It’s a question many of us have been quietly (or not so quietly) asking. With AI moving faster than ever, it’s hard not to wonder: Where do we, as data analysts, fit into this future?

Some days it feels exciting automating the repetitive stuff, getting to deeper insights quicker. Other days, it feels a little uncertain  like the ground beneath us is shifting and we’re expected to keep up.

But here’s the truth: this isn’t just about tools or trends. It’s about how we grow with them. AI might change how we work, but it doesn’t replace the curiosity, the critical thinking, the domain knowledge or the human judgment  that we bring to the table.

If you’ve been learning to adapt, finding new ways to stay relevant, or even just thinking about what your future as a data analyst looks like this space is for you.

Let’s talk honestly. Let’s learn from each other.

For projects involving customer data analysis, how do you balance data transformation steps (like aggregation, enrichment and deduplication) to maintain both data quality and model performance, especially when using tools like Alteryx before feeding the data into machine learning models?