Hey guys, happy Monday! Checking in after a two-week break (oops!)
A few weeks ago I had the chance to be a featured speaker at DataConnect Conference. I gave my talk about what it means to evolve as a data professional in the age of AI, which I’ve shared before as a keynote talk at DataDay Texas earlier this year.
I’ve had amazing turnout at both conferences for this talk, so I thought I’d share it with those who haven’t been able to see it in person—in this week’s newsletter!
How to optimize for longevity as a data professional in the age of AI
Ever since 2021, AI has probably been the biggest thing to happen in the data world, to data professionals. Data people are especially affected by this arrival because we have the luxury of working in a field that’s always on the cutting edge of technology.
But with that luxury also comes the threat of becoming outdated and obsolete every day. Especially because data science professionals aren’t just grappling with the arrival of Generative AI, but also with the economic recession, the field shifting to be more commoditized, and increased competition for jobs.
All of these factors lead us to this important question: how do we as data professionals stay relevant in a space where the ground shifts beneath our feet every day?
I present you with 3 options:
Move your skills to meet the job market
Move the job market to meet your skills
Keep a healthy distance between your skills and the market at all times
Let’s dive into each one!
1. Move your skills to meet the job market
This is probably the most straightforward method to evolve as a data professional, and is best if you’re earlier on in your data career. The basis of this option is staying updated with emerging technologies and techniques to remain competitive in the field.
But another facet of this method that’s more underrated is collaboration. Collaborating not only within your data org but also with business stakeholders or data operators who are tangential to your role is like a shortcut for expanding your skillset to domains that are complementary to data, without needing to become an expert in those domains.
2. Move the job market to meet your skills
This is the route I would recommend to data professionals who are mid-career with technical skillsets that are a bit further along. You can move the market to meet you in two ways:
Make your skillset known to attract market opportunities to come to you, namely through personal branding. Not only is your personal brand nearly untouchable by AI, but it also is how you differentiate yourself in a space that is increasingly competitive and commoditized.
Develop a t-shaped skillset. “T-shaped” people excel in their core specialties and can go deep in them, which is represented by the vertical line in a T. But they also have a broad enough skillset that they can do other tasks outside of their direct expertise effectively—that’s the horizontal line of the T. This type of skillset positions you to attract opportunities over time because you’re general enough to cast a wide net for diverse opportunities to find you, but that spread also means you have the flexibility to develop multiple vertical specialties and move into different ones over time. Your “vertical” is not stagnant; it should change with both the market and your interests as you grow.
3. Not moving your skills or the job market
This last option sounds counterintuitive, but you might consider it if you’re a later-career data professional with a lot more to lose. Why would you want to keep a strategic distance between your skills and the market at all times?
Because this AI hype could very well just be a fad! One study by AWS revealed that only 6% of executives they surveyed reported their company had any production application of Gen AI in place, whereas another study by Wavestone only reported 5%.
Being able to spot fads is a skill in and of itself. By not just floating along with the wind in every direction it blows, especially in the data field where it feels like there are storms of change brewing all the time, you can shield your well-developed technical skillset from a very fickle job market and prevent dilution of that hard-earned skillset.
So if Gen AI is indeed just a fad, does this mean you have absolutely nothing to worry about if you fall in this bucket of advanced data professionals?
Nope, because we all know this field does not reward staying put.
What I recommend is keeping your technical skills where they are, but upskilling in the areas AI can’t touch—the “human skills” that no technology can ever automate away. Additionally, these skills complement your technical ones, are always in demand, and equip you with the tools to adapt to any industry shifts or just shifts in your personal interests that may come over time.
So out of the 3 options, which one is the right one for you? How do we actually optimize our evolution as data professionals in the age of AI?
Just like how we rarely use just one clean-cut method to arrive at our answers in data science, the right answer here lies somewhere between all 3 options. It might be weighted toward one more than the other depending on the person, but for most, it will be a combination of all of the following: upskilling your technical capabilities, learning from your collaborators, building your personal brand, developing a T-shaped skillset, and last but not least—upskilling your human capabilities too.
The biggest takeaway I wanted my audiences to come away with from my talks was this:
View this inflection point as an opportunity to grow, yet also practice some healthy skepticism… to embrace AI as friend and motivating foe at the same time.
Even more specifically, use AI to shore up your weaknesses, while leaving enough room for your innate strengths to shine.
That’s it for this week’s newsletter! See you for the next episode of The Data Diaries soon 👋
- Megan
And in case you don’t know who I am, I’m Megan Lieu, Data Scientist-turned-Developer Advocate who has helped thousands of job seekers through my content on LinkedIn and Instagram, as well as my courses on LinkedIn Learning. I’ve learned a lot from the ups and downs of my data career, and sharing the lessons has helped me build a community of 200k+ tech and data professionals.