Overcoming Technical Barriers
But not technically
Let’s take a moment and look at the title, “Overcoming Technical Barriers”.
Now, let’s take another moment and think about who is writing this article. It’s me, Arynn, and in case you didn’t already know, my background prior to data is in Industrial-Organizational Psychology. You really thought I was going talk about how to overcome technical barriers in a technical way? Silly you - technical barriers are actually people barriers in disguise. Don’t believe me? Well, keep reading. Maybe I can change your mind.
Here’s the three technical barriers I’ll discuss:
Entrance
Discovery
Learning
Entrance Barrier
You know how you graduate high school, then go to college knowing the exact major to study for the career that you want? LOL. Okay, there are some of you out there, but that’s largely not a given for many people who find themselves working in data and tech. Some people wind up in a data career because they discovered they loved working with data in a different field. Others, including your author, wind up in data because they needed a job that paid the bills. It just so happened that they checked off a few of the job requirements for “data analyst”.
Data Analyst is a catch-all job, and working in data is a catch all-career. It’s extremely difficult to enter into any field that doesn’t involve data in some way. What about majoring in English? Yes, even English. Some of the best technical writers I’ve ever known were prior English majors. Similarly, you can find tons of data work in political science, natural sciences, business, psychology, anthropology, and so on. Of course, there is the more straight and narrow path of studying computer science. However, these other areas of study bleed into data careers like small lakes and streams into a larger body of water. A data career is a reservoir for people who didn’t quite get it right when answering “what are you going to do with your life?” when they were a teenager.
The reservoir of data people is beautifully yet tragically diverse. The diversity of it is beautiful because it is the confluence of different views and backgrounds forming something more complete. It’s beauty is also valuable; organizational measures of success improve with diversity of thought. However, diversity in the reservoir is also tragic. Pouring things together doesn’t mean that they will mix. Sometimes, the differences are so great separate layers form. This is the entrance barrier - figuring out your “data density” and what layer you’re supposed to settle into. Find the other data people that are like you.
So how do you overcome the entrance barrier? By knowing the barrier exists. Knowledge is power! (haha) But seriously, understanding that there are others with the same job title but wildly different knowledge, skills, and abilities is an asset. Yes, you all still work in data. No, just because they use Python and you use R doesn’t mean you’re any less of a person working in data. Different doesn’t mean better or worse. It just means different. Make friends in across as many layers as you can. Like I said- knowledge is power.
Discovery Barrier
Have you ever thought back to when and where you first heard of a tool that you use all the time? You weren’t born with an innate sense of how Git works (if you were, tell me your secrets). Maybe you know some tools because they were what your company used at your first job. Maybe you read about it on LinkedIn, or Hacker News, or Gartner, or StackOverflow, or Reddit, or carved into a tree with a heart around it. If it wasn’t at some job or part of some course, you had to have read about it or seen it somewhere. But where and when? Sometimes this question is easy to answer. Learning a tool or some feature of can be a “tech grail moment”, as Jacob Matson so eloquently put it.
My theory is that these tools become our “tech grails” because they solved a key problem we were having or made a process significantly easier. That’s all anybody really wants, right? A tool that just works; it helps you get the sh*t done that you need to get done with as little pain as possible. That’s what’s we search for, but that’s not so easy to discover. We seek the grail.
I don’t know if you frequent LinkedIn or other social media places where tech #vendors exist, but searching for the grail there looks something like this:
When every cup (or SaaS product) looks like it might be the grail, how can you be sure that you chose the right one? Is this the cup that will provide the illusive business value that will save your data team? Indiana Jones is stuck in a cave - the discovery barrier. As if getting to this point wasn’t hard enough, it gets harder.
Wouldn’t it be nice if you waltzed into the cave and there was just one cup? Obviously that’s going to be your grail. But there isn’t just one cup. There’s a veritable buffet of cups of all shapes, sizes, and shininess. Vendors. Ugh, vendors. (Wait, doesn’t the author work for a vendor company? Yes, but I can still be self critical, alright?). Many of these places we go to read about and discover new tools either have vendor accounts or paid media and influencers talking about certain vendors. And they all want one thing: your attention.
“Wow that cup has rubies on it!” - racks up your compute costs
“Look at the ornate cup design!” - spaghetti code even an Italian grandmother is afraid of
“That cup has wings on it!” - data superheroes, zen master
“Is that a cup maid of chainmail?” - data mesh/fabric debates
“Why is there a duck in here?” - obvious
This isn’t a knock on marketing teams or sales - they’re just doing their job. But them doing their job makes your quest for the grail a heck of a lot harder. Humans are distractible creatures. Sometimes we get carried away by shiny things, and marketing/sales capitalizes on that.
How do you overcome the discovery barrier? I’ll tell you, but you might not like the answer. No matter how good a marketing campaign is, no matter how influential an influencer is, you are the one making the choice. Just because they got your attention, it doesn’t mean you have to let them keep it. You must choose wisely. The most tried and true way to guarantee you make the right choice and overcome the discovery barrier is by doing this one thing (and it’s disgusting):
Reading the documentation. Hidden in the depths of product documentation are gems of wisdom more valuable than anything else you could read. But guess what? You actually have to read it. I know, it’s not as fun as going to low-key data happy hours, attending conferences, listening to podcasts, or reading blog posts (haha). But let’s talk about the value. Think about going to a data happy hour for an hour. Now, think about reading the documentation for an hour. In which scenario did you learn more valuable information? Almost always, the answer is going to be the latter. I am a realist, though. I know that all work and no play isn’t good or healthy way to live your life. But all play and no work is also a bad option. Find a balance.
The Learning Barrier
The learning barrier is very similar to the discovery barrier. Their origins are located in the same place, but the content and purpose is different. Image you’re scrolling through posts on whatever social media platform you look at data content. One thing you’re sure to come a cross is listicles.
Some examples
Top 5 skills you need to learn to become a 10x engineer
The 7 python hacks every data scientist should know
Best free courses for beginners in Machine Learning
Top skills I used to land a job as Principle [Data Role] at [Big Tech Company]
There’s a few reasons why listicles are popular. The biggest one being, they work. Humans remember things best and enjoy consuming content when it’s in a smaller, chunked format. The same thing goes for why so many posts on LinkedIn separate smaller paragraphs or single sentences with lines of blank space instead of writing a more contained paragraph. Marketing is self-perpetuating here. Because humans like to consume listicle format better, marketing realizes that the listicle format is their content that performs the best. Because the listicle content performs the best, companies make more listicles. Now consumers see more listicle-formatted articles. The cycle continues.
Now that you know what the learning barrier looks like, let’s talk about what makes this a barrier. These articles, based on the titles, seem like they’re written to be extremely helpful. Sometimes they are, but sometimes they’re filled with a bunch of fluff. Independent content creators put these things out all the time, too. It’s what does well with the newcomers to the data space - the ones still trying to figure out their layer in the data reservoir.
Lots of content creators (i.e. The Seattle Data Guy) make things intended for beginners in a particular data practice - it’s a large audience. But why are independent content creators…content creating in the first place? They make money. And the best way to make money is to post the content that gets the best performance. That’s how we wind up with listicles like: 5 Great Libraries to Manage Data with Python. I do want to reiterate that some of this content is genuinely helpful. But you can’t tell what’s helpful or not helpful based on title alone.
How do you overcome the learning barrier?
There’s two different ways.
One way is through paid courses from a reputable source that come with a certification. These certification courses may help you to learn what you want/need to learn. There’s an additional benefit, but it isn’t for you; it’s for your resume. It’s a useful way to prove to hiring managers that you know what you say you know.
The second way is through free learning content provided from a company or third party. There’s so many free options, and the hardest part of starting is picking which one to use. Look up all the courses on beginner Python that are free. The options are overwhelming. If you have friends suggest a particular course for you - just do it. If you see a course that has some great reviews - just do it. If you see a course that fits your time constraints - just do it. Maybe Nike should sponsor how to overcome the third technical barrier.
The point is, don’t spend too much time being guided on what to do instead of actually doing it. If you start a free course and you aren’t getting what you want out of it, switch! Actually learning is the best way to overcome the learning barrier.
Conclusion
Are technical barriers put there by people, or by the tools themselves? Will technical skills help you overcome these three barriers? I’ll leave you to your own opinion. Let me know what you think.