Curiosity made me watch way too many YouTube videos debating the merits of a data analyst compared to a data scientist, and vice-versa. One features a low barrier to entry, the other doesn’t; one possesses R scripting skills, the other doesn’t; one requires a master’s degree, the other doesn’t; and so on.
I’m sorry to have to break it to you, but there isn’t much of a point in trying to define or defend those territorial boundaries, which are only as meaningful and concrete as the lines drawn on the ground in Squid Game. I have learnt that shaping one’s career to a pigeonhole, any pigeonhole, is counterproductive. What matters is how deep, broad, and skilled an individual goes to establish their unique role and thought leadership within their environment.
2023 has been a tough year for white-collar jobs. Speaking of which — every job ad, no matter the position title, carries these two bullets: “excellent communication” and “strong analytical skills”. Graduate and experienced alike, it is impossible to avoid these “requirements.” But what do they even mean, especially in the age of artificial general intelligence?
First, the excellent communication part: the imminent availability of AGI and specialised GPTs means that there is no place or tolerance for sub-par documentation, narratives, slide decks, or emails. As soon as the wow-inducing demos are over, surviving human workers are held to high standards that they must meet no matter what tool or intelligent assistance it takes, not only to produce results in a highly efficient manner but also to be able to competently synthesise, evaluate, and criticise their own and others’ output.
The strong analytical skills part, on the other hand, is way more complicated than firing up Copilot in Excel. Analysis is now a matter of literacy; just like arithmetic and basic Excel skills, it is a foundational component fused into every human role in every business. The lines that decide who can and should get tables in shape, produce visuals, communicate findings, and validate results, are becoming blurrier by the day. If you hold a Data Analyst or similar title at work and the previous sentence summarises your job description in its entirety, I’d be worried because the commoditisation of relevant tools, services, and learning paths is turning informed individuals into people who have an analyst built into their original role. This, in turn, means every thriving team will possess human workers (not AI) who have no trouble saying things like:
- “I ran the numbers for the last quarter and have found two interesting patterns.”
- “See in this chart how things change dramatically as soon as I take (x) into account instead of (y)?”
- “I/you/they will need (x) and (y) on top of what’s been made available here to be able to draw any conclusions. Where would I find those?”
- “Let me cross-check that real quick just to be 100% sure before we take this to (x).”
- “Assuming what I have here is accurate and will hold true for the foreseeable future, my recommendation is to do (x).”
- “Yes, my own reports support that (x) has been on the increase steadily over the past 6 months, too.”
- “Now I realise that I/we/they won’t be able to take action without (x) included as the primary group-by field. Can I/you/they re-organise this report to facilitate that?”
- “Where did you/they get the underlying data?”
- “This clearly doesn’t add up as I can see items with missing (x) and (y). Rows with a missing (x) can be discarded whereas all instances of missing (y) will need to be tracked down and filled in correctly.”
- “This is the data my team has been able to extract based on the known requirements. Tell me what the gap is so I can assist you with your back-end processing.”
There is a reason why well-known aptitude/admission tests such as SAT and GMAT are divided into language and mathematical parts. These two pillars correspond to — you guessed it — the excellent communication and strong analytical skills requirements across real-world jobs. That said, I am not selling any course or bootcamp today because the way true upskilling happens is:
- Self-taught.
- Self-paced.
- On the job.
To conclude, here’s my paraphrase of the cliché of the year 2023: human workers will be replaced not by AI, but by humans who push demand and direction down AI’s throat with original thoughts backed by data.
