The Dashboard Dilemma: When Requests Don't Match Reality
You know the feeling. That sinking sensation when you check your usage logs and see the truth staring back at you. Two views. Four months. All that work—the meetings, the requirements gathering, the late nights making it "clean and pretty"—for what? A digital trophy that collects virtual dust.
If you're in business intelligence, data analytics, or any role that builds dashboards, you've probably experienced this. Maybe you're living it right now. That Reddit post from r/BusinessIntelligence with 774 upvotes and 110 comments? It resonated because it's our shared reality. Users beg for dashboards like they're life-saving medications, then never take the prescription.
But here's the thing—it's not actually about the dashboard. Not really. The dashboard is just the visible symptom of deeper organizational issues. In this guide, we're going to unpack why this happens so frequently in 2025, what it really means, and most importantly, how to fix it. Because your time is valuable, and building unused tools helps exactly no one.
The Psychology Behind Dashboard Requests
Let's start with the human element. Because at the end of the day, we're dealing with people, not just data.
That manager who pinged you nonstop? He wasn't lying about feeling urgency. The pressure was real. But here's what was actually happening: he was experiencing what psychologists call "solution anxiety." He had a problem—maybe declining sales, maybe operational inefficiencies—and he needed to show he was addressing it. A dashboard feels like action. It's tangible. It's something he can point to in meetings and say, "Look, we're getting visibility!"
And there's status involved too. In many organizations, having a custom dashboard has become a status symbol. It signals that your department is data-driven, that you have resources, that you're important enough to get development time. It's the modern equivalent of the corner office—a visible marker of organizational importance.
But here's the kicker: once the dashboard exists, the psychological need is often satisfied. The anxiety dissipates. The status is achieved. The actual usage? That requires a different kind of effort—the daily discipline of checking, analyzing, and acting on data. And that's where the breakdown happens.
The Request vs. Need Disconnect
This is where things get technical, but stick with me—it's crucial.
Users almost never ask for what they actually need. They ask for what they think they need based on their current understanding. There's a massive gap between "I need a dashboard" and "I need to understand why our customer churn increased 15% last quarter."
I've seen this dozens of times. A department head comes to me saying they need a comprehensive sales dashboard with 20 different metrics. We build it. They look at it once, get overwhelmed, and never return. What they actually needed was a simple alert when key accounts showed declining engagement, plus a weekly summary email with three critical metrics.
The problem is in the translation. Users speak in solution language ("dashboard") while we should be listening for problem language ("I can't track...", "I don't know when...", "It takes me hours to...").
And there's another layer here: the difference between monitoring and investigation. Most users request monitoring dashboards—something they'll check regularly. But what they actually engage with are investigation tools—something they use when something goes wrong. If nothing goes wrong, they never open the monitoring dashboard. But they still wanted it "just in case."
Organizational Theater and The Illusion of Progress
Now let's zoom out to the organizational level. Because this isn't just about individual users—it's about company culture.
In 2025, being "data-driven" is table stakes. Every company wants to say they are. But wanting to be data-driven and actually being data-driven are very different things. The dashboard request often represents organizational theater—performative data practices that look good in presentations but don't actually change decision-making.
I worked with a company that had over 200 dashboards. Two hundred. Their BI team was constantly building new ones. But when we analyzed usage, 80% of those dashboards had fewer than 10 views per month. The C-suite loved showing off their "data ecosystem" to investors. The actual managers? They were still making decisions based on gut feelings and Excel sheets they'd been using for years.
The dashboard becomes a checkbox. "Do we have visibility into X?" Check. The question of whether anyone actually looks at that visibility? That's often not part of the conversation.
And there's a resource allocation problem here too. Building dashboards consumes developer time—time that could be spent on data quality, pipeline improvements, or actual analysis. But dashboards are visible. They're deliverable. They're something you can show in a sprint review. Cleaning up messy data sources? That's invisible work. So what gets prioritized?
The Technical Reality: Build vs. Use Friction
Let's talk about the tools themselves. Because sometimes, the dashboard isn't the problem—access to it is.
Think about your own workflow. How many different platforms do you log into daily? Email, Slack, project management, CRM, HR system, and now... the BI platform. Every additional login is friction. Every separate platform is cognitive overhead.
That beautiful dashboard you built in Tableau or Power BI? It lives in another tab, another bookmark, another place to remember. Users have to actively seek it out. And in the daily rush of work, that "active seeking" often doesn't happen.
Contrast this with notifications. When something needs attention, it comes to you. Slack message. Email alert. Phone notification. The information finds you rather than you finding it. This is why alert-based systems often see much higher engagement than dashboard-based systems.
And then there's the dashboard itself. We BI professionals tend to build for completeness. We want to include every relevant metric, every possible filter, every dimension. But users don't want completeness—they want clarity. They want the answer to their specific question, not every possible question they might someday have.
Practical Solutions: Changing the Conversation
Okay, enough diagnosis. Let's talk solutions. Because you can't change human psychology or organizational culture overnight, but you can change your process.
First, stop starting with "What do you want in a dashboard?" Start with "What decision are you trying to make?" or "What problem are you trying to solve?" This simple shift changes everything. It moves the conversation from features to outcomes.
I use a simple three-question framework with every request:
- "What specific decision will this help you make?"
- "How often do you make that decision?"
- "What will you do differently based on this information?"
If they can't answer these questions clearly, we don't build. Instead, we schedule a discovery session to understand their actual needs.
Second, implement a "dashboard pre-nup." Before any development starts, agree on success metrics. "We'll consider this successful if you open it at least once a week for the first month." Or "We'll measure success by whether this changes any of your team's processes." Get this in writing. It creates accountability.
Third, build the smallest possible thing first. Not a dashboard—a report. Not a report—an alert. Not an alert—a single metric in an email. Start microscopic and expand only if usage justifies it. This is the BI equivalent of the lean startup methodology: build, measure, learn.
Technical Strategies for Higher Adoption
Now let's get technical with some specific strategies that actually work.
Embed, don't send. Instead of sending users to your BI platform, embed the relevant visualizations where they already work. In their CRM. In their project management tool. In their internal wiki. Reduce the friction to zero.
Automate data collection when possible. If users need specific data points from various websites or platforms to inform their decisions, consider using tools like Apify's web scraping and automation platform to gather that information automatically. This ensures the data feeding into your dashboards is current and reduces manual data entry—a common reason dashboards become outdated and unused.
Schedule deliveries instead of expecting visits. Most users won't proactively check a dashboard, but they will read a scheduled email with key metrics. Build your dashboards with export and scheduling capabilities as primary features, not afterthoughts.
Implement usage tracking from day one. Not just views—interactions. Which filters are used? Which tabs are viewed? Which metrics get hovered over? This data is gold. It tells you what's actually valuable versus what's just decoration.
And here's a controversial one: build expiration dates into your dashboards. If a dashboard hasn't been viewed in 90 days, it automatically archives. Users get a warning at 60 days. This creates natural cleanup and forces conversations about what's actually needed.
Common Mistakes and How to Avoid Them
Let's address some frequent errors I see teams making.
Mistake #1: Building before understanding. This is the big one. The user says "dashboard" and you start wireframing. Stop. Ask why. Then ask why again. Use the "five whys" technique to get to the root need.
Mistake #2: Equating views with value. A dashboard with 100 views might be less valuable than one with 10 views if those 10 views led to actual decisions. Measure outcomes, not just interactions.
Mistake #3: One-size-fits-all design. Executives, managers, and frontline employees need different information presented differently. An executive dashboard should fit on one screen with 5-7 key metrics. An analyst dashboard might have drill-downs and detailed tables. Match the tool to the user's actual workflow.
Mistake #4: Ignoring data literacy. Sometimes users don't use dashboards because they don't understand them. They're intimidated. They're afraid of misinterpreting. Include training, documentation, and ongoing support as part of your delivery—not as an optional extra.
Mistake #5: Treating the dashboard as the end goal. It's not. The dashboard is a means to an end. The end is better decisions. Keep the focus on the decision, not the visualization.
When to Say No (And How to Do It Politely)
This might be the most important skill in your BI toolkit: the ability to say no without saying no.
Instead of "No, we can't build that," try "Let me understand what you're trying to achieve first." Instead of "That's not a priority," try "Help me understand how this compares to our other projects in terms of impact."
Frame everything around value and outcomes. "Based on our team's capacity, we need to focus on projects with the highest ROI. Can you help me quantify the expected return from this dashboard?"
Create a formal intake process with clear criteria. When requests have to go through a structured process with specific questions, the low-value requests often filter themselves out. The people with real needs will jump through the hoops. The people who just want a shiny new toy often won't.
And sometimes, you need to redirect. If someone needs a simple report but is asking for a complex dashboard, say so. "What you're describing sounds like it could be handled with a scheduled report. Would you like me to show you how to set that up in our existing system?"
The Future: Beyond Dashboards
As we move through 2025, I'm seeing a shift away from traditional dashboards toward more integrated, intelligent systems.
Chat interfaces that let users ask questions in natural language. Automated insights that highlight anomalies and trends without users having to look for them. Data products that embed intelligence directly into business processes.
The dashboard as we know it—a static collection of charts and graphs—is becoming a legacy concept. The future is proactive, not reactive. Contextual, not separate. Automated, not manual.
This doesn't mean dashboards disappear entirely. They still have their place for certain use cases. But they become one tool among many, not the default solution for every data need.
Your role is evolving too. You're not just a dashboard builder anymore. You're a data product manager. A decision enablement specialist. Your value isn't in how many visualizations you create, but in how much better decisions your organization makes because of your work.
Turning Frustration into Value
That Reddit post captured a universal frustration. But here's what I've learned after years in this field: the frustration is actually an opportunity.
Every unused dashboard represents a misunderstanding. A misalignment. A missed opportunity to truly help someone make better decisions. And that's where you come in.
You have the power to change the conversation. To move from order-taker to strategic partner. To build tools that actually get used because they actually solve problems.
Start small. Pick one recurring request. Apply the "what decision" framework. Build the minimum viable product. Track usage religiously. Learn. Iterate.
The goal isn't to eliminate dashboard requests. The goal is to ensure that every dashboard you build delivers real value. That every hour you spend developing translates to better business outcomes.
Because at the end of the day, we're not in the dashboard business. We're in the better-decisions business. And that's work worth doing.
Now go check your usage logs one more time. But this time, look for patterns. Look for opportunities. And then start the conversation that turns those unused dashboards into something that actually matters.
