In this post: Data Driven Cultures in startups should discover product-solution-market fit more reliably than Ego Driven Cultures Data Driven Cultures Carl Anderson, 2015 (Data Scientist at Warby Parker) defines a data driven culture as: Is continuously testing; Has a continuous improvement mindset; Is involved in predictive modeling and model improvement; Chooses among actions using a suite of weighted variables; Has a culture where decision makers take notice of key findings, trust them, and act upon them; Uses data to help inform and influence strategy. Startups A startup is defined as an experiment looking for a problem-solution-market fit. The goal of a startup is to become a business. To do that, it must discover a market, a subset of people[…]
Category: Analytics Strategy
Consider the statement: The strategies generated by data driven cultures are likely to produce sustainable competitive advantages. Data Driven Cultures Carl Anderson, 2015 (Data Scientist at Warby Parker) defines a data driven culture: Is continuously testing; Has a continuous improvement mindset; Is involved in predictive modeling and model improvement; Chooses among actions using a suite of weighted variables; Has a culture where decision makers take notice of key findings, trust them, and act upon them; Uses data to help inform and influence strategy. Strategy For the purposes of brevity, I’ll define a strategy as: An artifact; That enunciates choices selected from acknowledged tradeoffs; Which is rooted in a paradigm; That is actionable; With the intent of causing a sustainable competitive advantage in[…]
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[…]
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[…]
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.[…]
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[…]