It was a bright summer day in 2014, which meant sweaty armpits and that my tight school pants were even more uncomfortable than usual.
Midday was approaching, and I, along with my fellow soon-to-be-senior peers, was being cajoled to the assembly hall of my school where a career guidance session was about to be held.
After the session, I hadn't had any major shifts in how I viewed myself as a person, but one of the session's keynote speakers touched me in a way I didn't expect.1 He looked and spoke like me, sure — dressed in the local attire of my people, and even mixed up his "f" and "p" sounds. But he was one more thing: an engineer.
Staring into the vastness of data analytics is like gazing into the unknown depths of the universe — both equally perplexing and mysterious; each an endless fountain for the elixir of a better existence. But lurking in the shadows, endless potential catastrophes often await, apparent to only those curious enough to dare explore their abysses.
For the uninitiated, data analytics is simply turning data into insights that can help individuals, businesses, or even government agencies make better decisions in approaching how they solve their unique problems. "Yeah but what does that mean?" you ask. Pfft… What does anything mean, really?2 Didn't see that coming, did ya? (No but seriously click on the footnote if you're unsure.)
As is often the case, the solutions to each problem are tangled in threads of complexity interwoven in anything but rationality. Our biases and emotions get the best of us, even as we hide behind entities such as "the law," holding them in high regard as if they aren't a fabrication of our own doing, riddled with what made us seek them in the first place.
Thus, I recognize how analyses rely on much more than clean datasets and the authenticity of the source and the quality of the collection process; external factors — not to mention the ethicality surrounding it all, among other things. Nonetheless, I am elated by the possibilities it offers. By giving us sufficient control and robbing us of just enough authority, data analytics allows us to believe that our answers are the most reliable. It's a balance we don't appreciate enough.
My explicit involvement with analytics is more recent, even as I have been involved in the tech industry for many years in more ways than I care to discuss here. More than a casual observer, I'm already getting acquainted with the challenges faced by those in the creeks of the industry.
Questions around what the structure of the modern data team should look like and how to effectively quantify the contributions of analysts, in particular, floods Substack lists and YouTube hot takes. Issues around the responsibility each role entails kept popping up.3
Those are only the tip of the iceberg.4 Every couple of years, the entire industry seems to undergo a massive overhaul and the cycle remains unpredictable. This segues nicely into my problem: What part am I comfortable playing in this exciting world? Technical hindrances are hardly a factor. The many years I spent in school coupled with the veracity with which I explored my artistic tastes have allowed me a certain freedom to pick and choose what I do now. I can be as techy or non-techy as I want.
It all depends on my ambitions — and the damn economy, of course. Do I want to be a renewable energy analyst and dedicate my remaining weeks towards helping tackle complications around the much needed global energy transition, or do I pursue a career in finance and aim for $500k a year before I retire? Would I rather be a data detective slogging my way into mysterious and messy datasets only to resurface with impossible insights aiding to support meaningful data-driven decisions, or would I prefer the nerdier work of creating data pipelines and maintaining data lakehouses?5
What do I enjoy doing right now? I already gain immense satisfaction from extrapolating which SQL queries would be effective in answering particular questions. I fly from table to table in absolute ecstasy — like a teenager that's just discovered porn. We shall see. As my quest matures, I wonder how much will change, both within the industry and in what I prioritize.
I maintain an intimate bond with my past; its few remarkable moments, odd dealings with strangers, that initial pubertic phase of morning wood before I learned to accept it as a way of life, and, of course, a pinch of everyday trauma stemming from inherited familial dysfunctionality. This could explain why decade-old events like the 2014 career guidance session remain, to me, fresh and vivid — as if they happened yesterday.
Now, many years after this defining moment that sparked my interest in tech, my pursuit of a career in data analytics is still driven by that same fervor. Much like my morning wood, I have accepted that life will always be confusing and that there will always remain unanswered questions.6
There is much I need to figure out in the coming months, confounded even further by how fast the world moves. Personally, however, this has been terrific!
Endless catastrophes might await me; firing my clients and draining my savings while I navigate what might not earn me a dime in the short run is probably not a wise idea. Also, ten years from today, I wonder if I would remember this moment with fondness or cringe. But what is this life without a dangerous dose of adrenaline, amirite?
Even so, I had stumbled upon a solace I had been craving for a long time: a genuine distraction from my hammering existential crisis. Debugging my code and realizing that the temp tables I intentionally tagged with unintelligible names actually did end up confusing me is infinitely better — and much more hilarious — than gazing at the ceiling of my room, deep into the night, concerningly wondering why bother with anything in light of my inevitable mortality.
Get ahold of your dirty mind!
If this was your first time hearing about data analytics or just always wonder what data is and how it all factors in making life better and businesses richer, then I got you!
A simple example would be an institution like the World Health Organization (WHO) collecting data on people affected by a particular disease — tracking how many people are contracted by the disease, their ages, genders, location, and relevant demographics.
Data analytics would allow for the analysis of this data in identifying patterns and trends in the spread of the disease. WHO could find that the disease is spreading faster in certain climatic conditions or among certain age groups, cutting down on a lot of the guesswork involved in managing the spread of the disease. Shaka brah!
If you ever happen to find yourself questioning how an analytics engineer differs from a warehouse developer and how they all differ from a data engineer, for instance, do yourself a favor and stop. What's that? You need answers. Well stop yelling dammit. Simon Whiteley's breakdown of who exactly who should be calling "an engineer" may not solve your problem, but it is a great start.
All the penis jokes in this entry were intentional. I'm immature. Sue me.
While editing, I realized I hadn't mentioned Tableau or any BI tool. I'm fascinated by how people artistically create visualizations. However, I don't necessarily enjoy building them except out of necessity. This was weird because if you had asked me a few months ago what I would enjoy more between playing around in the workspace of data warehouses and populating my Tableau profile (or my Twitter page) with colorful graphs or animated timelapses, my answer would be way off. This might change in the future, but until then, CAPS LOCK it is.