It's time to ask better questions
Allan WillePublished 2026-07-03Summary: The analytics industry is going through its biggest shift in twenty years, and growing businesses are better positioned than most to benefit. This post explores how analytics has evolved from building dashboards to understanding the business, why AI raises the value of good metric foundations rather than replacing them, and what smaller companies should build now to make confident, trusted decisions.
The biggest shift in analytics in twenty years is happening right now — and if you run a growing business, that's genuinely good news.
For most of the last two decades, analytics was something you built. You built dashboards. You built reports. You built pipelines and data models. The tools were expensive, the expertise was scarce, and the results were mostly reserved for companies with dedicated data teams and enterprise budgets.
That era is ending.
We're entering a moment where more people than ever can ask meaningful questions about their business — and actually get answers. Not because they hired a data scientist, but because the tools have finally caught up with the need.
Let's explore what that means, why it matters, and what you should do about it.
From building analytics to understanding your business
There's a subtle but important distinction happening in the analytics world right now.
For twenty years, the industry competed on who could build the best dashboard. Cleaner visualizations. Faster load times. More chart types. The dashboard was the product.
That's changing.
The next decade will be defined by something different: helping people understand what the numbers mean, trust those numbers, ask better questions, and make better decisions. The dashboard becomes one possible interface — not the destination.
That shift sounds small. It isn't.
It means the value of analytics is no longer locked inside a report. It lives in the conversation you have with your data, the confidence you feel when you share a number with your team, and the clarity you gain when a trend finally makes sense.
We're moving from an era of "building analytics" to an era of "understanding the business."
For growing businesses, that's a fundamentally different — and better — relationship with data.
Every generation of analytics solved one problem and created another
It helps to zoom out and look at how we got here.
The first generation of business intelligence centralized reporting. Before that, every department had its own spreadsheets, its own numbers, its own version of the truth. Centralizing data was a genuine breakthrough.
The second generation made dashboards beautiful and accessible. Suddenly, business leaders could see their data without writing SQL.
The third generation brought self-serve analytics. The promise was that anyone could explore data, not just analysts.
Now we're entering a fourth generation, where AI makes asking questions almost effortless. You type a question in plain language and get an answer. No SQL. No dashboard configuration. No waiting for a report.
That's genuine progress.
But here's the pattern: every leap forward exposed the next bottleneck.
When dashboards became easy to build, organizations realized they couldn't agree on what their metrics meant. When self-service became possible, people discovered they didn't know which report to trust. Now that AI can answer almost any question, we're discovering something even more important.
AI can only be as trustworthy as the numbers underneath it.
The question that actually matters now
The conversation in analytics is finally shifting.
It used to be: Can we get this data into a dashboard?
Then it became: Can AI answer this question?
The question that matters now is: Should I trust this answer?
That's a much better question. And it's one that smaller, more agile businesses are actually well-positioned to answer — if they build the right foundations.
Ask three people at your company what "revenue," "customer," or "active user" means. You may still get three different answers. Adding AI to that situation doesn't resolve the disagreement. It just surfaces conflicting data definitions faster and with more confidence.
The organizations that thrive in this next era won't necessarily be the ones with the most sophisticated AI. They'll be the ones that invest in metric governance, shared definitions, and business context that both humans and AI can understand.
AI raises the value of good analytics foundations. It doesn't replace them.
Why growing businesses have an unexpected advantage
For years, enterprise companies had access to analytics capabilities that smaller organizations simply couldn't afford. Dedicated data teams. Expensive BI licences. Custom data warehouses. The gap felt permanent.
It isn't anymore.
Modern analytics platforms now connect directly to cloud applications, calculate business metrics automatically, and use AI to explain trends, surface anomalies, and answer questions in plain language. The cost of getting started has dropped dramatically. So has the complexity.
But there's another advantage that's easy to overlook.
Smaller, growing companies aren't burdened by decades of legacy reports, multiple BI platforms layered on top of each other, and conflicting definitions baked into systems no one fully understands anymore. They can build on modern foundations from day one.
Sometimes being second is an advantage. You skip the mistakes of the first generation and build something cleaner.
What you should do: Resist the urge to replicate what enterprise analytics looks like. You don't need a hundred dashboards. You need a small set of trusted metrics, clearly defined, consistently calculated, and accessible to the people making decisions.
New categories are emerging — and that's worth paying attention to
The last twenty years were dominated by dashboard tools. The next decade probably won't be.
We're already seeing the early edges of entirely new categories:
- Metric-centric analytics instead of report-centric analytics — where the metric itself carries its definition, context, and history
- Semantic layers becoming first-class products, not just a layer hidden inside a BI tool
- AI-native analytics experiences where conversation and exploration replace static reports
- Analytics embedded in operational workflows instead of separate reporting portals people have to remember to visit
- Agentic systems that don't just answer questions — they monitor, explain, and recommend actions
Some of today's products will evolve to meet this moment. Others won't. Companies we haven't heard of yet will almost certainly define the next generation.
That's how technology works. And it's a healthy sign that an industry is still innovating rather than coasting.
What you should do: When evaluating any analytics tool, ask whether it's built for where analytics is going — not just where it's been. Does it treat metrics as structured, defined objects? Does it give AI the business context it needs to produce trustworthy answers? Can your team use it without a data analyst standing by?
The goal has never been dashboards
It's worth saying plainly: the goal of analytics has never really been dashboards.
The goal has always been helping people make better decisions.
Dashboards were a means to that end — a good one, for a long time. But they were never the destination. The destination is confidence. Clarity. The ability to look at a number, trust it, and act on it.
If AI, better metric governance, and new product categories get us closer to that outcome, then all the disruption happening in the analytics market right now is worth it.
Growing businesses that recognize this moment — and build the right foundations now — won't just keep up with the changes ahead. They'll be better positioned than companies that have been doing this for decades.
The best time to start asking better questions about your business is right now. The tools are finally ready to help you answer them.