Since we all can’t be up to date on everything all the time, there is huge benefit in listening to alternative perspectives to your own.
As with any field, analytics has a set of characteristics that can help a person develop into an increasingly better version of themselves. While these characteristics overlap with “virtues” you’ll see in other contexts, these posts are meant to highlight their specific relationship to professional development in this career field.
There seem to be 2 extremes that are too easy to fall into–the first being a self-deprecating beginner sort of mindset where one is unsure about everything and constantly second-guessing solutions, and the other being an overly self-assured mindset, either from lots of experience or a confident beginner who isn’t yet aware of the complexities of the field (for more on this, see the Dunning-Kruger effect). With sufficiently diverse experience, it’s easy to feel that you’ve “seen it all.” In many cases, you have seen a good slice of things, but you haven’t worked on every aspect of an implementation every day – that is simply not possible. No matter what your level of experience, the web analytics stack that surrounds you is constantly evolving, and it’s possible to lose sight of some of the basics or certain aspects of a platform as you narrow your focus for various projects.
This necessitates walking a line where you can be confident in your recommendations, yet open to new information and listening to differing opinions. Since we all can’t be up to date on everything all the time, there is huge benefit in listening to alternative perspectives to your own. That doesn’t mean they’re automatically valid, but allowing the space for them to be aired is supremely valuable, not least of all because it allows you to hone your active listening skills and refine how you can eloquently navigate a set of varied opinions to arrive at a definite project plan.
This can be a hard one to find where the line is, and there are bound to be some times when you veer a bit far to one side or the other of the overconfidence/humility line, but the process of figuring this out is key to continued personal progress. I certainly don’t have everything figured out, but I’ve seen this come up as a recurring theme that can have a big impact. Onward and upward!
First rule of analytics: you will have to make decisions with imperfect and incomplete information.
In the web analytics field, there is a constant tension between mathematical rigor and the “eureka” moment that comes unexpectedly – the hunch that leads down a path of many more questions that need to be explored.
Despite all the (necessary and laudable) efforts placed into precise implementation of the tools, and rigorous statistical analysis of A/B testing, to succeed in this space requires the ability to make the leap to a concrete suggestion with imperfect data, versus knowing when the information you have is so incomplete as to be inadequate for the task.
In this vein, it can actually be a disadvantage in some ways to come from a very mathematically-focused education, since in the world of numbers it is possible to reach a state of perfect or near-perfect proof of something. In the real, messy, day-to-day world of business, however, many complicating factors are likely to arise, and “making all the data perfect” is unlikely to happen anywhere – there is nearly always room for improvement, or past issues that need to be accounted for. That’s the nature of placing analytics on top of live web data, which is prone to complications at the server, internet provider, browser, individual machine, site platform, back-end database, and a number of additional levels, not to mention implementations changing hands over time and human error.
To navigate this, I suggest:
- Spending the first few years in rigorous study of major analytics platforms and learning the “ideal” state.
- Get in a place where you can look at many different implementations to see how things play out in the real world and see different scenarios.
- Throughout, follow your hunches and support them with the data that you can, but when you just “know”, use that as fodder for an A/B test or similar that allows you to fully prove out your theory.
- Don’t be paralyzed by too many small details of an implementation – break the problems or suggestions into major buckets.
Of course, it may not be possible to order your career in precisely this way, but the general idea is that in order to be nimble at the speed the business is nimble, you will have to make decisions with imperfect and incomplete information. The best way to develop your analytics intuition is spending time building a “vocabulary” of scenarios in your mind so that when you encounter a new one, you have a palette to paint from.