I sometimes get asked for career advice. Sometimes I break down and give it. I’m not as eager to dispense advice as Lucy van Pelt at her “Psychiatric Help” booth in the old Peanuts comic by Charles Schultz. My rates are cheaper than hers, though.
The questions come from around the world, from people hoping to work in various data-intensive industries: What do I have to study? How long will it take? Can someone my age get hired in this field?
I don’t have specific answers. My own background in analytics is non-existent; I simply stumbled into a field that I discovered a passion for (data mining), via another field that I stumbled into (higher ed fundraising). The last time I took math was in high school, and I’ve never taken a course in statistics. My career is a patchwork and, although I wouldn’t do anything differently, my path is hardly a model to emulate.
If I am cut out for this work in any way, it is that I am diligent about learning new things that help me do better work, and I seem to have some affinity for data that I wasn’t aware of even just a few years ago. My CV may be lacking in credentials, but I’m lucky to have an employer that listens to what my good work has to say, and not whatever claims my credentials might be making.
So I don’t know much, but I know more today than I did yesterday, and I am good at explaining what I’ve learned to other people. Here are a few things I’ve come to know.
First, I doubt that age makes any difference. The growing demand for workers with data analysis skills may never be satisfied, so I would think you’d be marketable whatever age you are. Unless perhaps you’ve never heard of the Hinterwebs.
For someone taking first steps, this is an exciting time. The data analytics field is wide open — it’s not some kind of priesthood. There is a ton of knowledge-sharing going on via the Web, in publications and at conferences. Expose yourself to all of that.
I imagine that formal education in computing and programming (or statistics and advanced mathematics, or business, or database-related information technology), would be a big asset — if you’re young and prepared for several years of university, and are bent in any of these directions, then go for it. But don’t let yourself be steered into subject areas that are not of central interest to you. Analytics, it seems to me, is best pursued as complimentary to work that interests you — as a means of doing great work in a new, insightful way.
That especially applies to older workers who are looking for a change from the work they’re doing now. You may not get to do analytics work for IBM without an advanced degree, but that doesn’t mean you don’t have plenty of options. Any industry, business or activity that generates reams of data related to human behaviour is a rich playground for the data miner.
But really, what activity these days doesn’t generate loads of data? I can’t think of a single area of human endeavour that does not (or can not) benefit from gathering and analyzing relevant data. Which leads me to my final and primary piece of advice: If you want to work with data, then just do it. Look around you, where you are working right now. Seek out any sort of data-related problem or project you can find in your current employment, and learn just enough to make some progress. Any exposure to real-world data and its messy problems will be good experience. And who knows but that you might become a data pioneer in your specific area of employment?