The Data Crunch · 2025-08-21 · 18 min
Key moments - from our scoring
Substance score
22 / 100
Five dimensions, 20 points each
Vadim and Yevgen from Oax explore why data analysts express frustration with SaaS tools, shifting the narrative from supposed technophobia to legitimate operational and governance concerns. The hosts dissect five concrete problems: corporate IT policies that ban third-party cloud tools, procurement delays that can stretch months while business needs shift, SaaS platforms designed for business users that strip analysts of control over data pipelines and metric accuracy (using Looker Studio as an example of dashboard sprawl), pricing models like Supermetrics that penalize scale, and architectural lock-in that prevents customization. They advocate for self-managed solutions running on private infrastructure - including their own Oax datamarts on GitHub, alongside tools like Airbyte, DBT, and Matomo - that let analysts govern their workflows, debug independently, and scale sustainably without renegotiating budgets. The conversation is valuable for data leaders, procurement decision-makers, and anyone building data infrastructure at enterprises struggling with tool adoption and analyst retention.
Analysts don't hate the tools themselves; they hate the control loss, slow approval processes, punitive pricing that scales with usage, and vendor lock-in that forces them to wait on vendor roadmaps for needed features or customizations they could implement independently.
Flat, usage-based, or freemium models - where tools are free forever for most users or charge fairly as consumption grows - rather than per-connector or per-row pricing that penalizes scale and forces data teams to cut costs instead of driving value.
Using self-managed tools hosted on private infrastructure (local laptops, company servers, or private cloud with Docker) rather than external SaaS clouds - these still deliver functionality while ticking compliance and security boxes.
Vendor lock-in occurs when SaaS tools make it impossible to customize or access features without waiting on the vendor's roadmap; analysts become dependent rather than able to debug, modify, or integrate the tool independently.
Open-source and self-managed tools like Oax datamarts, Airbyte, DBT, and Matomo give analysts control, transparency, and the ability to customize, debug, and scale without relying on vendor roadmaps or punitive pricing.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode names five real pain points (corporate restrictions, procurement lag, data ownership loss, punitive pricing, vendor lock-in) but all are well-known grievances in the data space. There is virtually no non-obvious insight per minute; the content reads as a checklist any data analyst would already recognise, with surface-level treatment of each.
Data analysts do not hate tools. They hate tools that take away the control over the processes that they are responsible for.
By the time the tool is approved, the problem it was meant to solve has either changed or been patched manually 10 different ways.
Every argument here - bureaucratic procurement, opaque SaaS black boxes, Supermetrics-style punitive pricing, vendor lock-in - is a recycled industry talking point. The conclusion ('use open source/self-managed tools') is the standard counter-narrative with no novel framing, no contrarian angle, and no first-principles reasoning.
open source options like Airbyte, Matomo and of course Oax datamarts
do not rely blindly on vendor black boxes like supermetrics
Both speakers are marketers (a growth marketing manager and a head of marketing) at the same company whose product is being promoted throughout. There is no external practitioner, no data analyst or analytics engineering leader who has actually built systems at scale. This is internal branded content, not expert testimony.
I'm Vadim, um, growth marketing manager at Oax
Joining me for this one is Yevgen, our head of marketing here at Ox
A handful of named tools (Supermetrics, Looker Studio, DBT, Airbyte, Matomo) and one reasonably concrete anecdote about SQL query duplication in Looker Studio give the episode some grounding, but there are zero data points, no named companies (other than Ovox itself), no dollar figures beyond a vague '$50 per month tool', and anecdotes are anonymous and underdeveloped.
Supermetrics is a great example of this kind of pricing model
Someone from the same marketing team that helped yesterday decided to use ChatGPT to edit your query. And you know that happens all the time
The exchange is clearly scripted and coordinated between two colleagues; every host question is a leading prompt that hands the floor back for product promotion. There is no pushback, no genuine follow-up, and no moment of productive disagreement anywhere in the 18 minutes.
And the solution, well the first solution that comes in my mind is using the self managed tools
Awesome. I love you. Again, how in depth you were describing
Computed from the transcript - who did the talking, and the words that came up most.
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 Contribute to our open-source connectors on GitHub Get trusted reports with your context in 1 minute OWOX Website Analytics with OWOX BI YouTube Channel
Transcribed and scored by The B2B Podcast Index.
Speaker A: M. Foreign.
Speaker B: Hey, everyone. Welcome back to the Data Crunch podcast. I'm Vadim, um, growth marketing manager at Oax, 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 tools? Or is there a bigger story that nobody talks about? 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 Yevgen, our head of marketing here at Ox, and someone who's seen this struggle from every angle. Evgen, welcome back to the show.
Speaker A: Thanks, Vadim. Always a pleasure being here. I've missed the datacrunch podcast since we started running it together. And as of the topic, yes, I've seen analysts get burned by the 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 the lives of of data analysts harder.
Speaker B: Yeah, exactly. And right 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. All right, let's get into why this topic matters so much. I want to kick things 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 have automated a big chunk of her reporting by the time it cleared. Procurement leadership has already changed priorities. The tool is now unused, and she went back to manual. CSVs. Yevgen, you've heard similar stories, right?
Speaker A: Absolutely. Um, I remember one kind of huge enterprise company where data analysts had to write Python scripts because the approved SaaS, uh, solution was too locked down. And you know, Python is not the language of data analysts. They typically know JavaScript or that kind of stuff. But Python is more for data engineers. And you know the tool that they were using, the one that was approved, it looked great on paper. It looks great in the sales demo pitch. But that pitch was done to someone responsible for the procurement because that's a huge enterprise. But in practice, basically it slowed everyone down and created more manual work than it should be. And that's why this topic matters. Data analysts do not hate tools. They hate tools that take away the control over the processes that they are responsible for. Uh, the ones that delay their Work were basically cost a fortune for nothing.
Speaker B: Yeah, totally agree. Before we break down the reasons, let's kill the biggest analysts aren't anti SaaS or resistant to change, right?
Speaker A: Sure. They are not dinosaurs writing SQL just for fun. Like data analysts like love good tools. The ones that give them enough flexibility, enough speed, but more importantly the one that gives them trust in the data. What they hate is bureaucracy around the black box tools and basically losing the ownership of their uh, workflows. The ones that they are responsible for.
Speaker B: All right, let's start with the reason number one. Corporate restrictions. Some companies outright ban third party SaaS tools because of strict internal security policies. What do you say about that?
Speaker A: Yeah, it's like asking a carpenter to build a house without power tools. Data analysts end up stuck exporting CSV files for a reason. That's not because they love doing it, that's because the IT teams do not want data in the external tools. Even if they are secure, even if they have the SoC2 where those tools are kind of industry standard.
Speaker B: And the solution, well the first solution
Speaker A: that comes in my mind is using the self managed tools hosted on the private infrastructure, either on your local PC or Mac or on your company server or anywhere else, but basically not on the SAS tool cloud. So those kind of tools, they typically tick the compliance boxes easily while giving the data teams the functionality they need. Maybe slightly less, but again that's what's important. So those kind of tools could be stored on on the Mac and run in Terminal on the Windows laptop. On Linux, you can run it in uh, basically any famous cloud. You can use Docker to install it. That's kind of how it works. And by the way, if you're curious about how this works in practice, about any of the examples of the analytics tools that are self managed, you can check out Ovox Data mart. It's on GitHub. We will add a link into the description of this video and it's a great example of a self managed analytics tool that keeps both IT and data analysts kind of happy.
Speaker B: I love that. Let's uh, get to the reason number two. Even if security says yes, procurement can take forever. A typical data analyst doesn't control budgets and approvals can drag on for months.
Speaker A: Exactly. By the time the tool is approved, the problem it was meant to solve has either changed or been patched manually 10 different ways. Data analysts waste time chasing signatures instead of solving real business problems, bringing insights. Sometimes it's really about money. If you want to buy something expensive or that can scale to Be expensive, but sometimes it just takes forever to get approval for a $50 per month tool that the whole organization would benefit from. But you just cannot get somebody to get that line on their budget. And actually typically data folks go to marketing departments because they kinda need data all the time. They have the budget for some tools and that really works. We have a lot of customers whose marketing team is paying the bill for the data warehouse because they are the ones that need that the most.
Speaker B: All right, thanks for sharing about reason number two. So let's go over to the third reason which hits home for a lot of analysts. I believe so, which is losing control of the data.
Speaker A: Yeah, many software as a service tools are designed for business leaders, for, for business users, not for data teams. Just because they are designed for those who pay money. And I totally get them. They prioritize easy access, beautiful design for everyone. But strip away the ability to govern the logic to control the data pipelines or ensure the metrics accuracy. Those points that are important for data analysts and the ones that they are responsible for. So data teams become babysitters of broken dashboards. They are responsible for numbers that they cannot fully control. The classic example here 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 click the share button. So someone else from the marketing, sales, any other team can then go and build a dashboard on top of your query so they can add more filters, change the charts, add more columns to the tables. So that's basically easy to do in Looker Studio, right? But then one day and three months after that that you realize that that SQL that you created is all over the place because that dashboard was duplicated a couple times, something was changed, tweaked. Someone from the same marketing team that helped yesterday decided to use ChatGPT to edit your query. And you know that happens all the time and it's your problem again. It's frustrating and risky.
Speaker B: Yes, I totally agree with you here Evgen. Um, so we talked about the procurement, but sometimes there is a money problem, which is reason number four on our list. A lot of SaaS tools, especially in the data connectivity world, they charge per connector, per row or per user. What do you say about this?
Speaker A: It's typically way worse. They do not just charge you per connector or perot, they fine you for using the product because at some point instead of paying less for using more, you start paying 10x more because now you're kind of an enterprise. Well, Supermetrics is a great example of this kind of pricing model which means as your data volume or data needs grow, you are suddenly the the expensive department. Data analysts are forced to cut costs all the time. Like I hear that from almost anyone in the data field. And they are doing this instead of driving more value just because the pricing models weren't built for data teams or the data first teams, not for the ones that want to bring more insights to the business. Flat ownership based pricing models that are used in most of the open source tools that are either free forever as most of the OVACS data marts editions. You won't pay a penny if you don't have uh, pretty much enterprise needs or the ones that are free plus something as an add on or the ones that charge for real usage. And the more you use, the more you consume, the less you pay. And those tools let data teams scale reporting without having to beg for budget every quarter.
Speaker B: Awesome. Uh, I like how we're going about this. So let's go to the last but not the least reason. Number five, Vendor lock in.
Speaker A: Yeah, just uh, some tools make it almost impossible to customize what you can do. Even if there is something small to change, something to tweak, like adding a field from the platform API that everyone is using from the platform interface but that you cannot get it inside the report. Um, like as an example, imagine the column CPC or the metric CPC is not available for Facebook ads reporting across the tools, but it's available on the Facebook ads interface. So data teams become dependent on the vendor's roadmap, waiting months for features or fixes that they desperately need right now, maybe yesterday, and that they would code themselves in minutes if they have an ability to. So with open source architecture and self managed tools, data analysts finally can stay in control and actually make the tweaks that they need. They can debug, customize and actually trust their workflows. Look, that's kind of the reason why we uh, built our connectors from advertising platforms to Google Sheets inside Google's apps script. Like we've built the templates that are run inside the same Google sheets that you configured. So you basically add some data, uh, add the access token inside the document property of your document, not ours. And that's how it works. Like you tweak it, you make the changes. If this connector doesn't suit your needs right now, you can go and check the code, you can make tweaks there you can use ChatGPT to help you. You can create another connector, you can add some more endpoints of the platform API. That all is available to you.
Speaker B: Awesome. I love you. Again, how in depth you were describing, uh, all the reasons we just talked about all five reasons. So what kind of tools do analysts actually love?
Speaker A: I would say that data analysts typically love the tools that give them control, freedom and transparency. So open source options like Airbyte, Matomo and of course Oax datamarts, which is both for data connectivity and data enablement. Not only connectors. Again, you can use it free forever using the link in the description below or by finding us on um, GitHub. Then there is DBT for data transformations. There are plenty of open source tools. Some of them are free forever, some of them are not that free, but still useful.
Speaker B: Awesome. So let's wrap this list with some advice for listeners stuck in this SaaS nightmare.
Speaker A: So I'll probably have two DOs here and two DO NOTs here. So let's start with the do. Start small with self managed tools that you can use on your laptop or on your company server to avoid any bureaucracy. The next do would be push for the tools that respect the privacy. Then I would say do not rely blindly on vendor black boxes like supermetrics. Know what's under the hood, how it works, what you can change and where your data ends up, in which hands your data, uh, ends up. And finally, do not let pricing dictate the data strategy in your organization. Choose the tools that scale sustainably.
Speaker B: All right, so if we sum it up, analysts don't hate SaaS tools. They had red tape, loss of control, um, punitive pricing and vendor lock in.
Speaker A: Sure. So looking back to the title of this episode of the podcast Daedalus, do not hate SaaS tools. Data analysts deserve the tools that they can trust, control and scale with together. And they will be the first ones advocating for innovation. The future of analytics isn't about replacing analysts with SAs tools or AI. It's about empowering data teams with the right tools so they can scale their workflows and deliver more insights at the end of the day.
Speaker B: And if you're listening to this thinking, we need better tools that don't lock analysts out. Check out OX datamarks. 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 OACs data marts and start your journey to an analyst first self service analytics system today.
Speaker A: Remember, the tool should work for data analysts, not against them. They should help and make them more productive, deliver more insights, save control, flexibility and trust. That's how you unlock the real value.
Speaker B: And before we wrap this up, um, I want to take a moment to say something special. Today marks the 30th episode of season two, and it's also the final episode of this season of, uh, our datacrunch podcast. This whole podcast started with an idea from Yevgen back in the fall of 2024. We recorded the first season with just 10 episodes and now here we are wrapping up season two, which ended up being three times longer.
Speaker A: Yeah, indeed. We started this podcast journey together and it feels right that we are closing this season together as well. I just wanted to say thank you to everyone who has tuned in today, yesterday, and in any of the previous episodes, whether on YouTube or on your favorite podcasting platform. Your support means the world to us. Keep in mind, OVOX is where data really makes sense. So if you want your data to truly make sense, reach out to us@ovox.com, we'll be happy to give you a hand.
Speaker B: Yes, thank you, Evgen. Once again, guys, thank you for joining us today and for listening. Please subscribe, leave us a comment, and share your favorite data analytics story with us. And hopefully we'll see you again in the next season of the Data Crunch Podcast. Have a great day and take care of.
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