Beyond the Dashboard: The Three Levels of Data-Driven Decision Making

The global business intelligence market will reach $72.1 billion in spending over the next 12 months, yet fundamental adoption challenges persist. According to Forrester Research, only about 20% of business users can independently fulfill their own BI requirements – despite a decade of “user-friendly” and “intuitive” features from BI vendors. That’s a staggering disconnect – billions invested in visibility, but insights remain locked behind technical barriers for 80% of decision-makers.

The problem isn’t the technology. It’s that we’ve been using the wrong tool for the wrong job. And now we have a better technology that allows business users to capture insights and predict the future of their business based on their data intuitively.

Dashboards were never meant to democratize data – they were meant to standardize it.

Think of organizational data maturity like physical fitness. At the first level, everyone needs to track basic health metrics – weight, blood pressure, heart rate. These are your dashboard KPIs. They’re essential, standardized, and everyone in the organization needs to see the same numbers.

But here’s where most companies stop. They build elaborate dashboards for everything, treating every business question as if it needs a pre-built visualization. It’s like expecting every fitness goal to be solved by stepping on a scale.

The Three Levels Nobody Talks About

Last month, a CFO at a mid-market chemicals company told me something revealing: “We have 47 dashboards. I look at maybe 3 regularly. The rest sit there because someone once thought they’d be useful.”

This is the dashboard trap. Companies confuse visibility with capability.

Here’s what data maturity actually looks like:

Level 1: Dashboard Monitoring – Track your organizational vital signs. Revenue, margins, production efficiency, safety metrics. These are non-negotiable, standardized KPIs that everyone needs to see. Dashboards excel here. They’re your bathroom scale – simple, consistent, essential.

But dashboards fail the moment you want to explore beyond the pre-built view. What happens when you ask: “Why did our product perform differently in the Northern region last quarter compared to the South?” The dashboard shows you the what. It can’t help you explore the why.

Level 2: Conversational Data Exploration – This is where “Talk to Your Data” changes everything. Because data exploration isn’t a single question – it’s a conversation.

Imagine I ask you: “How fast are you driving?” You answer: “100 kilometers per hour.” That number means nothing without context. So I ask: “Are you on a highway or a residential street?” You say highway. Better, but still incomplete. “Are you holding the steering wheel with both hands or texting?” The question continues until I have enough context to judge whether what you’re doing is dangerous or reasonable.

Data exploration works exactly the same way. A sales director asks: “Why did revenue drop 15% in the Northern region?” The first answer spawns five more questions. Were product lines affected equally? Did competitor activity change? How do customer segments compare? What about seasonal patterns versus last year?

Traditional BI forces each question through the data team – a days-long loop. Conversational AI enables the back-and-forth exploration in minutes. The business user drives the investigation, following their instinct through the data until context emerges and judgment becomes possible.

This isn’t reporting – it’s investigation. Think of it as moving from gym scales to having a personal trainer who helps you explore what’s working, what isn’t, and guides you through different approaches to understand the full picture.

Level 3: Predictive Data Science for Business Users – This is the moment where the dynamic really shifts. “Talk to Your Data Science” puts machine learning in the hands of people who understand the business context but lack coding skills.

A sales manager with 20 years of experience has gut feelings about which leads will convert. Now they can test those instincts. “Build me a model predicting customer lifetime value based on their first three interactions and industry vertical.” The system uses AutoML technologies to create, train, and validate the model conversationally.

They iterate: “What if we add purchasing timeline to the model? Does that improve accuracy?”

They refine: “Show me where the model confidence is low. What additional data would strengthen predictions?”

This is the personal trainer actively spotting you while you try new exercises. You’re building capability, testing hypotheses, and gaining confidence through guided experimentation. The business user isn’t becoming a data scientist – they’re applying data science to business judgment.

Why This Matters Now

The bottleneck in most organizations isn’t data availability – it’s data accessibility at the point of decision.

There is lots of chat around AI projects fail to deliver on their promises. And I agree. The primary culprit? Organizations attempt sophisticated AI implementations without establishing the foundational levels – they’re building advanced predictive models while their business users still can’t explore data independently.

Speed of insight directly impacts quality of decisions. When you force business users to work through intermediaries – submitting tickets, waiting for reports, explaining context to data teams – you create a translation problem. The person who understands the business question isn’t the person building the analysis. Nuance gets lost. Follow-up questions get batched into future sprints.

Conversational AI eliminates the translator. The business user who feels something in the market data can immediately explore that intuition, cross-reference it with operational data, and even build predictive models to validate their hypothesis.

What This Requires

Implementing this three-level approach needs two foundational elements.

First, you need a starting point. That’s where Portera Boost comes in – a unified platform that orchestrates these three levels without forcing you to stitch together fragmented point solutions. Dashboard monitoring, conversational exploration, and predictive modeling in one coherent experience.

Second, you need data readiness for AI. This isn’t optional. Conversational AI and AutoML are only as good as the data they work with. At Portera, we’ve built plug-and-play tools on Azure and Google Cloud Platform that walk you through the data readiness journey – from governance to quality to accessibility.

The uncomfortable truth? Most companies have the data. They lack the readiness.

So there you have it: The dashboard isn’t dead – it just needs to know its place. It’s your organizational bathroom scale, essential but insufficient. What you need alongside it is a personal trainer who can guide exploration and help you build predictive capability.

Because in business, like fitness, the goal isn’t just seeing your metrics. It’s improving them. And improvement requires exploration, experimentation, and the confidence that comes from testing your instincts against reality.

The question isn’t whether you have data. It’s whether your business users can act on it without permission slips and waiting periods.

Technology alone won’t democratize data-driven decisions. But conversation – backed by proper foundations – might.

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Sources

1. HG Insights (2025). “Business Intelligence Market Size, Share, & Buyer Landscape.” Companies will spend $72.1 billion globally on BI software in the next 12 months.

2. Forrester Research (2024). Boris Evelson. “Bring Data To The Other 80% Of Business Intelligence Users.” Only 20% of enterprise decision-makers can fulfill their own BI requirements hands-on.

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