Hi, I’m Kerith. You might have met me through this blog before, but if you haven't, I play music professionally, sometimes at a mall near you or at your school (if you’d like to invite us). During the day, I handle marketing and admissions at Eskwelabs.
At a family gathering, I had a conversation with my brother-in-law about how fun it was to work as a medical representative in the early 2000’s. He remembered befriending doctors and taking the time to get to know them. He had a strategy that gave him his A game. He’d get close to soft-spoken, introverted doctors, because these kinds would usually know only a few med reps. Once he gained that doctor’s trust, well, he’d be the only choice of that doctor. He’d be the only one summoned for the latest pain relievers or antibiotics.
That was the heyday of that career, he said. Right now, the industry has changed—and it’s not as lucrative as before. He said the challenge now is that people more frequently buy generic drugs. It seems that people just need to get well regardless of the brand of the medicine.
Industries change so quickly. You never see VHS store managers in this day and age anymore. Gone are the days when you’d see a long line of taxi drivers whom you’d have to desperately persuade to take you to NAIA or to Megamall. Why? Because now you have Angkas or Grab to take you there.
Another huge change is the impact of AI this year. AI is here to make research easier. AI is here to give us ideas for next week’s meal prep. AI is here to help write resumes better. Sadly, AI could replace a lot of jobs such as those of social media managers, content creators, translators, customer service representatives, sales representatives, technical support specialists, and more.
Still, there are jobs that AI can’t replace such as HR managers, sales managers, marketing managers, and more—including data analyst roles. Here are a couple of reasons why.
I’m not sure if you’ve worked in a large company where you would find that the sale of Product A has increased by x%. Then, another department or someone from the higher echelons of your company would say that it has increased by y%. That happens, right? This makes it hard to tell if you’ve achieved your target or not. As complex as it gets, data comes from multiple sources, and the heroes who make us realize which sources yield more accurate data are the data analysts.
One of our co-founders Aurelien Chu taught us to give detailed prompts to ChatGPT for higher-value responses. For example, you have to give it the Context, the Audience, the Style, and the specific Task that you need. Large Language Models (LLM) need more than just short prompts from stakeholders, who, with their limited time, usually ask shorter questions than data analysts do. This is where data analysts come in. They write more comprehensive prompts for higher-value analysis, without needing to go back and forth too much with ChatGPT.
Data analysts are able to achieve so much more with these AI tools.They can use AI to clean their data, and help them achieve a more thorough exploratory data analysis. AI can also help with adding new features and variables to datasets, allowing for more predictive power. What’s more, data analysts will be able to choose the right machine learning algorithms with the help of AI. Data analysts can also ask AI for help in evaluating the performance of the models that they’ve trained.
If you feel like your job might be conquered by AI, let’s take conscious steps to upskill. You can check our Data Analytics Bootcamp and Data Science Fellowship to see if these are to your liking. If not, explore some more. In Steve Jobs’ timeless words, spoken almost two decades ago, “The only way to do great work is to love what you do. If you haven't found it yet, keep looking. Don't settle.” I’m sure you won’t end up being a VHS store manager who hails a taxi to who knows where.