Season 2: Episode #20 | Building Trust Through Data Lineage

🔍 Ever wondered where that metric actually came from? In this episode, Vadym and Ruslan break down data lineage — the essential (but often overlooked) practice of tracing your data’s full journey from source to decision. 

Whether you're debugging dashboards, managing compliance, or just trying to build trust in your numbers, this one's for you.

🛠 What you’ll learn:
1️⃣ What data lineage really means — and why it's more than just a buzzword
2️⃣ Real-world use cases and how lineage speeds up debugging and decision-making
3️⃣ How to overcome common challenges like tool chaos and lack of ownership
4️⃣ Practical tips to get started — from automation to team-wide visibility

➡️ See how OWOX BI supports better lineage and cleaner analytics

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Podcast listing

Vadym:
Hey everyone, welcome back to The Data Crunch Podcast! I’m Vadym from the OWOX team. And today, we’re diving into a topic that doesn’t always get the spotlight — but it really should. We’re talking about data lineage — what it is, why it matters, and how to actually use it to make better decisions and avoid those classic “where did this number even come from?” moments.

With me today is Ruslan, Head of Product here at OWOX. Ruslan, welcome back to the podcast!

Ruslan:
Hey Vadym, great to be back here! And yeah — data lineage is one of those things that can seem a bit technical or abstract at first, but once you understand it, you realize it’s the backbone of trustworthy analytics. It’s what connects your raw data to your reports, dashboards, and decisions. Without it, everything starts to fall apart.

Vadym:
Exactly. And before we dig in, quick reminder — if you’re finding value in these episodes, don’t forget to subscribe on YouTube or follow us on Spotify, Apple Podcasts — wherever you’re tuning in from.

We drop new episodes every Thursday, packed with real stories, practical advice, and maybe a few data horror stories too.

Alright, let’s set the scene a bit. When we say “data lineage,” what are we really talking about?

Ruslan:
In simple terms — data lineage is the story of your data. It’s tracking the full journey: where it comes from, what transformations it goes through, and where it ends up.

So when a metric looks weird in your dashboard, data lineage helps you trace it back — step by step — to find out what went wrong or who changed what.

Vadym:
So it’s like turning on the lights in a dark room. Suddenly, you can see the flow — not just the final number.

Ruslan:
Exactly. And that visibility is what makes it so powerful. It’s not just about debugging. It also supports governance, compliance, transparency, and better decisions.

Vadym:
Alright, let’s zoom out for a second. Why does lineage matter so much in the first place? Is it just about avoiding errors?

Ruslan:
Errors are a big part of it, but let me break down the bigger picture.
There are five big reasons I’d call out:

• First up, regulatory pressure. With frameworks like GDPR and CCPA and industry-specific standards, organizations are being asked to show their work — to prove where data came from and how it was handled.
• The next big one? Transparency. Everyone from analysts to execs needs to trust the data they’re seeing. If they don't know where it came from or how it was processed, they’ll start second-guessing every dashboard.
• Then there's troubleshooting. When something breaks — and let’s be honest, it will — lineage helps you trace the issue back to its source fast. Saves hours of digging.
• Governance is another one. Data lineage aligns everyone — analytics, marketing, engineering — around shared rules and responsibilities. Who owns what? What gets tracked? What’s the standard?
• And finally - decision-making. When you know your data is clean and traceable, you can make decisions with confidence.  No more “I’m not sure if we can trust this metric” during board meetings.

Vadym:
That hits the nail on the head. Now, I know a lot of teams are probably thinking: cool, but what does this look like in the real world? Can you give some practical use cases?

Ruslan:
Sure. Let’s say you’ve got a weird number on a report — like revenue suddenly dropping to zero. With lineage, you can trace it back through each transformation and find out where the issue happened. Maybe it was a change in a SQL script. Maybe the source table wasn’t updated. That’s root cause analysis — and it’s so much faster with lineage.

Another one is deprecating unused columns. You can see exactly which reports or pipelines are using them — or not — so you can clean up safely without breaking anything.

Or think about data retention. If you can see how data flows and what it’s used for, you can make smarter decisions about how long to keep it — and make sure you’re not hoarding sensitive data unnecessarily.

Vadym:
These are the things that feel small, but they add up. And when you don’t have lineage in place, your data debt just grows.

Ruslan:
Totally. It’s one of those things where a little effort upfront saves you a ton of pain later.

Vadym:
Alright, let’s talk about what trips teams up. Lineage sounds simple, but it’s not always easy. What are the common challenges?

Ruslan:

Good question. There are a few that stand out:

• One is granularity. Some teams go way too deep and make things overly complex, while others track too little and miss the context. The trick is to track what matters — not every little change, but enough to tell the story.
• Another big one is standardization. If different teams use different names or rules, your lineage gets muddy. So, aligning formats and metadata is key.
• Then there’s tool chaos. You’ve got data flying in from warehouses, APIs, spreadsheets… it’s chaos. You need a platform that can connect those dots.
• And lastly — timeliness. If your lineage isn’t up to date, it’s as bad as not having it at all. Automating updates is a must.

Vadym:

It’s amazing how many of those problems come back to one core thing: people.

Ruslan:

Exactly. The tech is there. But the culture? That’s the hard part. You need governance, ownership, and ongoing collaboration to make lineage stick.

Vadym:
By the way, the tech landscape isn’t exactly simple either. You’ve got a dozen tools between data ingestion and final dashboards.

Ruslan:
Yeah, each handoff is a risk. And unless you’ve got lineage stitched across the entire pipeline, you’re flying blind.

Vadym:
So, what can teams actually do to fix this? Let’s talk solutions.

Ruslan:
Totally. There’s no magic button — but here’s where I’d start:

1. Automate lineage tracking — don’t rely on humans to document every transformation. Tools can do this way better.
2. Track multiple types of lineage — not just the technical steps, but also business rules and operational flows. That’s how you connect what’s happening in SQL to what the business sees.
3. Make lineage visible. Put it in front of the people who use the data. The more accessible it is, the more people will care.
4. Align it to real goals. Lineage isn’t a vanity project. Tie it to things like faster debugging, cleaner compliance audits, or less time wasted chasing data issues.

Vadym:
That sounds like a solid checklist. What tools would you recommend for teams starting out?

Ruslan:
I’d start with what you already use. BigQuery has lineage features built-in now, and Google Dataplex offers interactive graphs to visualize dependencies.

If you want something more advanced, look into MANTA, Alation, Collibra, or Atlan — but honestly, for a lot of teams, Dataplex plus good governance is more than enough.

Vadym:
I am curious, what does “good governance” actually mean in this context?

Ruslan:
It means having clear roles, standards, and accountability.
Who owns what dataset? What’s the rule for naming fields? How often do we audit?

It’s about building a shared responsibility model where lineage isn’t just a data team thing — it’s everyone’s job.

Vadym:

So — final thoughts. If someone’s listening right now and thinking, “Okay, we want to get better at this” — where should they start?

Ruslan:

Start by mapping out just one important report. Figure out where the data comes from, what transformations happen, and where it goes.

From there, build some habits — document transformations, standardize naming, assign ownership.

And remember — the goal isn’t perfection. It’s progress.

Vadym:
That’s a great takeaway, Ruslan. 

If you want to see some of this in action, visit owox.com and start using OWOX BI for free today. You can also check our OWOX Reports Extension for Google Sheets. It helps you connect directly to BigQuery and bring that visibility into everyday reports — no SQL required.

And if your team’s ready to level up your lineage game, give us a shout. We’re here to help.

Ruslan:
Data doesn’t have to be mysterious. With the right tools and habits, it can be clear, reliable, and genuinely helpful.

Vadym:
Thanks again, Ruslan. And thanks to everyone listening! If you’ve enjoyed the episode, subscribe, share it with a teammate, and let us know in the comments below how you’re thinking about lineage in your own work.We’ll catch you next time on The Data Crunch Podcast.