Season 2: Episode #23 | Version Control in Analytics and Why It Matters

🔍 Tired of sticky-note SQL and fragile dashboards? In this episode, Vadym and Helen dig into version control in analytics – why treating your SQL like code matters, how to avoid "final_FINAL" disasters, and what real-world teams are doing to keep their data trustworthy.

What you’ll learn:

🚨 Why version control is critical for analytics teams
💥 Real-world mistakes caused by missing change history
🛠️ How to implement lightweight versioning with tools like Git and dbt
📁 Folder structure, naming tips, and archiving best practices
🧰 A quick tour of helpful tools – from GitHub to Notion

➡️ Start making analytics more reliable with OWOX BI

Podcast listing

Vadym:
Hey everyone, welcome back to The Data Crunch Podcast! I’m Vadym, your host, and today, we’re tackling a question I think most data teams have silently screamed at some point: Why do we treat analytics logic like temporary sticky notes? You know – dashboards built on fragile SQL, versioned only by file names like “final_FINAL_THISoneUSE.sql.”

And joining me again – you know her, you love her – is Helen, Head of Customer Success here at OWOX. Helen, welcome back!

Helen:
Thanks, Vadym! It’s always great to be here. 

And about the question you asked, yes… the real nightmare starts when someone opens that file six months later and says, “Wait, where did this number come from?” I’ve been there. SQL that no one remembers writing, no context, no backup – just vibes.

Vadym:
Exactly. And that brings us straight to today’s topic: version control in analytics. 

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Alright, Helen, let’s jump in. Version control – Not the sexiest term, but wow is it important.

Helen:
Totally. Think of version control as giving your analytics logic a brain – and a memory. Without it, you’re one broken query away from a crisis.

Vadym:
Let’s talk about the pain points. What happens when analytics teams don’t use version control?

Helen:
First up – people overwrite each other’s work without knowing. Or worse, they copy-paste logic into five other places, and suddenly your “single source of truth” is five sources with different numbers.

Vadym:
And when things go wrong, you hear: “Oh yeah, Anna changed that formula last week. I think.” No changelog, no context, just a shrug.

Helen:
Exactly. And let’s be honest – tribal knowledge is not a long-term data strategy.

Vadym:
Alright, so what does good version control look like in the analytics world?

Helen:
Think central Git repos. For dbt models, SQL scripts, even your BI dashboards. Use pull requests. Add comments. Review changes. It’s not about slowing things down – it’s about knowing who changed what, when, and why.

Vadym:
And structured folders! Not “final_final” or “Dashboard_new_July_EDITED.”

Helen:
Haha, yes – real names, please. And the ability to roll back when something breaks? Absolute lifesaver.

Vadym:
Now, let’s say you’re new to this. What’s the best way to dip your toes into version control?

Helen:
Start with your core metrics. The KPIs everyone depends on. Version those queries first. Even if you’re just using GitHub with basic folders and simple commit messages, it’s a huge step forward.

Assign code owners. Track who’s responsible for what. And hey, it doesn’t have to be perfect. It just has to be better than invisible logic floating around in people’s heads.

Vadym:
Okay, let’s shift gears. Helen – hit us with a real-world story. One where version control saved the day... or where the lack of it almost sunk the ship.

Helen:
Sure – at one company, a junior analyst updated a revenue model on Friday. On Monday, the CFO saw a 30% jump in MRR and almost celebrated with champagne. But turns out, the analyst accidentally duplicated a filter and inflated the numbers. No versioning, no PR, just a silent update. It took days to untangle. If we’d had version control? One glance at the commit would’ve caught it in seconds.

Vadym:
Champagne postponed! That’s a perfect case for why this matters.

Vadym:
Alright – rapid-fire time. Helen, give us some quick version control tips every analyst should know.

Helen:
Let’s go:

1. Always explain your commit messages.
   “Fixed stuff” doesn’t help anyone. A good message tells not just what changed – Git already shows that – but why it changed and how you fixed it. That context is gold, especially when you hit a similar issue months later and need to retrace your steps.
   So you just should be able to find that version.
   
   
2. Don’t push to main – open a PR and get a second set of eyes.
   Of course, if your workflow allows it. And this ties directly to the previous point: a clear, precise commit message makes the review process way faster. The person reviewing your PR will instantly understand what was changed and why – no guesswork, no back-and-forth.
   
   
3. Create a README.md for your folders with context.

Actually, the folder structure itself matters a lot. I’ve seen different setups that all worked well: organized by department, by product area, by vendor (if relevant), or by project. The one thing I don’t recommend: naming folders after analysts. If you’re looking for a change related to country grouping, you won’t think to check the “Julie” or “Tomas” folder – even if their readmes are perfectly written. Also, keep folders shallow when possible – if you need five clicks to find a model, that’s a red flag.

4. Archive old SQL – don’t delete it.

Even if it’s no longer used, old SQL can hold valuable context. You might need to reference it later to understand how a metric was calculated in the past, debug a regression, or answer a “What changed?” question from stakeholders. Create an archive or deprecated folder, add a quick note about why it’s no longer in use, and move it there. Clean structure, no lost history.

5. And use consistent naming. “Report_v2_final” is a lie.

I know it sounds obvious, but this naming chaos is still everywhere. When you’re using tools like Git, there’s no need to cram metadata into file names – no dates, no initials. Git already tracks who changed what and when. Your file names should focus on what the SQL actually does – like monthly_revenue_by_region.sql instead of final_FINAL_useThis_one.sql. Clear names = faster onboarding, easier reviews, and fewer headaches.

Vadym:
Love that. Now, before we wrap, Helen, give us a quick tool round-up. What should people be checking out?

Helen:

• dbt: Built-in versioning, testing, and documentation. It’s a game changer.
• GitHub or Bitbucket: Don’t overthink it – start with the basics.
• Notion + SQL: If your team’s not ready for Git, this combo still gives you structure.

Vadym:
So there you have it: version control isn’t just a dev thing – it’s the key to consistent, reliable analytics in fast-moving teams.

Helen:
And it doesn’t have to be complicated. Just pick a spot, start small, and grow from there.

Vadym:
And if you want to trust your data – and bring order to your analytics logic – check out OWOX BI. We make it easy to version your transformations, track changes, and collaborate on data across your team.

👉 Head to owox.com and start your journey to reliable, transparent analytics today.

Helen:
Thanks for listening – and remember, you don’t need perfection, you need visibility. Version control helps you get there.

Vadym:
Thank you, Helen! Thanks to everyone listening. Subscribe, leave us a comment, and tell us your best version control horror story. We’ll catch you in the next episode of the Data Crunch Podcast.