Previously, I wrote about communication overhead in tech and the two cultures around it. Broadly, I perceive two broad camps: there are the shippers and there are the talkers. Shippers ship. Talkers talk, then ship. In this post I’ll describe three forms of written communication and how they link up with current cultural megatrends. There are those that can write instructions that a human can reliably compile and execute (management). There are those that can write instructions that an organization can reliably compile and execute (governance/policy). There are those that can write instructions that a computer can reliably compile and execute (development). There are instructions that can be typed that cause performance in people. Such parameters include the outcome, the instruction,[…]

There’s a tension between two modes of communication – comms by storytelling and comms by bullet point. They each have pros and cons. In this post, I’ll summarize the differences. There is no verdict. The bullet point Some speak in bullet points. They’re being the golden threes be’s: Be Brief. Be Brilliant. Be Gone. Their talks might as well be written in nouveau-valley font, with Serif. Brevity is valued; where hard problems command easy heuristics, and where ‘don’t make me think’ is the l’ordre du jour. If I had more time, I would have written less. That’s especially true in the Valley. It’s true of politicians in certain settings. It’s true on Adelaide and on King. Elevator pitches are a[…]

A tier one MSI topic focuses on how should quantitative methods and qualitative methods be combined to understand the total consumer experience. It’s an excellent topic. The two worlds aren’t natural complements. They have radically different systems of activities, tools, and methods, which in turn affects their own experiences, and how they see the world. However, if the stance is unified, in the form of understanding the total consumer experience, the sum of the two approaches produces such more. That focus creates the cohesion. Facts, Experience, and Anecdata Have you ever been asked how many people need to be in a focus group before their statements become statistically significant? It’s a pretty neat question. What are they really asking when they ask[…]

How do you, and those around you, deal with failure? Because if the answer is anything but “well” or “every fail has a lesson”, then progressive, iterative, experimentation and AB testing really, really isn’t for you. Go away. Stop reading. It’s not everyone. The way to build an experimentation and testing bench is entirely by setting sail for fail. How to build your Experimentation and Testing Bench Your bench will be built: 95% with culture 5% with technology The technology is well thought out and several vendors are excellent in this space. The technology isn’t a problem. Sure, there are a few pretty shockingly bad systems out there. Every industry has a phony. Building a testing bench begins with policy.[…]

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[…]

Discovering truth in data always begins with you, and your judgement. Assume that you have some idea about the world. Something that you believe is true, and you want to discover if you’re right. Here’s how I draw out that out. It becomes a matter of organizing a dataset along those thoughts. I call causal variables X1, X2, X3… I call the single variable that I’m trying to explain the Y variable. There can be only one Y variable. For your own sanity, there can only be one Y variable at a time. There are a large number of tasks to figure out if X1, X2, X3 cause Y. One of them is to run any one of the many[…]

A strategy is a set of choices that, when combined, cause a sustainable competitive advantage. Conscious, reinforcing, choices, are powerful. That’s what you learned in B-school. I’m far more pessimistic that strategic choices are generally conscious. I’ll explain. A set of deliberate choices, that constitute a strategy, might be: Because we chose the same aircraft we save money on maintenance. Because we chose the same aircraft we save money on ticketing. Because we chose the same aircraft we compete exceptionally well on specific flight pairs. Because we chose a large set of direct point-to-point flights without going through hubs, we save money on baggage transfer. Because we simplify baggage, we can turn planes around more reliably. Because we turn planes around[…]

Planning is preparation of the mind. It’s impossible to quantify every variable, every assumption, and every potential future state. Attempting to do so will simply boil the ocean and frustrate everybody around you. Analytics leaders tend to be very specific types of folk. Here are a few heuristics that might be useful for us in particular. Backcasting Backcasting is primarily an expression of preferences. The exercise almost always begins with an enunciation of a preferred, desirable, future state. Consider the following statement: “By 2016, we will be a 1 billion dollar company.” Such a statement, be it vision statements, stretch goals, or just goals, are typically not based on any sort of forecast. It’s entirely possible, and very likely, that[…]

knowledge in relation to knowing what you don't know

A charrette is an intense, collaborative session, that enables designers to draft a solution to a very complex problem. It’s a technique first used by artists. Then designers picked it up. And then later still, urban planners. And then a few brave souls wisely invited stakeholders in on the process. Finally, this approach would evolve into software development and web development. It is very applicable to solving analytical problems. First, consider the natural law below.                       In analytics, the proportion of what we don’t know always grows as more knowledge is added. The more imaginative the analyst, the steeper the curve. Get three or more analysts into a room together[…]