“The future belongs to the companies and people that turn data into products.”– Mike Loukides, O’Reilly Radar, June 2010. One of my favourite thinkers, Mike Loukides, repeated and expanded on that today. And a good thing too. It’s not as though product development alone is easy. Even when armed with the tears of hundreds of thousands of developers as a reference guide, you’re bound to contribute several of your own to the corpus. Google Search is the most familiar example of turning data into product. And it has a few effects you know about. We all know several dozens of people who can explain SEO in high detail. I know only four people who can describe the mechanic behind PageRank[…]
Darryl Ohrt consistently produces very divergent and relevant content. I’ve been visiting his blog regularly for about a year, and it’s pretty awesome. He founded an agency, Humongo, some 15 years ago. He recently left. He wrote about it in AdAge. What caught my eye were four questions he asks himself: “For me, the ultimate decision came down to a few factors. I found that I couldn’t answer “yes” to all of these questions: Are you doing something that scares you nearly every day? Are you having fun every day? Are you proud of what you’ve accomplished at the end of the day? Are you consistently learning something new?” These are pretty good criterion. The first criterion is about comfort[…]
Analytics is alive and growing in Toronto. This post summarizes what I know I know. If you define analytics as being ‘the scientific method applied to data to generate sustainable advantage’, then there are three major concentrations of practitioners: finance, marketing and operations. The financial sector breaks out into the risk management and the speculation fields. There’s a higher self-referential graph amongst the risk management people in insurance than there are on the banking side. The speculation analytics folks are at severe disadvantage against their New York counterparts. If there’s a thriving hedge fund section in our community, I don’t know about it. Marketing is divided between startups, CRM vendors, agencies, and client side (which includes data mining and web[…]
S-Curves are awesome. They’re ultra-reliable. They’re among the best tools in social analytics that you’re not using. They’re not really accessible. The math is particularly ugly/beautiful. That’s why I’m excited about Google correlates draw function: Anybody can draw this super powerful curve and explore. Check it out. The first step is to draw an S-Curve. I drew one below. I’m looking for a technology that took a few years to remain in innovator mode, had hyper growth begin in 2007, and then took about a year to reach saturation. Google Correlate found it. It’s CakePHP. What about another technology, say, one that was adopted and then fell out of use the same way it came in. Draw out the[…]
I’m using BuzzData. It’s pretty awesome. From their own description of what it is: Data should be free-flowing, well-organized and easy to share. Wouldn’t it be nice if there was a place where you could store, share and show off your data with just a couple of mouse clicks? BuzzData lets you publish your data in a smarter, easier way. Instead of juggling versions and overwriting files, use BuzzData and enjoy a social network designed for data. Keep data simple. Use BuzzData.” What’s quite remarkable is the combination of technologies used to solve a very real problem. It’s important for marketing scientists, analysts, data scientists, and technologists alike to exchange real data and replicatable proof that things are the way[…]
Two trends, an exponential increase in data produced, and a linear increase in the number of analysts produced per quarter, continue pose a massive challenge to businesses and analytics practices alike. We need both physical technology and social technology to practice analytics at scale. There are three grouping of physical technologies: First, there’s instrumentation technology that we use to measure and record the world around us. Second, there’s analysis technology that we use to understand the data that’s coming. Third, there’s presentation technology that we use to communicate a world view, and what to do next. On the instrumentation technology side, we’ve all had a few challenges with instrumentation as of late. Specifically, the understanding of definitions, their impacts,[…]
How will we measure attention when you can play an app on your TV? It’s coming, and the future won’t be the walled garden offered by WebTV. TV came to the Internet through YouTube and Hulu. Now the Internet will go to the TV through open boxes, possibly a new device that completely blurs the line between a computer, a set top box, and a tablet. The digital medium has the ability to capture ‘the event’, and it’s through these events that we create spectacular pictures of how people consume media. If I’m generating events through an App while watching TV, what’s the attribution model for TV? Will broadcasters let go of their methods for measuring attention? No. From their[…]
Eric Peterson, who some of you may recall from interview questions and repeated WAW discussions, asked: “What do you think? Is web analytics hard because the tools are hard to use?” And this thread really got going. Check it out. This is all fairly predictable, and it always ends in detente. You may recall a lot of annoyance within the web analytics community back in late 2009. There was one in 2007. I didn’t self-identify as a web analyst for the 2005 iteration, but I’ve seen fingerprints of it. It’s recurring and predictable. It’s akin to the periodic “Information Architecture is DEAD!” line that appears so frequently at their conference that it might as well become a lolcat meme. So,[…]
I have a few questions I’d love to answer in any forthcoming Google+ Analytics package. These include: What is the Recency-Frequency curve of viewing and posting for Google+ users? Can we accept or reject the Zuckerberg Hypothesis of List-Making? What are the characteristics of UGC content that is shared and plused the most often? What is the relationship between circle views, stream activity and UGC post frequency to those circles? What is the relationship between the number of those in a circle and post frequency? What is the average post frequency that results in somebody being booted from a circle and placed into a new circle (or deleted altogether for posting too hard)? Do Circles cause organic community of interest[…]