Data Science is the mix of computer science, user experience, and statistics. The aim of data science should be: to make things better by influencing people and things to make better decisions, by making people and things more aware of better alternatives, based on better algorithms and more relevant data. Language kept intentionally vague to set up the ‘well that could be anything’ argument when it suits me later. If you do it right, nobody is really aware of the complexity of what just happened to them. The point is not to experience data. The point is to experience…an experience. And be better off for it! And, the most interesting part is that it’s not really driven by humans with[…]

How consumers are using mobile to shop IRL (In Real Life) is of paramount interest now that mobile has finally arrived. A few figures to run through. The first, below, describes what consumers report they want from mobile phone applications, for the holidays, in August 2011. A common behavior, well known to clicks-and-bricks retailers, is that consumers will research products before coming in store to buy them. This is especially true of electronics goods, but I suppose it’s conceivable they do it for home appliances, automotive purchases, and anything else that is generally of high consideration. Mobile offers the capability of researching while you’re physically in the store. And, since most stores are now ghost towns, it enables the consumer[…]

Web Analytics Wednesday is tonight at The Wellington, in downtown Toronto’s analytics alley. It’s generously supported by AT Internet. There are some 40 people – representing among the best of the best, who will be in attendance. It’s a great opportunity for web analysts, social analysts, marketing scientists, data scientists, hackers, developers, and usability professionals to come out and talk about the great ideas and opportunities we have going on in Toronto. It’s also the first get together after eMetrics New York, which was a major, and had big time Canadian attendance. These tend to be among the more interesting evenings. It has also been some three months since the last WAWTO event, so there should be quite a few[…]

Not all data is usable on its own. The vast majority of it isn’t in its raw form. Its coal. It has potential. But on its own, it has limited uses. Algorithms are the modern day equivalent to machinery. Fire (combustion) is really just statistical analysis – a violent process that generates waste in the form of heat and soot. Our modern day Watt Pump is Google. Their coal is HTML. The best coal used to be the HREF link. The algorithm that drives Google’s primary product is PageRank. It runs on a massive amount of coal. Most people aren’t aware of the complexity that goes on – and why should they. All the mine owners really cared about was[…]

The eMetrics NYC conference / data driven business week, is said to have attracted some 1600 people. The WAA Industry meeting coincided with it. It was a success. Three major takeaways that stick out in my mind. Janus Faces for Janus Audiences I’ve now seen two versions of Peter Fader. The academic Fader and the industry Fader. The one you see at an INFORMS Marketing Science conference is the academic Fader. He’s the kind of guy that’ll smile as he tells his skeptical detractors to take their perspectives and reconsider them. His insights into models and the way theories have been constructed are original. He doesn’t mess around with the bits at the edges. He’s good at the comparative method[…]

I wrote a definition of ‘Insight’ on December 29, 2010 that read: “An insight is: New information Executable Causes action Profitable Or, more detailed, an insight is: A piece of information that you didn’t know before, which – Can feasibly executed, culturally acceptable and of a scale relevant to the firm, and – Causes a decision to be made that wouldn’t have been made otherwise, and – Results in profit or a sustainable competitive advantage” If held to that standard, insights are incredibly rare. Depending on who you talk too, an insight may mean: A bullet point factoid An element that combines all that is known about a segment, compressed into the foundation what motivates and inspires them A simple[…]

I’ll be at eMetrics next week. I hope you will be too. It’ll be great to be back in New York. There are a few people that I’m looking forward to seeing: John Lovett on social media, Melinda Driscoll on web analytics, Shari Cleary on media, Joseph Stanhope on mobile, Alex Langshur on government. And then there’s Michael Healy, Patrick Glinski, and me. I’m presenting with Michael Healy on sentiment. Michael Healy is among the best thinkers in this space and is just great. There have been a few very recent breakthroughs in sentiment analysis over the summer (and as recently as last week), and I’m looking forward to explaining how to treat the measure. I understand a core problem[…]

We’re living in the most measured era in history. Are you the beneficiary of any of the data you’re generating? You optimize what you measure. One of the most data intensive self-improvement projects I undertook was in 2005. I recorded everything I ate and every exercise I did. And did I ever optimize – to the point where my joints couldn’t keep up with the muscle and bone growth. It was a massive amount of work to record all that detail, the weights of various things and then to cross reference with the USDA database. Then it all had to get loaded into SPSS for analysis. It was brutally time intensive. But it did generate incredible evidence-based insights about the[…]

Have you seen this site, put out by Google for their “Our Mobile Planet” study? It’s an excellent way to present data in a very accessible, very explorable way. I found it inspiring. The call to action is “create your chart now”. A very good, honest, call to action. The technology adoption S-curve can be a slow beast, and expectations of growth have persistently outstripped actual adoption, at least in North America, and especially in Canada. Adoption has a few drags on it in North America and Europe. No such drags exist in Asia. The chart below compares all the countries smartphone penetration. (Click to embiggen) That chart masks underlining maturity in each country. The chart below compares m-commerce ‘at[…]

This is a pretty good summary of the definition of data science. Some statisticians seem to be incensed. Some people say that this whole thing is invented as an O’Reilly buzzword. And there’s consternation, fear probably, over the devaluation of actual craft. Sound familiar? Ah, the great Web Analytics debate of 2007. Yes. We’ve seen this. Nothing like a fresh Gartner Hype Cycle in the morning, is there? But lets consider what technology is causing, and the role that data scientist will play, in driving that cause. Accessibility to data is expanding. What used to be the jealously guarded by people who didn’t want to be educators, is now liberally spread. It doesn’t really matter that most people don’t know[…]