TigerEye

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Like many undiagnosed data nerds, I didn’t choose Excel—Excel chose me. I’ve worked in marketing, product, and operations, and data analysis has been the one constant that’s helped me succeed. My early love for Excel set me on a path toward SQL and greater analytical self-sufficiency. I’d seen the nice, orderly grid in high school, but didn’t write my first formula until a few years into my career—mostly because I was tired of updating things manually. I was lazy, and formulas just seemed easier than doing it all myself. Of course, once you start, you can’t stop. The more you know, the more inefficient everything else seems. But the deeper I got, the more obvious Excel’s limits became. It’s easy to mess up data. Formulas are crammed into a tiny box, making them hard to debug. (Though you do get good at spotting mismatched parentheses.) Later, I was analyzing user network data at a startup, and my process had gotten ridiculous: I relied on Engineering to pull raw data, brought it into Excel, transformed it, and eventually surfaced insights. Then the next week, I had to do it all again. That’s when I decided I had to learn to do more on my own. We were using MySQL, so I started there—downloading the software and working through the online manual. It felt totally foreign at first. Unlike Excel, the desktop app didn’t show much, and what it did show was packed with jargon. But the documentation was solid. Bit by bit, I decoded it—and built a surprisingly robust pet registry along the way. Then I turned to user behavior data. It took a while to get as comfortable with SQL as I was with Excel, but once I did, I quickly surpassed it. I could do everything faster, more flexibly, and more reliably. And as data changed, my growing bank of queries scaled with it. There are people whose Excel skills are next-level, and this isn’t about saying one tool is better. SQL is just the right tool for the kind of analysis I’ve built my career around. SQL makes you more self-sufficient. You don’t have to wait on Analytics or Engineering. You’re not limited by anyone else’s time or knowledge. When you have a question, you can answer it—and then dig into the next one. Excel gave me the data literacy I needed for SQL: structure, normalization, cleanliness. Writing formulas taught me to break questions into discrete steps. For many in Marketing Ops, Demand Gen, Sales Ops, and RevOps, SQL feels like a tool for “the other side of the house.” Meanwhile, so many are crushing it in Excel. If that’s you, consider taking the leap. The skills you’ve built are more powerful than you think—and SQL is the next unlock. Need a nudge? Join me for a free webinar hosted by TigerEye on April 9th. I’ll be joined by Rose and Audrey to talk about why it’s never been more important for Ops teams to control their data stack—and how to start building the skills to do it. #sql #marketingops #startups #bi #revenueoperations #salesoperations #data #learning Hope to see you there.

Check out our new Team Deck, aka "Sales Rep Baseball Cards". Even works on your phone so sales managers can swipe through to quickly check on team performance.