Season 2: Episode #16 | Building a Product Analytics Culture in Your SaaS Company

🧠 Building a truly data-driven product team takes more than dashboards. In this episode, Vadym and Ruslan break down what it really means to create a product analytics culture – from shared definitions and fast insights to tools your team actually uses. Learn the 5 pillars every SaaS company needs to turn data from a bottleneck into a competitive advantage.

💡 What you’ll learn in this episode:
1️⃣ What a product analytics culture actually looks like (and what it’s not).
2️⃣ 5 foundational pillars for empowering SaaS teams with data.
3️⃣ How OWOX BI combines modeling, chat UI, and Google Sheets to bring analytics into your daily workflow.

➡️ Start building your product analytics culture with OWOX BI

Podcast listing

Vadym:
Hey everyone, welcome back to The Data Crunch Podcast! I’m Vadym, Growth Marketing Manager at OWOX, and today we’re diving into a topic that a lot of SaaS teams struggle with – even if they don’t realize it: building a product analytics culture that actually works.

And to help us unpack this, I’ve got Ruslan here with me, our Head of Product at OWOX.

Hey Ruslan, great to have you back!

Ruslan:
Hey Vadym, always a pleasure. And I’m really excited about this one, because this topic cuts to the core of how SaaS teams actually use data.

Too many companies say they want to be data-driven. They hire analysts, set up dashboards, and stack up all the right tools… but when someone on the product team asks a simple question like, “Which features are most used by paid users in their first week?” – the answer still takes a week and a few meetings.

That’s not data-driven. That’s a bottleneck. So today, we’re talking about how to fix that – how to build a culture where product teams don’t just see data, they use it. And they trust it.

Vadym:
Exactly. For too long, “being data-driven” has meant waiting on someone else to pull numbers. But we’re seeing a shift, and today we’ll explore what it actually takes to build a product analytics culture that empowers teams across the board.

Before we jump in, just a quick reminder to all our listeners:
If you're watching us on YouTube, make sure to subscribe and drop a comment below. We’d love to hear if your product team has faced any reporting bottlenecks.
And if you’re tuning in on Spotify, Apple Podcasts, or anywhere else, follow the show and turn on notifications. We’ve got new episodes coming your way every Thursday.

Alright, let’s get into it. Ruslan, to kick things off, what do we really mean when we say “product analytics culture”?

Ruslan:
Yeah, great place to start. A product analytics culture is not about having a dozen dashboards in Looker Studio that nobody opens. It’s about creating an environment where product managers, growth teams, designers – anyone building the product – can turn questions into insights quickly and confidently.

It’s not just about having access to data. It’s about using that data – every day, in sprints, in standups, in roadmap planning.

Vadym:
So it’s less about metrics... and more about the mindset, right?

Ruslan:
Exactly. It’s about empowering teams to explore, question, and act on data.
Let me break it down. A real analytics culture looks like this:

• 🔓 Empowerment: PMs don’t wait days for a report – they can explore data themselves.
• ✅ Control for analysts: They define the models and structure, so there’s trust.
• 🔍 Exploration over dashboards: You don’t hunt across 10 tabs – you just ask.
• ❓ Questions over metrics: Curiosity drives insights, not just charts.
• 🤝 Ownership across roles: Everyone uses data, not just analysts.
  
  

Vadym:
I love that – especially the part about “questions over metrics.” That’s where the real value lives. But let’s be honest, most companies try to build this and fall short.

What usually gets in the way?

Ruslan:
Oh yeah – there are some common traps.
📊 First, dashboard dependence. Teams build static dashboards that get outdated quickly. They look good... but no one uses them.
🧱 Then, siloed ownership. PMs rely on analysts, analysts rely on engineering... and by the time you get the answer, the opportunity’s already gone.
🌀 Or there’s overcomplicated tooling. You’ve got five different platforms duct-taped together, and no one knows how to use them properly.
🔤 And maybe the most damaging: no shared language. If “active user” means something different to everyone, you’ll never trust the data or each other.

Vadym:
That last one is huge. So many debates in meetings come down to mismatched definitions, not insights. So, how do we get it right? What does a strong product analytics culture actually rest on?

Ruslan:
It comes down to five pillars. Let’s break them down:

1. 🧱 Standardized Metrics Definitions
   This is your semantic layer. You need clear definitions for terms like “user,” “milestone,” “trial start.” Otherwise, every team ends up building their own truth.
2. 🛡️ Control for Data Teams
   Analysts don’t need to gatekeep – but they do need to protect the model. Define your joins. Own the transformations. Prevent duplicate logic.
3. 🌐 Accessibility
   If only analysts can use the system, culture dies. Product teams should be able to explore insights through tools they already use – like Google Sheets, Chat interfaces, or templates.
4. ⚡ Responsiveness
   Speed matters. When a PM has a question, they should get answers fast, like within the sprint. Not next quarter.
5. 🤝 Trust
   No one uses data they don’t trust. You build trust with clean definitions, consistent logic, and explainable outputs – not magic numbers.

Vadym:
That makes so much sense. And it’s not just about processes – it’s also about having the right systems in place to support this culture.

Let’s talk about that. What does the ideal modern product analytics stack actually look like?

Ruslan:
Absolutely. Here’s what we recommend – and what we use at OWOX:

1. 🧠 Data Warehouse + Modeling Layer
   Your raw data lives in BigQuery or Snowflake. But that’s just the start.
   With OWOX BI, you can build a standardized Product Analytics Data Model – or adopt our ready-to-use template with tables like user, event, session, project, subscription, and more. That becomes your semantic layer.
   
   
2. 💬 Chat-Based UI for Business Teams
   Even the best model is useless if it’s locked behind SQL. That’s why OWOX BI lets PMs ask plain-English questions like:
   
   

• “Which key action do lead to user activation?”
• “What is the retention rate for active users in their 2nd month?”
• “What’s the downgrade rate by plan?”
  And get instant answers – no SQL, no delays.
  
  

3. 🧾 Google Sheets as the Reporting Surface
   Let’s face it – PMs live in Sheets. So push insights there, where decisions happen. OWOX BI lets you deliver live, modeled data straight to Google Sheets. Filter it, share it, build on it.
   
   

Vadym:
So that gives you the full stack: BigQuery to OWOX BI for modeling + chat in Google Sheets for action.

That’s super powerful – but also really simple.

Ruslan:
Exactly. You make insights:

• Accessible
• Actionable
• Aligned

That’s what reinforces culture.

Vadym:
So here’s the big question. Let’s say someone’s listening and thinking, “Okay, I want this. I want a culture where my product team can explore data, get answers, and move fast.” What’s their first step?

Ruslan:
Start with your model, not your dashboard.
Define your metrics. Create shared language. Enable teams to explore.
From there, your system should support them, not slow them down.

Vadym:
Beautifully said. Ruslan, thanks so much for walking us through this. I think a lot of product teams will see themselves in this conversation – and hopefully walk away with a clearer path forward.

And for those of you listening – if you want to see how this works in action, check out OWOX BI. You can explore our product analytics data model, test the chat interface, and see how fast your product team can get insights flowing.

It’s free to get started, and you can find the link in the description of this episode.

Ruslan:
Thanks, Vadym. And to our listeners, don’t settle for waiting on reports. Build a system that empowers your teams to think with data. That’s how culture scales.

Vadym:
Alright, that wraps up this episode of The Data Crunch Podcast. Thanks for tuning in – and we’ll see you next Thursday with more insights on data, analytics, and building better systems. Until then – stay curious, and keep crunching!