a few important impacts for 2015. The end of that hype will make it harder for the majors to sell binders of plans. It’ll be tougher to find optimistic customers. It’ll be rough going for some of the weaker offers on the market to fake it long enough to make it. It’ll sort out the ‘transformational change’ shops from the technical shops far more slowly. Markets aren’t nearly as efficient as they should be. It usually takes 180 days for the peak to bite and 270 days for the money to run out. It’s really[…]
There are three important, reinforcing concepts in analytics product development. These are usability, numeracy, and empowerment. Usability is an important goal to pursue in analytics product development, but is no antidote for poor numeracy and empowerment. Usability is particularly important for analytics product development. Good usability enables the non-specialist, the data civilian, or the casual business user to engage the product and extract the information they need to know. Some interfaces require specialized training to use (SAS, R, SPSS) while others used to require little experience (Google Analytics pre-2008, OWA today). Several companies have gone to IPO with only marginal improvements to baseline analytics usability. Some companies started out with usability as a key differentiator, only to fail to manage simplicity with[…]
Two big announcements – HBO and CBS, two major media companies that create original content, will both offer OTT streaming services. Consumers won’t need a cable subscription to get either of them. Sports are excluded of the service. More on that below. As a Canadian, it’s even more interesting because the CRTC has been holding hearings on another consumer friendly initiative, Pick-And-Pay. It’s pleasing to see HBO and CBS work at offering audiences the entertainment they want, and how they want it. It’s the beginning of the flip from a content-centric to a consumer-centric paradigm. And that’s a lot deeper than just a set of buzzwords. It manifests itself in the activities at the media company. I was impressed with[…]
Here’s what I think. I don’t like going to a presentation where I’m shilled at for 40 minutes. I don’t like being told about why a product is the best. I hate the saccharine story telling from the biz dev guy even more than the unenthusiastic “I am so tired of flying” delivery. They hate giving them. I hate listening to them. I think we’re aligned on that front. I don’t like going to a presentation and getting pandered at for 40 minutes. I hate it even more than the commercials. And you know the ones. They get up there and mouth platitudes and buzzwords. Stuff they know you want to hear purely because that correlates to an easy 4.5/5[…]
Data Civilians. Monica Rigoti used that terrific term in a New York Times Big Data piece. And the term resonated. It’s common to think of Big Data in much the same ways as nuclear research. Everybody wants the bomb. Yet, data comes out of the ground in a raw ore. The ore has to be mixed different chemicals to create various salts. Then it has to be shoved into huge centrifuges. These enormous processes are used to separate the slightly heavier bits of data from the slightly lighter ones – a process that’s important if you don’t want to contaminate the earth with dirty bias. It has to be milled into a sphere or sometimes an ingot. And then surrounded[…]
Consider a list of metrics. Now pick the Y, the dependent variable, from that list. A human would use their judgement. A machine would use an algorithm. It’s clear that human judgement varies. There’s evidence that it does. Causing a machine to make accurate predictions about human judgement is an interesting problem because of the inherent variation of judgement within human populations. This is a polite way of saying that some people really diverge from the median in their application of judgement. Consider the following question: If given a table of Gross Profit, Sales, COGS, Marketing Spend, Working Dollars, Non-Working dollars, Discount Cost, Impressions, Paid Media Impressions, Paid Media CTR, UV, V, and PV, which is the dependent variable? The[…]
Here’s what you need to know about automated statistical analysis: 1. Automated statistical analysis is not a substitute for good judgement Statistical tests are tools. They help us understand why nature is the way that it is. Nature resists being known about. But, she is knowable. Statistical tests themselves are part of nature. The tests themselves were never meant to be substitutes for good judgement. That belief, that tests could replace people, has only ended up causing the accumulation of some pretty outrageous assumptions over the years. Just because there is a significant correlation between Magnum Ice Cream sales and Piracy in the Indian Ocean doesn’t mean that it’s causal. Statements of causality require judgement. Automated statistical analysis is not[…]
Let’s take a look at what 16-bit interfaces could do. A great simulation game begins with just a handful degrees of freedom and explodes from there. Behold the grandeur that is SimCity for the Super Nintendo. If you’re familiar with SimCity (1991), skip ahead to Data Exploration, below. On a flat plane of pixels, you have the choice to: Bulldoze a feature. Build a road. Build a mass transit unit. Build a power line. Build a park. Build a residential zone. Build a commercial zone. Build an industrial zone. Build a police department. Build a fire department. Build a stadium. Build a port. Build a coal plant. Build a nuclear plant. Build an airport. Build a special reward building.[…]
The full New York Times Innovation Report was leaked last week. It’s worth reading if only because it lets you look at a paradigm – an entire way of thinking, laden with it’s own explanations of culture, causal factors, jargon, assumptions, myths, systems, and heretics. It enumerates the preferences and aspirations of a small group of people (including their preferred org-chart re-org!) and highlights a long-standing tension between technologists and journalists. It may also serve as a wake-up call that continuous improvement and scientific management is already a reality at several disrupting media startups. Let’s begin. Summary if you didn’t read it (and won’t): The document contains 97 pages. The term “Competitor” is mentioned on 39 of those pages. Analytics[…]