Beautiful formatting of code? AI’s got that down.

Perfectly memorized regex syntax? Nice to have, but totally unnecessary with AI.

Looking at an AI-generated analysis and noticing the window function is doing a running average where it doesn’t make sense? Highly valuable.

While interviewing dozens of candidates for a data analyst role, I thought a lot about what strengths are most important to succeeding in this role in 2025. Given all the AI tools available, the candidates who really stood out were the ones with the strongest skills at aligning analysis to business context. With so many people on the job market right now and concerns about AI disrupting the labor market, I’ve seen relatively little tactical advice on how to navigate this shift to more AI-driven data work and focus on building the right skills for the future. Here’s my advice on how to build your skills at aligning data projects to business goals.

Nail the no-brainers, showcase your skills, and highlight your humanity.

In the past week, I’ve reviewed hundreds of applications for a mid-level data analyst position. That’s right, no AI was used by me in the hiring process, just regular human eyeballs. Here’s some advice for job seekers based on the applications I read:

Nail the no-brainers

• Clarify Your Location: For a hybrid or in-person job, it helps if you indicate in your application where you are currently located or could be located. Phrases like “Open to Relocation” or “Moving to Boston June 2025” are helpful if you’re not already based in the right location. Please don’t make me guess whether you read/understood the job’s location requirements.

• Application Questions: Instead of a cover letter, we had two short-answer questions asking applicants to explain 1) their interest in the role and 2) their experience with the two most important tools in the job description. Anyone who wrote “n/a” or “…” or “discuss in interview” put themselves at a disadvantage compared to the number of thoughtful responses I received.

• Always Be Honest: I selected one candidate for an interview based on a specific skill mentioned in their application. When I asked about that skill in the interview, they asked me to define it and said they weren’t sure if they’d heard of it. Whether that was intentional dishonesty or a ChatGPT mistake, it reflected extremely poorly on that applicant.

Last week, I tried to replace myself with AI. I faced a long list of data analysis projects decided to do my best to get them all done with AI instead of writing the SQL and python myself. Here’s how it went.

A promising start

The project: updating an existing dashboard, which is built in SQL and python. I had two main tasks:

1. Transform a column containing dates to instead display the day(s) of the week (e.g., swap “5/6/25, 5/7/25” for “Tuesday, Wednesday”)

2. Parse two lists of aggregate text into separate columns for sorting and filtering (e.g., transform the text block “45% female, 35% male, 5% nonbinary, 15% unknown” into 4 columns with each percentage as a number).

Interested in pivoting into a data role from a non-data role? My #1 tip: find a way, any way, to do data work in your current role. Yes, there is definitely a way to do it, in virtually any role. Is there a bunch of information scattered a bunch of Word docs or physical papers? Do you handle repetitive tasks or requests? Does customer information live in 3 different systems? The easiest way to get data experience: start a spreadsheet to organize a set of information into one place. This is a low-risk, high-reward way to dip your toe into data work as a beginner, and it’s exactly what I did at the beginning of my career to begin my pivot into the data field. Here’s how I did it:

Build data skills by tracking your work

Early in my career, I worked in an internal company support role, where I provided support services to over 40 people across the organization. I started feeling a little burnt out on doing support work, and I was interested in shifting my own career into data analytics. Those two impulses led me to start keeping an Excel sheet to log of all the support requests I handled: the date of the request, what type of support was requested, and how much time I spent on each request. That information allowed me to create pivot tables to summarize my work:

• How much total time was I spending on support requests each month?

• When was my most recent support request from each person?

• What types of support requests were most frequent? Which took the most time?

What I’ve learned from teaching introductory Excel, SQL, and Python workshops in the age of LLMs

The first time I taught an Excel workshop on formulas, I taught the functions I use every day, including VLOOKUP and its newer counterpart, XLOOKUP. After that class, a student approached me. “So, if we have XLOOKUP,” she said, “why do we need to know VLOOKUP?” This question instantly crystallized a “duh” moment for me: anyone learning Excel today really doesn’t need VLOOKUP. (Thank goodness!) Thanks to that student, I dropped it from the curriculum I teach — and I’m so grateful for her boldness in asking the question. It even broke me out of my old habit of using VLOOKUP in my own work. Good feedback is a gift.

Her question led me to reflect more widely on all the content I teach. What else am I teaching because it’s a habit or it’s what I learned a decade ago? How can I offer students what they actually need right now?

Re-thinking data skills with AI