A Deep Dive: The Data as a Service Business Model

👋 Hi, it’s CJ Gustafson and welcome to Mostly Metrics, my weekly newsletter where I unpack how the world’s best CFOs and business experts use metrics to make better decisions.

DaaS (Data as a Service) isn’t just SaaS with a CSV upload. It’s the marriage of deep domain expertise, obsessive data hygiene, and tech-savvy packaging.

We’re pulling back the curtain on what it really takes to build and scale a vertical data business.

📦 DaaS ≠ SaaSWhy Data-as-a-Service isn’t just software with a CSV upload—and how the monetization model flips when you sell answers, not features.

🛠 The Challenges of Starting a DaaS BusinessWhy it’s brutally hard to get off the ground, even with AI—and how Masterworks, FINTRX, and others built proprietary datasets from scratch.

🔭 Going Broad vs. NarrowZoomInfo vs. FINTRX: The tradeoff between horizontal reach and vertical depth—and why obsession always wins.

🔄 Building into WorkflowsHow DaaS companies become mission-critical by showing up in CRMs, slide decks, Slack alerts, and GTM plans.

🧬 Rolling Your Own DataWhy licensing other people’s data kills your margins—and how control over freshness, structure, and provenance becomes your moat.

🎯 Figuring Out Your Ideal Customer ProfileSegmentation beyond logos: how to sell by job-to-be-done, not just title or industry.

👥 Hiring for a DaaS ModelWhy data businesses need product-minded analysts, ambiguity-tolerant engineers, and sellers who speak the customer's language.

🧾 Summary + Key TakeawaysThe full cheat sheet: how to build, defend, and scale a vertical DaaS company from first row to last click.

SaaS, even when beautifully built, depends on the quality of your company’s inputs.

The core assumption is that you, the user, bring the content. The software just helps you organize, visualize, and act on it.

DaaS flips that value equation. You’re not just buying the interface—you’re buying the intelligence baked inside. The company is providing the dataset: curated, cleaned, contextualized, and often updated continuously. The tech is just the delivery system.

Russ D’Argento, founder of FINTRX—a DaaS company built for navigating the foggy world of family offices—put it simply:

Think of SaaS as the beautifully designed lunchbox. DaaS is the lunchbox with a steak dinner inside. One is nice to look at. The other feeds you.

Both business models tend to monetize on a recurring basis, using ARR as their financial foundation. But how they structure that revenue is where things diverge.

SaaS typically charges based on usage: user seats, feature tiers, storage, or API access. DaaS, meanwhile, prices based on the value of access. That could mean pricing by dataset size, by user role (e.g. analyst vs. executive), or by frequency of updates. Many DaaS companies also layer in a platform fee—especially when embedding their data into external systems like Salesforce or CRMs—making the product feel more like infrastructure than a nice-to-have tool.

Some DaaS companies also adopt usage-based pricing hybrids: customers pay per lookup, API call, or data export, alongside their base license. Others price by data type or depth—surface-level firmographics might be included, while behavioral or historical insights live behind a premium tier.

Advanced monetization also introduces credit-based systems, where teams allocate internal budgets across departments or workflows. This isn’t just billing logic—it’s product psychology.

That dual-pronged model (platform + data) not only drives higher ACVs—it also offers more pricing durability. Because while SaaS boosts productivity, DaaS delivers intelligence. And intelligence, when it’s unique and timely, becomes a budget line item that doesn’t get cut.

You can’t just throw engineers at the problem.

The DaaS model sounds great in theory—high-margin recurring revenue, strong moats, sticky workflows. But it’s brutally hard to build, especially in the early days.

Unlike SaaS, which can often scale on the back of engineering velocity alone, DaaS requires a slow, deliberate grind up front. You’re not just writing code—you’re crafting a proprietary data asset. That means hiring researchers, building normalization rules, tagging metadata, checking accuracy, and often dealing with data that’s buried in dusty, obscure corners of the internet (or actual file cabinets).

That’s the thing: in DaaS, your data is your product. And collecting it isn’t a passive process. You have to proactively go out and assemble the insights—scrape them, clean them, contextualize them, and structure them in a way that actually makes sense to your end user.

Take Masterworks as an example. They’ve built a business on fractionalizing ownership of fine art. But to do that, they had to aggregate data on decades—sometimes centuries—of art sales. Technically, that data exists. But it might be sitting in an auction house ledger from the 1800s. Or a dusty PDF scan from a 1993 Sotheby’s catalog. Or a literal scroll from ancient Rome.

Just because data exists doesn’t mean it’s usable. And just because it’s public doesn’t mean it’s accessible. That’s the heavy lift. AI can help scale once you have a system in place, but it can’t bootstrap that initial dataset for you—especially not in vertical markets where nuance, domain knowledge, and context matter deeply.

There’s also an emotional weight to building a DaaS business early on. You’re not shipping delightful features every week. You’re not getting dopamine hits from customer love on Twitter. You’re sifting through haystacks, trying to stitch together the first version of a data product that someone might eventually pay for.

But the silver lining? Barrier to entry. Once you’ve done the hard work—once you’ve crawled through those auction records or SEC filings or broker-dealer databases—you’ve built something that’s not easily cloned. That effort becomes your moat. And once customers start building workflows around your data, your product becomes foundational.

You don’t get to be kind of obsessed.

One of the most important strategic choices in building a DaaS business is deciding whether to go wide or go deep.

Horizontal DaaS platforms like ZoomInfo cast a broad net—they index data across a wide array of industries, use cases, and buyer personas. The value is in scale and coverage. You can find just about anyone, but the data might be a mile wide and an inch deep.

On the flip side, you have vertical DaaS companies like FINTRX, which go narrow and deep. They focus on a very specific niche—in this case, family offices—and aim to know everything about it.

When you go vertical, you’re not just building a database—you’re embedding domain knowledge into every data point. That means understanding how your users talk, what decisions they’re trying to make, and what gaps exist in the market. You’re not selling contacts. You’re selling context.

And with context comes pricing power.

The more specific the problem you’re solving, the easier it is for the buyer to tie your data back to ROI. Great DaaS products don’t just sit in dashboards—they show up in financial models. They drive investor targeting, sourcing strategies, sales plans, M&A pipelines.

Because here’s the thing: if you’re solving a high-value problem in a language only your user understands, and you’re the only one with that data? That’s not just a niche—that’s a moat.

And the narrower you go, the more defensible your position—if you layer intelligently.

This focus also creates tighter feedback loops. If you're solving one problem for one type of customer, you learn faster. You get better signal on what to build next.

If your data isn’t used in motion, it gets left on the shelf.

In DaaS, great data isn’t enough. It has to show up in the right place, at the right moment, in the right format. Otherwise, it’s just an expensive fact sheet.

That’s why the best DaaS businesses don’t just sell information—they sell decisions.

You don’t want to be a bookmarked dashboard. You want to be the insight that shows up before a pitch, inside the CRM, or embedded in a memo.

And embedding isn’t just about integrations—it’s about access. The best DaaS companies optimize for data liquidity:

Because in DaaS, usage is everything. If your users aren’t engaging with your product daily, you’re replaceable. The moment your data becomes a daily ritual—not a quarterly report—you stop being a tool and start being a system of record.

You can’t build a differentiated product on someone else’s dataset.

Owning your data gives you:

But even with control, users want to know where the data came from—and why they should trust it. That’s where data provenance comes in:

And what separates good DaaS from great is not just accuracy—it’s freshness. Leading platforms layer in:

The riches are in the niches—and in the use cases.

Early on in a DaaS business, you often start by casting a wide net. You don’t yet know who’s going to love your data the most, or who will actually pay for it. But over time, if you’re listening closely, your best-fit customers start to reveal themselves—not just by who they are, but by what they’re trying to solve.

Russ D’Argento calls this the “use case within the niche” moment. FINTRX didn’t just serve “fund managers.” They started recognizing patterns in how those fund managers used the data. Some were fundraising. Others were focused on distribution. Still others were sourcing M&A deals or recruiting advisors.

In DaaS, segmentation gets powerful when you break it down not just by logos, but by jobs to be done.

Examples:

These might all be using the same underlying dataset—but the value prop, the delivery, and the packaging will look completely different.

This is why the best DaaS businesses evolve from serving a “market” to serving “user buckets.” You’re not just solving problems by industry—you’re solving by intent. And that lets you personalize everything: onboarding, pricing, support, and expansion.

Eventually, this clarity around use cases also feeds your go-to-market strategy. Your SDR doesn’t just say, “We sell data to fund managers.” They say, “We help mid-market fund managers identify family offices that are actively allocating to secondaries.”

Now you’re not just relevant—you’re surgical.

Hiring for DaaS isn’t just about technical skill—it’s about domain fluency.

Building a DaaS company requires a different talent profile than your average SaaS startup. You don’t just need great engineers—you need people who understand the data, the vertical, and why the end user cares.

That starts with product and data teams who know how to make judgment calls. In vertical markets, nuance matters. Understanding how to interpret raw inputs—like SEC filings, broker-dealer registrations, or 13F forms—is just as important as how you visualize them.

In DaaS, there’s a natural tension between engineering and data science. In SaaS, engineers often lead the build—scaling features, shipping code, pushing performance. But in DaaS, data science can’t be a downstream function. It needs to shape the product from day one. These teams aren’t just cleaning inputs—they’re defining what the output means, and whether it’s even usable.

That triad—engineering, data, and domain—is the engine of a good DaaS company. And it only works if those roles are equals at the table.

Your engineers need to be comfortable working in ambiguity. The data might be messy. The spec might be vague. The goal might be a feeling, not a formula. It’s not just code—it’s context.

Even on the GTM side, you want people who can speak the language of the buyer. Someone selling cybersecurity DaaS should understand risk frameworks. Someone selling financial data should know the difference between Series A LPs and family offices with direct investment arms.

In short: domain literacy is a cheat code in DaaS. It shortens onboarding, sharpens the product, and builds trust with users faster.

DaaS is a slow burn—but when it catches, it sticks.

Data-as-a-Service companies aren’t easy to build, and they don’t scale like typical SaaS. But when you do it right, the result is a business with deep moats, high margins, and real staying power.

Here’s the cheat sheet:

At the end of the day, DaaS isn’t just about assembling data. It’s about delivering confidence. Your customers aren’t just logging in to learn something—they’re logging in to know what to do next.

And if you build it right? They’ll be logging in every day. And paying every month (forever).