About ten minutes into our call, he said something that stopped me cold:
"Millions of dashboards. Nobody can tell you why anything happened."
That one sentence captures the dirty secret of enterprise analytics better than anything I've heard in years. And it's coming from someone who lived inside the machine, not an outsider throwing stones.
The Dashboard Paradox Nobody Talks About
Here's what's fascinating. This isn't a person who lacks technical sophistication. He came up through quantitative modeling. He understands the math. He's been involved in evaluating and acquiring analytics companies for the better part of his career. And yet, when he looks at the current state of business intelligence across the organizations he's worked with and advised, his assessment is blunt: the tools exist, the dashboards are everywhere, and the actual understanding is missing.
That paradox is something we hear in almost every conversation at Scoop, but it hits differently when it comes from someone who has operated at the highest levels of enterprise strategy. He described how corporate strategy teams at large organizations piece together their view of the business by collecting spreadsheets from dozens of different people,
"a spreadsheet from this person and a spreadsheet from that person, cobbling it all together."
No single system gives them the picture. No tool answers the why.
Think about that for a second. We're talking about companies with billion-dollar technology budgets, dedicated data teams, and every major BI platform money can buy. And the people making the most consequential strategic decisions are still stitching together Excel files like it's 2005.
What "Self-Service Analytics" Actually Means (And Why We Keep Getting It Wrong)
The conversation took an interesting turn when we started talking about self-service analytics. He made an observation that I think is genuinely important: the promise of self-service analytics is where every major enterprise wants to be. But the industry keeps defining self-service as
"here's a dashboard you can click on after IT spent six months building it."
That's not self-service. That's a menu at a restaurant where someone else picked every dish.
Real self-service analytics means a business user can sit down, ask a question in plain language, and get a meaningful answer (including the why) without filing a ticket, waiting three weeks, or learning a query language. The technology to do that didn't exist five years ago. It barely existed two years ago. But between advances in AI reasoning, machine learning explainability, and natural language interfaces, we're finally at a point where self-service analytics can mean what it was always supposed to mean.
What struck me about this conversation is that he wasn't theorizing. He's about to step into a leadership role at a fast-growing company in the entertainment production space, an organization that's scaled to several hundred million in revenue and is on a trajectory toward a billion. And the analytics infrastructure? His word was
"cobbled together."
Power BI dashboards exist, but the sophistication isn't there. The self-service capability isn't there. The ability to ask why did this project go over budget or what's driving margin compression in this division simply doesn't exist without a person manually digging through data.
His advice to the company leadership was refreshingly direct:
"Save yourself the trouble of building a 100-person AI organization. If you really want to embrace self-service analytics, there are tools that can get you there without that kind of investment."
The Enterprise Analytics Gap Is Bigger Than Anyone Admits
Here's the market insight that I keep turning over in my head from this conversation. This person has been involved in hundreds of software acquisitions at the enterprise level. He understands what large organizations look for when they're trying to solve capability gaps. And his observation (delivered casually, almost as an aside) was that the gap between what BI tools deliver and what business users actually need is "in spades" across the enterprise space.
Not in pockets. Not in edge cases. In spades. Everywhere.
The companies that have Tableau have hundreds of dashboards nobody can interpret. The companies that have Power BI have a handful of people who know how to build anything useful with it, and everyone else is exporting to Excel. The companies that have invested millions in data warehouses and analytics infrastructure still have strategy teams manually assembling spreadsheets from different departments because no single tool gives them investigative capability.
And this is where I think the industry conversation needs to shift. We've spent twenty years talking about dashboards, visualizations, and data democratization. Those are all fine things. But they solve the what happened problem. They don't solve the why did it happen problem. And the why is where every important business decision actually lives.
Why AI Changes the Equation (But Not the Way Most Vendors Claim)
He brought up something that I hear constantly from sophisticated buyers: the concern that the big platform vendors (Salesforce, Microsoft, the usual suspects) will just bolt AI onto their existing products and close the gap. It's a reasonable concern. These companies have massive install bases and enormous R&D budgets.
But here's the reality. Bolting a language model onto a dashboard tool doesn't solve the fundamental problem. If the system doesn't understand your business context; your terminology, your metrics, your analytical frameworks, your domain-specific way of thinking about problems. Then all you get is a slightly fancier way to run a single query. You still can't investigate. You still can't explore multiple hypotheses. You still can't get from what happened to why it happened and what should we do about it.
That's not a criticism of those platforms. They're excellent at what they were built to do. But the investigation layer, the ability to encode domain expertise and let AI reason across your data the way your best analyst would, that's a fundamentally different architectural problem. You can't retrofit it onto a visualization tool any more than you can turn a reporting engine into a data scientist by adding a chat box.
What This Means for the Next Chapter of Analytics
I walked away from this conversation thinking about something he said toward the end:
"The opportunity here is huge."
He wasn't talking about any one company or product. He was talking about the gap itself, the distance between where enterprise analytics is today and where it needs to be for organizations to actually become data-driven in a meaningful way.
Self-service analytics has been a promise for over a decade. The dashboards got prettier. The connectors got more numerous. The data got bigger. But the fundamental experience for a business user trying to understand their data hasn't changed nearly enough. They're still waiting for someone else to build the view they need. They're still exporting to spreadsheets to do the real analysis. They're still guessing at the why.
The organizations that figure out how to close that gap, not with more dashboards, but with genuine investigative capability that business users can access independently, are going to have a massive advantage. Not just in speed, but in the quality of decisions being made at every level.
That's what we're building at Scoop. And conversations like this one remind me that the problem we're solving isn't niche. It's the central unresolved challenge of modern business intelligence.
If you're living this reality (surrounded by dashboards but starving for answers) I'd love to hear your story. Sometimes the most important step is just acknowledging that the current approach isn't working, and that there might be a better way.
