The One Skill You Need To Develop In Order To Be Great At Working With Data (That no one talks about!)
Critical skills every data professional should have.
Remember when "I Googled it" became our universal response to any question? Now, with ChatGPT and other Large Language Models (LLMs) in a close horse-race to become the Master who 'knows it all' , that phrase seems to be slowly evaporating from our vocabulary – like water droplets imperceptibly leaving an ocean. While Google isn't disappearing anytime soon, our approach to information seeking is undergoing a fascinating transformation.
Ironically, as research for this post, I took the traditional route. I googled "data skills" to see what the well-SEO'd, "authoritative" sources would reveal about the essential skills for data science. Surprisingly, across several pages of results, I couldn't find a single mention of what I consider the most crucial skill:
Analytical Thinking.
You might ask, "What exactly is analytical thinking?" Let me explain: Analytical thinking is a multi-dimensional convergence of skills that combines different competencies to optimize (mathematically speaking) the most efficacious answer to the most consequential problem.
While this might sound obvious, there are several non-obvious challenges in data science (more broadly - pattern recognition; data science is inherently pattern recognition) where analytical thinking proves essential:
The missing "Meta" of the Problem
In business settings, we often tackle problems that are either nuanced or remarkably different from what we should be addressing. Why? Because we're missing the crucial context, origin, and ethos surrounding the problem. Each of these elements is vital (I'll delve deeper into these aspects in future posts).
The Toolbox "Friction"
Many of us develop a bias toward tools we've mastered, creating resistance to learning new approaches that might offer different perspectives. What we need is a heuristic map – similar to how emergency rooms have standard operating procedures for various scenarios. While medicine is a science, the ER's intervention process is a combination of science and art. Science as in algorithms making data driven decisions in critical cases such as trauma, cardic arrest, stroke. Art as in human judgment using their experience and intuition to apply pattern recognition and making fast decisions in the most ambiguous situations. ERs don't have a toolbox friction, the cost of that friction is too high. The cost of that friction is a human life. I would argue, these ER doctors would be the savviest analytical thinkers through their gruelling training. Most other places, we are afforded to carry this friction with us, as the cost equation is entirely different, or perceived to be non-consequential, which unfortunately is not true.
Bringing Guns to a Stick Fight
Sometimes simple is not only better. Simple is the only viable answer. Just because a complex answer can be built, doesn't mean it is the right answer. I mentioned ethos earlier. Depending on the industry, domain and yes, even organization, data-centricity culture plays a huge role in how we go about producing meaningful data-driven capabilities. This is compounded by what I call a 'Desire for complexity' bias. A lot of us data science professionals go through reasonably complex graduate math materials. There is a need to resist the urge to wield the laser gun in a stick fight.
There are more of these types of issues require the development of Analytical Thinking skills. And that is fundamental to success in data science and data analytics careers.
If you're:
Aspiring to build a career in Artificial Intelligence and Machine Learning
Curious about applying pattern recognition skills in various areas of life, or simply want to stoke your curiosity.
Follow me here
I'll be sharing detailed insights on developing these crucial analytical thinking skills in future posts.