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[…]
Author: Christopher Berry
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[…]
I tend to leave a trail of giant whiteboards wherever I go. The giant whiteboard is one of the most important instruments in an information firm. Whiteboards are colorful and communicative. They’re democratic. Anybody can really get up, grab a marker, and really go at it. They’re raw. They’re just awesome. I use them to collaborate. I use them to present problems, solutions, and opportunities. The eraser is a memory hole. Any mistake, any exploration, any avenue is open and correctable. It’s outstanding. Powerpoint. Ah yes, powerpoint. Powerpoint is an awesome visual medium. It’s great in that it really doesn’t command anybody being presented at to do anything. People sit there and you point at them. Powerfully. Powerpoint has uses.[…]
I asked five questions flowing out of eMetrics Toronto, posted on May 1, 2011. Some editorial to contribute. No hard evidence by way of a survey yet. (You’re all surveyed out anyway.) Does the a culture of testing, if sustainable and feasible, drive incremental improvements in usability simple because the organization becomes more aware of usability? If the dependent variable is clear, uncontested, and lends itself to a direct attribution model, a culture of testing is likely to drive incremental improvements. There exists a ‘bathtub’ point where the marginal returns on optimization are consumed entirely by headcount and technology attempting that optimization. Why don’t/won’t designers and analysts work together more often? The Excel communication medium hampers the relationship. The Photoshop[…]
I grabbed this data from the Toronto Open data site. I loaded it into Google Refine. I used SPSS to understand just what was going on. I’ve stripped this post of political editorial, so if you’re here for that, this post will dissapoint. The story: Always read the data dictionary and description. In this instance, I have a file containing a sample of a sample of all the service calls to 311. Toronto has a single call center routing system called 311. It’s pretty efficient, in that it’s a single department, and that any citizen can dial and report something, and get routed through to the right place. It’s an example of very good policy learning. The disclaimer is that[…]