Agile analytics isn’t a novel concept in any shape or form. Things like feedback loops and process-oriented development seem to integrate flawlessly into the analytics paradigm, at least on paper. Heck, there’s even the Build-Measure-Learn framework for continuous development. It would be difficult to argue that analytics doesn’t have a role in something with measure in the name! However, past three years of working at Reaktor, one of the world’s top agile technology houses, have introduced me to a whole new set of problems with integrating an “analytics mindset” into an agile workflow, or an “agile mindset” into the analytics process.
This year I had the opportunity to present at eMetrics London and Berlin on a topic that is very close to my heart. I’m psychotically neurotic about data quality. I’ve written about it many times before, and it’s pretty much why I want to keep on blogging and writing about analytics and tag management customizations. At eMetrics, I stepped out of my comfort zone of development and implementation, and chose to talk about organization practices.
Have you ever stopped to think about what the chain of events was that led you to a particular decision? Maybe not, but in web analytics it is something that should be considered. After all, there is something counter-intuitive about analytics tools such as Google Analytics, which require us to think in terms of clicks and recorded events that occur on the website, during the visit. Thankfully, we have evolved as a species, and we no longer place too much emphasis on last click attribution.
A word of warning. This is not a developers’ post, a guide, or a thought experiment. This is a bona fide rant. Sometimes we just need to vent. A couple of weeks ago, I checked one of our (inactive) client’s Google Analytics accounts I still had access to. What I saw in the acquisition report was this: See how direct traffic gobbles up a great big share of organic traffic in late October?