The end of the year is approaching, and with it, reflextion season begins. Here's one I'll share out loud, okay? I've been at Thoughtworks for nine months now (fun fact: I've been eyeing this company since 2013), and joining this place has been a revolution in my career in so many ways.
Thoughtworks takes building technology very seriously. It feels like everyone here lives and breathes this craft, you know? It's so deeply ingrained that there's always someone from TWers, somewhere in the world, involved in creating tech or concepts that go on to set market trends: Selenium, Data mesh, Infra as Code, DevOps, CI/CD, Microservices, the Agile Manifesto... The list goes on. Honestly, it's such an inspiring group of people that it gives you butterflies, you know what I mean?
So, why am I sharing this? Because it took me a few months to realize that being in this environment was an open invitation to dive into the world of Software Engineering - and maybe come out on the other side as an even better data professional. And let me tell you, I resisted that invitation at first. "No, I'm a data scientist. They hired me knowing I'm not a developer", I told myself more times thant I can count.
But I'm an incurably curious person. I can't stand not understanding at least the basics of something, you know? Yes, I'm the kind who spends Black Friday money on books and courses... and old-school nerd here!). Before I knew it, I was taking DevOps course on KodeKloud, reading The Phoenix Project, and signing up for Cousera Plus to explore Software Engineering programs.
Yeah, that happened...
We, data scientists and analysts, are passionate about diving deep into the nuances of analytics, algorithms, statistics, and data visualizations. But it's high time we acknowledge that the true impact of a data project doesn't lie solely in building models or generating insights. It's about how these solutions can be scaled, maintained, and integrated robustly within organizations. This is where Software Engineering takes the lead.
Here are some principles and practices that can transform how we deliver value with data:
- Cleaner, easier-to-maintain code
- Scalability
- More effective collaboration with technical teams
- Production readiness (remember:
"9 out of 10 data projects make it past the pilot/PoC stage". Gartner, 2019.
If that doesn't hit you hard, that's okay - life goes on, there's more out there to explore.)
It's time to learn from those who've already mapped the pitfalls along the way, like learning from an older sibling. This is the way to not just create value but sustain it. Consider this your invitation to take the plunge too!
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