There’s a story we tell ourselves about data in organizations. It goes something like this: Information wants to be free. Give people access to data, democratize it (as consultants like us tend to say) and better decisions will flow throughout the organization. Empowered employees will spot trends their managers miss. Small teams will outmaneuver large competitors. The pyramid of corporate hierarchy will flatten into something more like a network, with insights flowing from anywhere to everywhere. “Data is the new oil” after all, and most of the problem was about access, refinement and distribution of this valuable commodity.

It’s a beautiful story. It’s also, by most measures that matter, not happening.

Over the past couple years, I’ve been working closely with multiple Fortune 100 companies as they plan “what’s next” for their analytics platforms. That involves a lot of discussion with their analysts and users about their experiences working with data. And I keep thinking about a story we hear over and over again. They describe their morning routines: Log into Tableau, export to Excel, find a different dashboard, export, repeat, clean the data, build the real analysis, switch systems and email the report. After millions spent on modernizing analytics infrastructure, everyone still works like it’s 2003.

This is the kind of observation that should stop us cold. How is it possible that after the self-service analytics revolution, the cloud data revolution and endless conferences about “data-driven decision making,” the most sophisticated analytics users at our most sophisticated companies are still exporting to a 40-year-old business tool like Excel?

The answer tells us something important not just about data and analytics technology, but about the gap between how we hope technology will change organizations and how change actually happens.

The Problem Isn’t What You Think

Let me start with what the problem isn’t. It isn’t that people resist change. Anyone who’s watched the rapid adoption of AI tools, smartphones or video conferencing during the pandemic knows that people will embrace new tools when those tools genuinely make their lives easier.

It isn’t ignorance, either. The employees exporting dashboards to Excel aren’t luddites. They’re often experts in their business domain – power users who know exactly what these platforms can do – but they also know what the platforms can’t do.

And it isn’t, despite what vendors claim, simply a failure of “change management” or insufficient training. We’ve worked with companies that have run large skill certification programs, created centers of excellence and driven adoption programs. We’ve helped design and implement these programs. Yet adoption remains uneven, and extracting value often feels like it happens despite the tools rather than because of them.

The real problem is more fundamental: We’ve built analytics tools that solve the wrong problem.

Think about what modern BI platforms optimize for. They process vast amounts of data quickly. They create beautiful visualizations. They scale across organizations. These are impressive technical achievements. They’re also, from the perspective of someone trying to make a decision at 3 PM on a Tuesday, largely beside the point.

How Analytics Actually Work

Let me paint you a picture of how analytics actually work in most organizations. Not how it’s supposed to work, but how it does work.

Sarah is a marketing manager. She needs to figure out why conversion rates dropped last month. In theory, she opens her company’s analytics platform, explores the data, finds insights and takes action. This sounds clean, efficient and data-driven in theory.

In practice? She opens the platform and confronts dozens of dashboards, none quite answering her question. She finds one that’s close, but it shows aggregate data when she needs segment-level detail. She tries to filter it, but the filters don’t match her mental model of the business. She exports to Excel, not because she loves Excel; but because in Excel, she can add the context the dashboard lacks. She can annotate. She can combine it with data from other sources. She can shape it into something that actually answers her question. Then she can action it or track actions in the spreadsheet too.

By the time she’s done, she’s spent three hours doing what the analytics platform was supposed to make instant. And the worst part: Next week, she’ll have to do it all over again.

But Sarah’s struggles are just the beginning. When she takes her analysis to the leadership meeting, she discovers that the CFO has completely different conversion numbers. According to finance, last month’s conversion rate was 2.5%. Sales insists it was 3.8% based on their dashboard. Marketing claims 2.1% from theirs. That’s three departments, three “sources of truth” and zero trust.

The meeting that should have focused on solving the conversion rate problem instead becomes an archaeological expedition. Which numbers are right? Why are they different? Whose dashboard should we believe? By the time they’ve excavated the truth from layers of conflicting metrics, the meeting is over and nothing has been decided.

This erosion of trust in data might be the most insidious problem of all. When metrics become contested territory, when nobody believes the numbers and when every discussion starts with 30 minutes of forensic accounting, then data-driven decision making becomes impossible. You can’t build a data-driven culture on a foundation of data distrust.

The cruel irony? The proliferation of dashboards often makes this worse, not better. More dashboards mean more places for numbers to diverge. More visualizations mean more opportunities for misinterpretation. The tools that promised clarity instead delivered chaos.

This isn’t a story about one person or one company. This is the reality of enterprise analytics: millions of knowledge workers, every day, working around and against the very tools that were supposed to empower them, unable to trust the very numbers that should guide their decisions.

The Incentive Problem

To understand why this keeps happening, I like to think about incentives. Not the incentives of users, their incentives are clear. They want to do their jobs well with minimal friction. The incentives that matter here are those of the software companies that sell these platforms.

What does success look like for a BI vendor? It’s not actually about making users more effective or organizations more “data driven.” It’s about winning the RFP. It’s about checking feature boxes. It’s about creating enough lock-in that migration costs become prohibitive. It’s about selling to the economic buyer, usually IT or finance, not the end user.

But there’s another perverse incentive at play: the platform play. Major vendors aren’t just selling you analytics; they’re selling you an ecosystem. They want you using their hobbled data prep tool, adequate visualization platform and barely functional integration features. This is not because this collection is best for your users, but because it’s best for their revenue. Lock-in isn’t just about switching costs anymore; it’s about making you dependent on an entire constellation of interconnected, mutually dependent tools. And in the new world of consolidation via acquisition, it’s not even guaranteed that these disparate tools in the platform even work well together.

This creates a profound misalignment. The people who choose these tools aren’t the people who use them. The metrics that matter for procurement (total cost of ownership, vendor stability, “unified platform benefits”) aren’t the metrics that matter when you’re trying to figure out why conversion rates dropped and do something about it.

Even more perversely, vendors often benefit from this complexity. The harder the tool is to use, the more services and training they can sell. The more frustrated users become, the more likely IT is to buy premium support contracts, customer success programs or that new AI-powered interface that promises to finally make everything simple. The problem becomes the business model.

Let me be clear: I’m not saying all BI vendors are bad actors. Data and analytics tools are incredibly valuable as part of a well-designed stack. Many vendors genuinely want to help their customers succeed. But the current incentive structure pushes even well-intentioned companies toward complexity and lock-in rather than user empowerment.

What Would Better Look Like?

This is where things get interesting. We’re not here just to throw rocks at enterprise BI vendors, many of whom we partner with and respect. But we do think there’s a better way, and we can see it emerging in unexpected places.

We see it in the shadow systems that employees build when official tools fail them. We see it in the workarounds that become standard practice. We see it in the tools that people actually choose to use when they have a choice.

Better would mean analytics that meets people where they work, not in some separate platform they have to remember to visit. It would mean insights that come with clear actions, not just observations. A metric showing declining customer health would connect directly to the interventions available: trigger an outreach campaign, adjust pricing and modify the product experience.

Better would mean preserving context and institutional knowledge, not starting from scratch with every analysis. When someone asks, “Why did we change our pricing model?” The answer would be right there: the analysis, the discussion, the decision, and the results.

Better would mean acknowledging that most business questions aren’t about data; they’re about decisions and actions. This means that the tools we build for our users should be built to solve those problems, enable decisions and accelerate actions rather than merely displaying data for visualization and exploration.

But building better requires something the industry has been reluctant to do: putting human needs above technical capabilities. It requires admitting that the democratization of data isn’t about access, everyone has access. It’s about agency and attention. Agency requires tools that amplify human judgment, not replace it. And in an era of information overload, the scarcest resource isn’t data; it’s human attention. Our tools should preserve and focus that attention, not scatter it across dozens of dashboards.

Most importantly, this requires building a custom-tailored set of tools and experiences that match your organization’s specific needs, workflows and decision-making patterns. The one-size-fits-all platform approach has failed because organizations aren’t one-size-fits-all.

The Path Forward

So, where does this leave us?

First, we need to be honest about what’s not working. The analytics revolution, as currently conceived, has failed to deliver on its promise. Not because the technology isn’t powerful, but because power without usability is just complexity.

Second, we need to think differently about how change happens in organizations. Real transformation doesn’t come from deploying new tools and hoping people adapt. It comes from understanding how people actually work and building tools that enhance those workflows. It comes from human-centered design that starts with user needs, not platform capabilities.

Third, and most importantly, we need to realign incentives. As long as software companies are rewarded for complexity, lock-in and platform proliferation rather than user success, we’ll keep getting tools that demo well and work poorly.

The good news is that change is possible. We’ve seen organizations that have figured this out, that have built analytics systems that people actually want to use. They didn’t do it by buying better dashboards or the latest high-cost SKU from their BI vendor. They did it by starting with a different question: Not “what data do we have?” but “what decisions do we need to make?”

That shift, from data-centric to decision-centric, changes everything. It changes what you build. It changes how you build it. It changes who’s involved in building it.

Most of all, it changes the story we tell ourselves. This is not a story about democratizing data, but about empowering people. It’s not about making everyone an analyst, but about making everyone more effective at their actual job.

Building Something Better

Over the coming weeks, we’ll share how to make this vision practical:

• Modern Data Applications: New BI tools are enabling companies to build bespoke business applications using business experts, not developers – solving complete problems rather than just showing data.
• Conversational Analytics: Practical approaches to AI-powered exploration that actually accelerate insight discovery, letting users ask follow-up questions naturally rather than filing requests with the data team.
• Embedded Intelligence: Proven strategies for bringing insights directly into CRMs, email tools and project management platforms where decisions actually happen.
• Flexible Metric Layers: Creating business-friendly metrics that can be composed and recomposed to answer evolving questions without building new dashboards for every slight variation.

Each piece will include real examples, practical guidance and tools you can start using immediately.

The analytics revolution isn’t about better dashboards; it’s about intelligence that works the way people do. That means tools that meet users where they work, adapt to how they think and bridge the gap between insight and action. This isn’t a far-future vision requiring massive technical investments. The tools exist today. What’s missing is the willingness to put human needs above platform features.

Because somewhere right now, someone is exporting a dashboard to Excel. And they’ll keep doing it until we build something better.

Let’s build it together.

Ready to transform your analytics from a necessary evil into a competitive advantage? Contact us for a complimentary solution design session with our architects.