New Podcast Episode | SaaS Data in 2026: What's Changed and Why It's Never Mattered More

If you’ve ever sat in a board meeting while two executives argued over two different versions of the same metric, this episode is for you.

At OPEXEngine, we talk to SaaS leaders constantly about what it takes to actually know your business — not just have dashboards. This conversation with Dan Palay, CEO of KPI Sense, is one of the clearest articulations we’ve heard of where that breaks down and what to do about it.

Dan works with growth-stage SaaS companies on financial analytics and metrics, and he brings a practitioner’s perspective on:

• The most common data mistakes leaders make
• How AI is reshaping the cost side of the SaaS P&L
• Why 2026 is raising the stakes on all of it

A few highlights from the conversation:

• Your SaaS metrics are probably living in 3–4 different systems. A true single source of truth means reconciling all of them, not just picking the most convenient one.
• Not all churn is bad. Segmenting your data might reveal that your SMB customers churn constantly while your enterprise base is rock solid. That’s not a crisis — that’s an action plan.
• AI changes the cost equation. Unlike traditional SaaS, AI introduces real marginal costs per use. If you’re not tracking token spend the way you track headcount, surprises are coming.
• Benchmarking is how you know what “good” truly looks like. Stable metrics don’t always mean safe ones. Context — knowing whether your numbers reflect company-specific risk or broader market trends — is what separates leaders who catch problems early from those who get surprised by them.
• Perfect data isn’t the goal. “We’re trying to see directionality here. We are not trying to send a man back to the moon.” — Dan Palay

Whether you’re prepping for fundraising, an M&A process, or just trying to run a tighter ship, this one is worth your time.

You can listen to the episode here.

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Transcript

Katherine Zhang: Welcome to this episode of SaaS Conversations. Today we’re talking about the state of data in SaaS in 2026. What’s changed and what matters now? We’re here with Dan Palay. Dan is the CEO of KPI Sense.

KPI Sense offers financial analytics for growth-stage SaaS companies. They have an extensive history of building and maintaining financial models and metric dashboards for their clients. In addition to providing finance and data expertise, they move beyond spreadsheets to make data more visual, easier to interact with, and able to tell a story. That’s why we have Dan here today to talk about how to tell stories with data and how to get the right data for your business. So welcome, Dan.

Daniel Palay: Thank you, and thanks for having me.

Katherine Zhang: Let’s start high level. We know that good data matters in any business, but SaaS feels different in that data is especially critical. Why is that?

Daniel Palay: At the highest level, what else is there? In SaaS, you can’t go into a warehouse, count inventory, and estimate value. You can’t look at trucks or physical assets. It’s all lines of code, data, and ones and zeros. That’s what defines the business and how customers derive value.

That’s really all there is to judge the business by. But having clean data is only half the story. It comes down to the metrics you can derive from that data.

Katherine Zhang: That makes sense. In SaaS, you’re also continuously selling. There’s no one-time transaction. Customers renew, expand, or churn, which requires constant engagement. That’s impossible without good data.

And with AI, pricing is shifting toward usage. Instead of periodic reporting, you may need to measure data hourly or even in real time.

Daniel Palay: Absolutely. And in SaaS, not all revenue dollars are equal. The more predictable and stable they are, the more valuable they are. That’s critical in M&A or exits. Understanding revenue quality depends heavily on data and metrics.

Katherine Zhang: So if you can’t track everything at once, what are the non-negotiable metrics SaaS leaders should focus on?

Daniel Palay: There are two buckets.

First, general financial metrics: revenue, margins, expenses, profitability, and cash runway. These apply to any business.

Second, SaaS-specific customer financial metrics: recurring revenue and retention. These are the backbone of SaaS, but they’re fragile. It’s easy to misinterpret the underlying data, and once you do, everything cascades from there.

Katherine Zhang: What’s the most common mistake you see?

Daniel Palay: Assuming SaaS metrics can come from a single system. That’s almost never true, especially in earlier-stage companies.

Typically, data lives across three or four systems. You might get different answers from each. None are fully right or wrong, but the worst outcome is having multiple answers to the same metric.

For example, Stripe shows payments, but not whether revenue is recurring. That requires contract data from a CRM like Salesforce or HubSpot. You also need accounting data, often from QuickBooks. These sources must be reconciled.

Another issue is poor revenue recognition. Without clean accounting, everything breaks.

Katherine Zhang: So a single source of truth actually requires multiple sources.

Daniel Palay: Exactly. As companies grow, systems improve, but dashboards often become the true single source of truth by consolidating multiple systems.

Katherine Zhang: Once you have that foundation, what comes next?

Daniel Palay: First, structure the data and calculate metrics. Then add context. Metrics alone don’t mean much without understanding how they compare to typical SaaS benchmarks.

Katherine Zhang: How do you turn that into a story?

Daniel Palay: That’s where analytics creates value. It’s about what you highlight, how you interpret changes, and how you present them.

Take gross and net revenue retention. There are “good” and “bad” ranges, but segmentation is key. You might find that SMB customers churn heavily while enterprise customers are stable. That’s not just a metric, it’s a story and a strategy.

Segmentation can go deeper than SMB vs enterprise. It could include geography or industry. Without the ability to filter and test hypotheses, you can’t identify where to focus.

In diligence, buyers care about these stories. For example, companies with hardware and software must show how hardware drives recurring software revenue, not just that both exist.

Katherine Zhang: And the story isn’t always positive. Segmentation can reveal problems.

Daniel Palay: Exactly. But identifying negatives is how you improve. Not all churn is bad. Some customers aren’t a good fit. With enough data, you can identify patterns and stop targeting the wrong customers.

Katherine Zhang: So it’s about turning negatives into action plans.

Daniel Palay: Exactly.

Katherine Zhang: If you’re a CEO or finance leader, what should you do tomorrow?

Daniel Palay: First, clean accounting. Everything depends on it.

Second, don’t boil the ocean. Start with a few key SaaS metrics.

Third, establish a single source of truth that combines multiple systems.

Fourth, use consistent definitions. Misalignment between teams can derail meetings and decision-making.

Fifth, invest in experienced SaaS finance talent.

And finally, don’t wait. If you wait until fundraising or diligence, it’s too late.

Katherine Zhang: I agree, especially on talent and timing. Trying to do it yourself while running the business usually backfires.

Daniel Palay: Exactly. Things break before you realize they’re broken.

Katherine Zhang: Let’s shift to AI. How does it change metrics?

Daniel Palay: Retention metrics become even more important. ARR is often “annualized” rather than truly recurring, which can be misleading.

AI businesses often have a small group of high-value users and a lot of churn among others. Understanding who drives value is critical.

On the cost side, AI introduces real marginal costs. Unlike traditional SaaS, usage drives expenses. Companies need to track token usage and understand the financial impact early.

Katherine Zhang: That aligns with what we’re seeing. Companies need to rethink how they measure both revenue and costs in the AI context, especially separating AI-driven impact from the rest of the business.

Daniel Palay: Exactly. Companies are used to tracking headcount, but not AI costs in the same way. That needs to change.

AI may seem cheap now, but costs will catch up. Companies need to plan for that.

Katherine Zhang: Final question: is perfect data achievable?

Daniel Palay: The better question is whether it’s worth it. Perfect data is rarely necessary. We’re looking for direction, not perfection.

You need data that is generally correct most of the time. With enough volume, outliers won’t dominate.

Katherine Zhang: I agree. Data is only valuable if it helps you make decisions.

Daniel Palay: Exactly. Focus on data quality at the source, apply consistent methodology, and refine only when needed. There are diminishing returns to chasing perfection.

Katherine Zhang: Dan, thank you for joining us.

Daniel Palay: My pleasure.

Katherine Zhang: If listeners want to reach you, what’s the best way?

Daniel Palay: Email is easiest: dan@kpisense.com. You can also find me and KPI Sense on LinkedIn.

Katherine Zhang: Great. Thanks again, Dan.

Daniel Palay: Any conversation is a good one.

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