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🚨 Your dashboards might be lying to you — and you wouldn’t even know it. In this episode, Vadym and Helen get brutally honest about the hidden data quality issues lurking in your reports, pipelines, and culture. They break down the real reason data chaos persists in 2025 — and what your team can actually do to fix it.
🛠 What you’ll learn:
1️⃣ Why data quality problems are cultural, not just technical.
2️⃣ The most common pitfalls: patchwork fixes, no ownership, and silent errors.
3️⃣ How to build a system of trust with automation, standardization, and clear ownership.
➡️ Ready to stop patching and start fixing? Explore OWOX BI
Vadym:
Hey folks, welcome back to The Data Crunch Podcast! I’m Vadym from the OWOX team — and today, we’re talking about something that sounds simple but quietly ruins everything from campaign budgets to company-wide strategy. You guessed it: data quality.
Helen:
Hey Vadym! And yep — we’re going there. Again.
But honestly, we have to. Because no matter how many tools or dashboards or audits you throw at the problem, nothing gets better unless one very basic thing happens…
Vadym:
…someone actually cares.
Helen:
Exactly. That’s the real issue, isn’t it?
It’s not just about fixing broken data. It’s about the fact that no one wants to own the problem in the first place or take some actions, and prefers just to skip it or leave it as it is.
Vadym:
Let’s be real: there are two kinds of nightmare scenarios in data.
One — someone pings you and says, “Hey, your dashboard is showing the wrong numbers.”
Two — and this is worse — no one notices for weeks, maybe months. Then you find out your execs have been making decisions based on trash.
Helen:
And the third one is — the silent killer.
Everyone kind of knows the data isn’t great. But no one wants to dig in, because… well, that means owning it.
Taking initiative means taking responsibility — and suddenly it’s your mess to fix. So people just shrug and say, “That’s just how our data is.”
And that? That’s how bad data becomes business-as-usual.
Vadym:
Yeah, we’ve been there. And it’s 2025! There’s no excuse anymore. There are so many tools.
Monitoring. Validation. AI-assistants. Real-time alerts. But here’s the kicker…
Helen:
Tools don’t solve culture. If no one takes ownership, the whole system breaks down.
You can have the fanciest setup in the world — but if people just shrug and say, “Not my problem” — you’re dead in the water.
Vadym:
Right. It’s kind of wild when you realize that 40% of business initiatives fail due to poor data quality. And executives are still reporting dissatisfaction with their data — even in 2025!
So if you're listening today and feeling like your team spends more time fixing broken reports than extracting insights — this episode is for you.
Or if you know your company’s data isn’t great, but fixing it feels way too hard or messy — yep, still for you.
Before we dive in, don’t forget to subscribe to the podcast on YouTube or follow us on Spotify, Apple Podcasts — wherever you’re tuning in from. We drop new episodes every Thursday, full of actionable advice just like this.
Alright Helen, let’s get into it.
Helen:
Let’s do it.
Vadym:
Let’s talk about data governance. Not the sexiest phrase. But it’s everything.
Helen:
It really is. Data governance is just a fancy way of saying: “Let’s decide who owns what — and let’s make sure they actually care.”
Vadym:
It’s shocking how often this doesn’t happen. A developer ships a new feature — but no one adds tracking. A new landing page is launched — and it is tracked totally different way than the rest of the website. A product manager runs a test, but forgets to define what counts as a “conversion.”
Helen:
Right?! Or marketing launches a new campaign, and no one uses the UTM standards specific to your company. So suddenly, “LinkedIn Ads” is showing up as “(Other)” in your report. And yes, someone has to write the standards — and keep them alive.
Don’t leave tracking to chance. Don’t assume “someone else will clean this up later.” Assign clear ownership — make sure the right person is responsible and has everything they need to do their part properly.
Vadym:
And if you’re thinking “But our team is small” or “We’re not ready for that kind of structure” — tough. You’re already paying for it in bad decisions, wasted hours, and dashboards no one trusts.
Helen:
Let’s talk about another big one: fixing symptoms vs fixing causes.
Vadym:
Yup. The “quick fix” mindset is poison.
Helen:
Look, sometimes you have to do it “quick and dirty” — a manual correction here, a re-upload there. But if that’s your default? You’re just digging the hole deeper.
Vadym:
Exactly. You wouldn’t keep refilling a leaky bucket without patching the hole, right?
Helen:
We call this “data debt.” And it builds up fast — especially when you’re rushing.
Instead, treat every weird report or broken number like a bug in production. Find the root cause. Was it a script? A schema change? A human error?
Vadym:
And here’s the trick: make your monitoring do the work.
If a pipeline fails, alert someone. If a value goes out of range, send a notification. Don’t wait for a Slack message from your CMO asking, “Why are conversions at zero?”
Helen:
Also — be curious. There are tools now that can flag weird spikes, validate stuff automatically, and even help you fix it with AI. And some services will just quietly do the dirty work in the background. But — and this is key — someone needs to care enough to set them up. Yes, it takes a bit of effort at the start. And yes, it might seem quicker to just click around in a report and patch things manually. But that’s how you end up stuck in Groundhog Day. Automate what you can — it’s the only way to stay sane.
Vadym:
Ownership again. See the theme?
Helen:
Let’s hit the last big one — data standardization. Or as we like to call it: ending the chaos of mismatched sources.
Vadym:
Nothing’s worse than five teams pulling the same metric and getting five different numbers.
Helen:
Oh wait — there is something worse.
Everyone knows the numbers don’t match, but no one agrees which one is right.
Vadym:
This is why we need a single source of truth. One place where the rules live — how we calculate conversions, which UTM goes where, what counts as a session, a user, a customer.
Helen:
What I see working really well — is when a team once and for all documents how things should be. Define your primary entities (sessions, users, leads, regions) — and turn that into a data model. That model becomes your single source of truth.
Yes, someone has to take the first step. Usually, it’s an analyst who says, “Okay, I’ll do it.” It takes time to build that structure at the beginning, but then the magic happens: the numbers start matching — no matter which angle you’re looking at them from. Everyone knows where to go for the right answers. And if something changes? You update it in one place, and that change flows through everything.
Vadym:
And standardization isn’t just about reports. It’s about enabling cross-team collaboration.
Sales, marketing, product — they can all speak the same language if the data is aligned.
Helen:
One version of the truth. Not one version per team or per report.
If a full data model feels like too much right now — start smaller. The key is the mindset: always aim for one source of truth. Let’s say you’ve got 18 reports, and the channel grouping rules are set in each of them separately — that’s chaos. Instead, put those rules in one Google Sheet (define the owner who set those rules!), and let all the reports pull from there. One change — and it updates everywhere.
It’s about taking small steps, but with discipline. Don’t let chaos grow.
Vadym:
So — let’s bring it home. Why is data quality still such a mess in 2025?
Helen:
Top1 reason: because no one cares.
Or rather — not enough people care, and not consistently.
But it’s not about finding a hero. It’s about building a culture where everyone takes responsibility for their part of the data journey.
Vadym:
And the good news? You don’t need a massive team or endless budget. You need ownership, structure, and the guts to say “We’re not patching anymore. We’re fixing this for real.”
Helen:
Start small. Assign roles. Fix the root causes. And think from the single source-of-truth perspective. And invest in tools that make quality easy, not harder.
Vadym:
And if you want help with that — you know where to find us, just visit owox.com. OWOX BI is built for teams who are tired of messy data and want clarity, consistency, and control.
Helen:
At the end of the day, good data doesn’t just happen — it’s the result of people who care. If there’s one thing to remember, it’s this: data quality is everyone’s job. It’s not about perfection — it’s about ownership. So stay curious, stay accountable, be persistent — and let’s build data we can actually trust. And finally, sleep well. You’ve earned it. 😊
Vadym:Thanks for listening, everyone. Subscribe, share, and drop us a note if you’ve got your own war stories about data chaos. We’ll see you next Thursday on The Data Crunch Podcast!
