I am very shy of promoting anything on this blog, my stuff or that of others. For a number of reasons. So I’ll caveat this post by saying that this is not a promotion for the book (though you won’t hurt my feelings if you buy it :) ).
But I was doing a final review of Ch 13 of the book (Web Analytics: An Hour a Day, page 341 specifically) and came across this set of text.
For some reason as I read this I felt a overwhelming desire to post it on the blog today, even though I wrote this in December. Must be something in the air about data. :)
Perfection: Perfection Is Dead, Long Live Perfection.
The ever resourceful Wikipedia defines perfection as “a state of completeness and flawlessness.”
As analysts, and even as decision makers, we are steeped in metrics and numbers and math and things adding up. We seek confidence in data to make decisions that can make or break our businesses (or our personal lives). More than others, we seek perfection because of our backgrounds in numbers and Excel and, most important, logic. If A plus B divided by C equals five million dollars, then we will take action Q, but only if we have utter confidence in A, B, and C.
To achieve a level of perfection, we spend more money on better tools; we slice, dice, hack, and smack the data until we feel that we understand everything about it; we spend time waiting for more data or different data; we wait for someone else to make the decision or we make no decisions at all; we lose money, time, resources, and value. It seems to make sense that it is risky to make decisions based on imperfections and that it could be expensive to make decisions when things (numbers, in our case) don’t seem to all add up and perfect sense.
The problem in the real world is that nothing is perfect. It sounds obvious, but it is not quite as obvious. The challenge for web analytics specifically is that we rely on a set of deeply imperfect systems to collect data, process it, and analyze it. These imperfect methodologies include cookies, JavaScript tags, data hopping around the world passing anonymizers and firewalls, pages loading, and web structures staying the same, and so forth.
The result is that often our core human instinct to seek perfection (perfect understanding, predictability in data, stability in numbers) actively hinders our ability to find insights from our data, insights that ultimately might make or break our businesses. This is much more of a challenge for analysts because we are used to things matching up and making sense. In all of our prior experiences (in finance, ERP systems, data warehouses, business intelligence , phone sales, etc), we are used to our ability to count off numbers and apply quality controls and cleansing mechanisms that would make the data perfect (or very close to that).
The Web, on the other hand, does not make sense, in more ways than you can imagine.
Perfection on the Web is dead (well, it was never there in the first place). You will have to steel yourself for that realization and adapt your mindset to make decisions and take actions in an imperfect world. It absolutely requires some level of comfort with “faith-based analysis” to ensure that some of the sub optimal outcomes (delays, cost overruns, lack of actions, time wasted) won’t happen.
Even if the pursuit of perfection is futile on the web, it is possible to make massively impactful decisions that will change your business and improve the web experience of your customers.
In this section of the book I’ll provide some examples that illustrate the challenges of perfection.
I’ll stop there but the story continues.
In context of everything else you’ll read about data today, I hope that this tiny post was helpful.
Please share your perspective and feedback via comments.
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Thanks for reminding me guys, here is the latest edition of the top ranked web analytics blogs. (Older Editions: