Originally posted on my employer’s blog (here), I explain how I came to love data- and ultimately data driven decision making.
I wasn’t born a data loving nerd; I started college as a double major in English and biology. That plan changed within three weeks of starting classes.
My parents pushed hard for the biology track. They felt that in order to get a job when I graduated, I needed to major in something that would leave me with quantifiable skills. Then I got back my first intro level bio test. I informed them in short order that I was going to be a theatre major.
Despite taking only two undergraduate math courses (one Pass/Fail, to avoid having to put much time in), within three weeks of starting graduate school I decided to add quantitative methodology as my secondary field of study. And amid theory and language classes, I wound up taking 4 semesters of statistics.
So, how did I come to love statistics after thoroughly giving up on math?
The answer is simple: I love stories. At first glance, math seems intangible. Numbers are abstract until you can find a way to get them to mean something. Once you move the numbers from a data set into a chart or into a table, they become more than numerals – they show patterns of behavior, give insight about how to solve problems, and even present new and exciting mysteries to solve. Statistical analysis makes numbers into narratives.
At school, I was soon able to look at data and sleuth out answers that applied to real world problems. I could look at a data set, clean it, scatter plot it, slice and dice it, and then tell you (with reasonable statistical certainty) that, for example, there is a correlation between successful social movement organizations and malleable organizational structures or diverse funding sources. Or that self-identified cat people are more likely than dog people to skew urban-dwelling, female, and to name George Harrison as their favorite Beatle (guilty). I’ve got the numbers to back it all up.
Then digital advertising found me. The nature of the questions changed, but the end result didn’t. The questions were less about organizational structure or the likelihood of violent conflicts, and more about an advertiser’s return on investment. The questions became:
- What’s working?
- What could be better?
- Could I be spending my advertising dollars more effectively?
- How can I best present this data to my clients?
And, for me, how can I take what I have learned, and use it to inform future decisions?
The beauty of online advertising is that between analytics solutions and highly trackable media, we can tell what’s working and what’s not. Then we adjust accordingly. The end result: campaigns that just work better for clients. Campaigns that meet real needs and tell better stories. And in a world where more effective use of ad dollars not only means more revenue but more satisfaction for the end customer, everyone wins. That’s why I love stats; they help us shape our stories into what we want them to be.