All the Data! Reporting & Analytics, and the Coming Battle for Data Gravity
Data is the next battleground between Vertical SaaS control point incumbents and their AI-powered challengers. And the fight won’t just happen in the backend—it will play out in dashboards and reports, the often-overlooked windows into a business’s health. Own the reporting layer, and you don’t just provide insights––you secure account control, creating a launchpad for expansion and cross-sells. But here’s the catch: Generative AI is changing the game. It’s making data integration effortless, normalizing disparate sources, and enabling natural language-driven analysis—no SQL sorcery or BI expertise required. If you don’t own reporting, AI will. It will start small, offering sleek, standalone reports, then quietly pull in more and more data until—surprise!—it has the most comprehensive view of your customer’s business. Classic “integrate and surround.” And once that happens, your control slips. The time to act is now—before AI gets its foot in the door.
To be fair, dashboards are nothing new. I spent a summer as a junior woodchuck MBA in the SAP Office of the Chairman. At the time, SAP was scared sh*tless about becoming the “system of ledger” in the back closet. It’s like the system of record where everything is stored and extracted, but rarely used. Over time, Hasso (SAP co-founder and then chairman) rightly worried that SAP would lose account ownership and the ability to cross-sell. Their answer? Hire IDEO to bring the UI into the 2000s (I am old) and look at buying a BI tool. My summer project involved creating many slides—which were rated substandard—to make the argument. They ended up buying Business Objects in 2007 for $6.78 billion.
Today, history is rhyming. SAP’s fear (and opportunity) in the early 2000s is now coming for VSaaS companies. This time, though, the wave isn’t cloud computing–it’s generative AI. A new wave opens new profit pools and new vectors for attack. Users can interact with your software differently through chat and voice interfaces, data previously locked up in PDFs is suddenly freed, and the opportunity is greenfield. VSaaS companies can either grow or be relegated to becoming the system of ledger that GenerativeAI applications use to pull their data from.
Historically, reporting is an afterthought, something you tack on after the “real product” is built. VSaaS CEOs should shift their thinking. Analytics is the start of the journey to increase application value. This happens by Hoovering up “all the data.”
Not only normalizing, transforming, and expressing their existing data in ways that drive decisions and impact, but gobbling up all the data around them. They need to shut the door on AI companies, and become the single source of truth for their merchants. It starts with reporting and analytics that can materially change how a merchant runs their business. The shortest path to get there is through the lowly dashboards. But that’s a gateway to key performance indicators, benchmarks, decision analytics, and much, much more.
Data Gravity and Control Points
We talk a lot about data gravity’s role in establishing a control point. As a VSaaS company, you want to own both the most important data and the most data because it increases your ease of going multi-product. The strongest version of this is becoming the single source of truth where you are the gold standard––the cleanest version of the data that your merchant relies on for all crucial business operations.
Data gravity is powerful because data compounds: data that is free of duplicates, well-integrated, and normalized can enrich itself.
Data gravity is also powerful because it can create engagement with the most powerful users at a merchant. Company wide dashboards are mostly the parlance of founders and C-Suite executives. When data is expressed in a UI or workflow, it can help the merchant make the right decision, understand their business, and run the business better. The merchant users who most value the data are often the owner or operations lead—the exact personas who make decisions on the next great product you are selling.
Most VSaaS companies whiff on this opportunity. Their reporting and analytics are an afterthought, or worse, they cede control of integration to Power BI/Tableau.
Why do they whiff? One is a focus on the workflow––help the merchant “do” vs “analyze.” That makes sense initially. Another reason: reporting can be challenging. Extracting and visualizing your own data is tricky enough. But integrating into other systems historically requires development work around deduping and normalizing data sets that is both challenging and not hugely popular with product teams. This requires up front development work from the VSaaS company, and even more challenging––getting the merchant on board.
Generative AI
Generative AI makes this easier. The opportunity we’ve seen is using AI in the creation of what we’re calling an “Operational Data Layer.” AI can harmonize disparate tools and data sources, translating information from one context to another without requiring companies to give up the tools they depend on. There is great potential to bridge gaps in terminology and function, awaken data that was hidden before, and connect related concepts like "release" and "launch" that might otherwise seem disjointed.
Put together, this means that data acquisition can go from a brute force effort of multiple years, to potentially something much easier.
Why is this powerful? AI simplifies the “data storytelling” process and enhances the overall effectiveness of enterprise software. This makes data more accessible and actionable, which poses serious implications for the idea of data gravity.
Generative AI opens opportunity, but you can't sleep on it—you need to act before native AIs jump in. While you’re busy optimizing workflows, AI-native companies are reimagining how merchants interact with their data, crafting sleek visualizations and intuitive engagement models that reshape how businesses understand themselves. And they’re doing it with your data. Worse yet, they’re layering in third-party data, creating a powerful network effect where 1 + 1 suddenly equals 5. Before you know it, these AI solutions become indispensable, and you can’t kick them out. Your customers will demand deeper, read-write integrations—and once that happens, your platform isn’t the control point anymore. You’re SAP–a system of record in the back closet.
By the way, the threat won’t just come from slick AI-powered reporting tools—it will sneak in through seemingly flimsy transcription apps, lightweight assistants, and deceptively simple tools. These AI-native apps win by giving users exactly what they want: effortless tools that make their lives easier. At first, they enter through side doors––Chrome extensions, RPA, or other lightweight integrations. But give it a year, and what once seemed trivial becomes indispensable. The tools get better, adoption skyrockets, and suddenly, your users are in them more often than they check TikTok. Before you know it, they’re not just the system of engagement––they’re triggering actions, shaping workflows, and inching their way into becoming the system of record. And, of course, you can’t block them––your users would revolt.
All the Data
Don’t be SAP! You shouldn’t hire me as your intern and you should also be more aggressive than they were.
Build reporting and dashboarding––an insight layer that heads native AI entrants off at the pass. Use those applications to build an integration layer to hoover up All the Data and lock down your role as the single source of the truth!
Here are some thoughts on how:
1) Answers not queries. “Operational data layer please,” said no small business owner ever! Think through the most important personas within your customer and their most important questions. Build products (reports, dashboards, analytics) to answer those questions over offering a “new analytics product.”
Some common winning questions include:
- Who or what is my most profitable product, customer, employee?
- Am I growing faster than my competitors?
- Should I raise or lower prices?
Keep looking for that right question. You’ll know you’ve found it when your customers’ eyes bulge.
2) Our private equity overlords. Don’t just think of the owner, think of the owner’s owner! Private equity is transforming most industries, and metrics are catnip to private equity bros.
Private equity firms will implement your software across their entire portfolio if you offer valuable insights. Further, your metrics could mean the difference between a merchant getting acquired and walking away with a dinghy or a yacht!
For example, our portfolio company Karbon’s product “Practice Intelligence” allows accounting firms to utilize either pre-built templates specialized to their workflows or easily construct dashboards using their existing data schema. These same dashboards that help the accountant best run their firm, are also likely the same ones that private equity buyers would use to evaluate the health of that company as a target. And for those PE firms acquiring accounting firms, Karbon Practice Intelligence will unify data across a portfolio to identify top-performing segments and set performance benchmarks.
Private equity is consolidating and transforming many of the industries we serve. In fact, some of our portfolio companies secured up to 20% of their new bookings in 2024 through private equity firms. It’s such an important factor that we’re hosting our first VSaaS Collective session of 2025 on it. Join us on Tuesday, February 25th, from 10:00am to 11:30am PST via Zoom. Space is limited to keep the conversation productive. Register your interest here.
3) Invest in your infrastructure. Your infrastructure was built for operational workflows, not data aggregation. To build a successful analytics product, you’ll need to set aside sufficient resources to refactor the infrastructure to support “All the Data.” Focus not only on data scalability, but also on the data schema itself. Get the schema wrong and your queries will run like molasses. Get it really wrong, and you won’t be able to access the right data to get to the answer. Generative AI is making this easier, but assume it’s an iterative process.
Create flexibility in both the UI and how customers engage with data. Some will want just the standardized reports. Others may want ad hoc query capabilities, while some will want analytics embedded into the workflow. And of course, AI is providing more intuitive ways to engage.
4) Use off-the-shelf frameworks: don’t get caught up reinventing the wheel. Building your own analytics system might seem straightforward at first, but it's a deceptively steep climb. The initial step might take just a few engineering hours. But as user needs grow, you’ll quickly face an endless list of design and technical challenges. Suddenly, you’re building a fully-fledged BI system––one that can cost millions of dollars annually and requires a dedicated team. Unless analytics is a strategic priority, this effort often falls apart––delivering subpar results.
Instead of reinventing the wheel, lean on embedded frameworks. Embedded analytics refers to integrating interactive visualizations and reports into an application to answer your customers' questions directly. Instead of building the full BI capabilities in-house, you can use APIs and iframes that provide some flexibility to customize the analytics UI to your customers' needs.
5) Bring in data from other software systems and 3rd party sources. Your goal is to create two-way integrations with other data sources to build a data network effect. Start with point solution systems––they’ve likely been begging to partner with you and will do the hard work to integrate into your taxonomy. You’ll also want to consider industry data sources that might significantly enrich your data. Finally, consider integrating data from other control points so that your reporting application can act as a ‘report of reports’, ‘manager of managers’.
So, What’s Next?
If you’ve followed these steps, you’ve solidified your data gravity and become not only the single source of the truth, but the hub of all the data. Each data source you integrate makes all the other data more valuable. The network effect is in motion, and soon your reports, analytics, benchmarks are as addictive as TikTok for your customers’ most senior personas––and perhaps even their PE overlords. Now that you have all the data you need, what’s next?
This is the fun (and profitable) part. You can start creating your own industry-defined metrics––e.g., CAC payback, Revpar in hotels, GRP in media––that can become currency for you.
You can embed analytics into workflows to help guide decisions and trigger downstream workflows. If done properly, you can move these analytics reports into analytics action engines that have humans in the loop––with decreasing involvement over time––to make decisions and trigger actions.
Data network effects don’t stop at a merchant’s four walls. The ultimate opportunity is to aggregate and normalize data not only from your merchant customers, but also from their partners, suppliers, customers, and employees. We call this an ‘Industry Ledger,’ in which a VSV builds a control point that spans stakeholders, unlocks new TAM, creates unrivaled data gravity, and dramatically digitizes an industry–transforming the consumer experience. It’s a pathway to becoming an Industry Platform.
That’s next on the Vertical SaaS Knowledge Project––stay tuned.
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