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🔍 Ever hear a data analyst sigh at the mention of another “SaaS solution”? You’re not alone – and there are real reasons behind that frustration.
In this episode, Vadym and Ievgen break down the top 5 reasons data analysts actually hate most SaaS tools – from locked data and broken trust to inflexible UIs and shallow dashboards. But more importantly, they reveal what analysts really want instead – and how companies can win their trust.
What you’ll learn:
🔐 Why data control matters more than flashy UX
📊 The real cost of shallow dashboards and black-box metrics
🧠 Why analysts crave structure, SQL, and scalable logic
📎 How to build trust with your data team (and keep it)
💡 Tips for companies building tools for technical users
➡️ Try OWOX BI – built for analysts and business users alike
Vadym:
Hey everyone, welcome back to The Data Crunch Podcast! I’m Vadym, Growth Marketing Manager at OWOX, and today we’re going to talk about something every data analyst has probably said out loud at least once: “I hate SaaS tools.”
But here’s the thing – do analysts really hate SaaS tools? Or is there a bigger story that nobody talks about? Spoiler: it’s not about being tech-averse or old-school. There are real, frustrating reasons behind this so-called “hate,” and that’s what we’re unpacking today.
Joining me for this one is Ievgen, our Head of Marketing here at OWOX – and someone who’s seen this struggle from every angle. Ievgen, welcome back to the show!
Ievgen:
Thanks, Vadym! Always a pleasure. I’ve missed the data crunch podcast since we started running it together.
And as to the topic - yes – I’ve seen analysts get burned by SaaS tools more times than I can count. Almost every time I see a data analyst, that happens. And it’s rarely about the technology itself – it’s everything around it that makes their lives harder.
Vadym:
Exactly! And before we get into it, a quick reminder: if you’re enjoying these honest, no-filter conversations about data, hit that subscribe button. We drop a new episode every Thursday, packed with stories and lessons you can actually use.
Alright, let’s get into why this topic matters so much.
I want to kick this off with a quick story. A friend of mine works as a senior data analyst. She spent three months pushing for a SaaS tool that would’ve automated a big chunk of her reporting. By the time it cleared procurement, leadership had already changed priorities – the tool is now unused, and she went back to manual CSVs...
Ievgen, you’ve heard similar stories, right?
Ievgen:
Absolutely. I remember one kinda huge enterprise company where analysts had to write Python scripts because the “approved” SaaS solution was too locked down. And you know, Python is not the language for data analysts, but rather for data engineers.
It looked great in a sales demo pitched to someone responsible for the procurement, but in practice, it slowed everyone down and created more manual work.
That’s why this topic matters. Data Analysts don’t hate tools – they hate tools that take away control over the processes they are responsible for, from the ones that delay their work, or basically cost a fortune for nothing.
Vadym:
Before we break down the reasons, let’s kill the biggest myth: analysts aren’t anti-SaaS or resistant to change.
Ievgen:
Right. They’re not dinosaurs writing SQL just for fun. Data analysts love good tools – ones that give them flexibility, speed, and trust in their data. What they hate is bureaucracy, black-box tools, and losing ownership of their workflows.
Vadym:
Alright, let’s start with reason #1: corporate restrictions. Some companies outright ban third-party SaaS tools because of strict internal security policies.
Ievgen:
Yeah, it’s like asking a carpenter to build a house without power tools. Analysts end up stuck exporting CSVs because IT doesn’t want data in “external tools,” even if they’re secure, have SOC2 or whatever, and those tools are kinda industry-standard.
Vadym:
And the solution?
Ievgen:
Using self-managed tools hosted on private infrastructure. They typically tick the compliance boxes while giving data folks the functionality they need.
ELABORATE / EXPLAIN - cloud vs self-managed
And by the way, if you’re curious how this works in practice, check out OWOX Data Marts. It’s on GitHub, we’ll add a link to the description – it’s a great example of self-managed analytics that keeps both IT and data analysts happy.
Vadym:
Let’s get to reason #2. Even if security says yes, procurement can take forever. A typical data analyst doesn’t control budgets, and approvals can drag on for months.
Ievgen:
Exactly. By the time the tool is approved, the problem it was meant to solve has either changed or been patched manually ten different ways. Analysts waste time chasing signatures instead of solving problems. Sometimes it’s really about money if you want to buy something expensive, or something that can scale to be expensive. But sometimes, it takes forever to get approval for a $50 per month tool that the whole organization would benefit from, but you just can’t get somebody to get that line on their budget.
Actually, typically, data folks go to marketing departments, because they kinda need data all the time, they have a budget for some tools, and that really works. We have a lot of customers, whose marketing team is paying to bill for the data warehouse, because they are the ones who need that the most…
ADD MORE HERE
Vadym:
This 3rd reason hits home for a lot of analysts: losing control of the data.
Ievgen:
Yes. Many SaaS tools are designed for business leaders, not analysts. Just because they are designed for those who have money. I get them.
They prioritize “easy access” for everyone but strip away the ability to govern logic, control pipelines, or ensure metric accuracy. Those points are important for data analysts.
Analysts become babysitters for broken dashboards – responsible for numbers they can’t fully control.
The classic example is everything that Google does. Let’s say Looker Studio.
You don’t build all of the dashboards, right? Sometimes you just connect the data as a data source. You add some custom SQL query in there to the dashboard and share EDIT, so someone can add some filters, change the charts, add more columns to the tables…. But then you realize that SQL is all over the place, duplicated dashboard, something changed, tweak, someone from the same marketing team that helped yesterday decided to use ChatGPT to edit your query…
And you know what happens. It’s your problem again. It’s frustrating and risky.
Vadym:
We talked about the procurement, but sometimes there’s a money problem, which is reason #4 on our list. A lot of SaaS tools, especially in the data connectivity world… they charge per connector, per row, or per user.
Ievgen:
It’s typically worse… They don’t just charge you per connector. They fine you for using the product. Because at some moment, instead of paying less for usage, you start paying 10x more, because now, you’re kinda an enterprise. Supermetrics is a great example of this pricing model.Which means as soon as your data volume or needs grow, you’re suddenly “the expensive department.” Analysts are forced to cut costs instead of driving more value, just because pricing models weren’t built for data-first teams. Not for those who want to bring more insights to the business.
Flat, ownership-based pricing models that are used in open-source tools, that are either free forever as most of the OWOX Data Mart offerings (you won’t pay a penny if you don’t have enterprise needs), or the ones that are free + something as an add-on, or the ones that charge for real usage (and the more you use, the less you pay).... those tools lets data analysts scale reporting without having to beg for budget every quarter.
Vadym:
Last but not least. Reason #5: vendor lock-in.
Ievgen:
Yeah, some tools make it almost impossible to customize what you can do. Even if there is something small to change, like adding a field from the platform API that everyone is using from the platform interface, but can’t get into the report… Data teams become dependent on the vendor’s roadmap, waiting months for features or fixes they desperately need (and that they’d code themselves in minutes)...
With open-source architecture and self-managed tools, data analysts can stay in control and actually make the tweaks they need. They can debug, customize, and actually trust their workflows.
TALK ABOUT CONNECTORS
Vadym:
So, what kind of tools do analysts actually love?
Ievgen:
Tools that give them control, freedom, and transparency.
Open-source options like Airbyte, Matomo, and, of course, OWOX Date Marts - which is both for data connectivity and data enablement (again, you can use it free forever using the link in the description below or by finding us on GitHub), then there is dbt for transformations… There are plenty of open-source tools, some of them are free forever, some of them are not that free, but still useful….
Vadym:
Let’s wrap the list with some advice for listeners stuck in this SaaS nightmare.
Ievgen:
Vadym:
So, if we sum it up… analysts don’t hate SaaS tools – they hate red tape, loss of control, punitive pricing, and vendor lock-in.
Ievgen:
Exactly. Data analysts deserve the tools they can trust, control, and scale with – and they’ll be the first ones advocating for innovation.
The future of analytics isn’t about replacing analysts with SaaS or AI; it’s about empowering analysts with the right tools so they can scale their workflows and deliver more insights at the end of the day.
Vadym:
And if you’re listening to this thinking, “We need better tools that don’t lock analysts out,” check out OWOX Data Marts.
We help data teams own their reporting pipelines, keep full control in their hands, and still deliver real-time, self-service access to business users. Yes, self-service with your control. Curious how it works?
Head to GitHub, search for OWOX Data Marts, and start your journey to an analyst-first, self-service analytics system today.
Ievgen:
Remember – tools should work for analysts, not against them. They should help and make them more productive.
Control, flexibility, and trust – that’s how you unlock real data value.
Vadym:
And before we wrap this up, I want to take a moment to say something special. Today marks the 30th episode of Season 2, and it’s also the final episode of this season.
This whole podcast started as an idea from Ievgen back in the fall of 2024. We recorded the very first season with just 10 episodes, and now here we are – wrapping up Season 2, which ended up being three times longer.
Ievgen:
Yes, indeed. We started this podcast journey together, and it feels right that we’re closing this season together as well.
I just want to say thank you to everyone who has tuned in, whether on YouTube or your favorite podcast platform. Your support means the world to us.
Keep in mind, OWOX is Where Data Makes Sense. So if you want your data to truly make sense, reach out to us at owox.com – we’ll be happy to give you a hand.
Vadym:
Thank you, Ievgen, once again. Guys, thanks for joining us today!
Please subscribe, leave a comment, and share your favorite data analytics story with us. Hopefully, we’ll see you again in the next season of The Data Crunch Podcast.
Have a great day, and take care!
