When the Dashboard Goes Dark: Building Decision-Making Muscle for the Unexpected

When the Dashboard Goes Dark: Building Decision-Making Muscle for the Unexpected 

By Tom Glover, Chief Revenue Officer at Responsive Technology Partners 

Three minutes into a board presentation, my laptop screen went black. Not the blue screen of death. Not frozen. Just black. The projector showed nothing. The carefully crafted slides with three months of data analysis, trend charts, and competitive benchmarks disappeared. Twenty pairs of eyes turned toward me. The silence stretched longer than comfortable. 

I had two choices: reschedule and look unprepared, or present without the crutch I’d been leaning on. I chose the second option. Forty-five minutes later, the board approved the largest investment in our company’s history. One board member later told me it was the most compelling presentation I’d ever delivered, precisely because I couldn’t hide behind slides. 

That moment taught me something fundamental about leadership: the dashboards we build, the metrics we track, and the data we collect create confidence, but they can also create dependency. And when those systems fail—as they inevitably do—leaders discover whether they’ve built genuine understanding or just learned to read screens. 

The Comfort of Dashboards 

Modern business runs on data. We’ve built sophisticated systems to track everything: financial performance updated hourly, customer sentiment measured in real-time, operational efficiency calculated to three decimal places. These tools provide genuine value. The visibility they create enables faster decisions and better resource allocation. 

But somewhere along the way, many leaders stopped asking whether they truly understood the business and started asking whether their dashboards were updating properly. The dashboard became a substitute for understanding rather than a tool to enhance it. 

I see this pattern constantly. A CEO panics when the morning report doesn’t generate. A CFO can’t discuss financial performance without pulling up specific screens. An operations director freezes when asked about capacity without consulting the live tracking system. They’ve optimized for normal conditions, where data flows smoothly and systems stay online. They haven’t built the muscle to operate effectively when those conditions disappear. 

The problem isn’t the dashboards themselves. The problem is mistaking information access for comprehension. A dashboard tells you what happened. Understanding tells you why it happened and what it means for what comes next. One requires electricity and network connectivity. The other requires thought, experience, and judgment you’ve developed over time. 

When Systems Fail 

Technology fails more often than we’d like to admit. A construction company client discovered this during a ransomware attack that encrypted every system, including the project management platform they’d used to run field operations for eight years. For seventy-two hours, nobody could access schedules, material orders, or project specifications. Superintendents accustomed to pulling up detailed plans on tablets stood on job sites without their digital crutches. 

The interesting part wasn’t the technology failure. That’s almost mundane at this point. The interesting part was watching which leaders could adapt and which ones couldn’t. Some superintendents shifted seamlessly to voice calls and physical walkthroughs, making decisions based on experience and judgment they’d accumulated over decades. Others seemed paralyzed, unsure how to prioritize work or allocate resources without their screens telling them what to do next. 

The difference wasn’t intelligence or capability. It was whether they’d maintained decision-making muscles that didn’t depend entirely on digital systems. The ones who adapted well hadn’t abandoned data—they used it constantly in normal operations. But they’d maintained a parallel skill: the ability to assess situations, weigh options, and make sound calls based on incomplete information and accumulated wisdom. 

Technology outages represent just one category of dashboard failure. Markets shift suddenly, making your carefully calibrated models irrelevant overnight. Key employees leave, taking institutional knowledge your systems never captured. Unexpected opportunities arise that your metrics weren’t designed to evaluate. Crises emerge in areas your monitoring systems never covered. 

In each of these scenarios, leaders face the same test: can you make sound decisions when your normal information streams go dark? 

The Over-Optimization Trap 

Part of what makes this challenge acute is that we’ve spent decades optimizing for efficiency. Lean methodology, just-in-time inventory, algorithmic decision-making—these approaches work brilliantly when conditions stay within expected parameters. They fail spectacularly when parameters change. 

A healthcare client learned this lesson during supply chain disruptions. Their procurement system used sophisticated algorithms to minimize inventory costs by ordering supplies based on historical usage patterns. It worked perfectly for years, keeping costs low and warehouses lean. Then supply chains broke down. The algorithm kept trying to optimize based on old assumptions about delivery times and product availability. By the time human judgment overrode the system, they’d already faced critical shortages of basic supplies. 

The system wasn’t broken. It was doing exactly what it was designed to do. The problem was that nobody had maintained the knowledge to make good procurement decisions independent of the algorithm. They’d delegated so completely to the system that institutional wisdom atrophied. When the system’s assumptions became invalid, they had no backup capability. 

This pattern repeats across industries and functions. We optimize processes until they run with minimal human intervention, then discover during disruptions that we’ve lost the ability to run them manually. We build forecasting models so sophisticated that nobody remembers how to make estimates without them. We create approval workflows so automated that decisions slow to a crawl when the workflow breaks. 

The efficiency gains are real. But they come with hidden costs that only become visible during disruptions. 

Building Decision-Making Muscle 

So how do you maintain decision-making capability that doesn’t evaporate when technology fails? The answer isn’t abandoning data or reverting to gut instinct. It’s about building complementary capabilities that work together under normal conditions and independently when systems fail. 

Start by understanding the difference between metrics and meaning. Metrics tell you that sales dropped 15% last quarter. Understanding tells you that the drop reflects a seasonal pattern amplified by your largest customer delaying orders because they’re managing cash flow ahead of a planned acquisition. One is data. The other is context that helps you decide whether to worry and what actions might help. 

Building this understanding requires regularly asking “why” questions that your dashboards can’t answer. When revenue increases, don’t just note the number—explore what’s driving it. When customer complaints spike, dig past the statistics to understand what’s actually frustrating people. When operational efficiency improves, figure out whether you’ve genuinely enhanced processes or just shifted problems elsewhere. 

This investigation builds mental models of how your business actually works. These models become increasingly valuable when your information systems fail because they give you frameworks for reasoning about situations even without perfect data. 

The construction company I mentioned earlier started requiring managers to periodically run operations without their project management system—not because of outages, but as deliberate practice. One Saturday a month, they’d coordinate work using only voice calls and physical documents. The first few attempts were clumsy and slow. After a year, managers had rebuilt skills that complemented rather than competed with their digital tools. When the ransomware hit, they adapted quickly because they’d already practiced operating without screens. 

This kind of preparation feels inefficient. It deliberately slows operations and creates friction where systems have removed it. But it builds organizational muscle that proves invaluable during disruptions. 

The Role of Judgment 

Data-driven decision-making is valuable, but it’s not the same as good decision-making. Good decisions require judgment—the ability to weigh factors that can’t be quantified, recognize patterns that metrics miss, and make calls when information is incomplete or contradictory. 

Judgment develops through experience, but only if you’re actively processing that experience rather than just collecting it. Every decision creates feedback that either sharpens judgment or leaves it unchanged. The difference is whether you’re reflecting on outcomes, questioning assumptions, and building understanding or simply moving on to the next decision. 

I’ve learned more from decisions that went wrong than from ones that worked out well. When a technology investment failed to deliver expected returns, the immediate lesson was about vendor selection and project management. The deeper lesson was about how we evaluated the need in the first place, what assumptions we’d made about adoption and change management, and where our decision process had gaps. That deeper lesson informed dozens of subsequent decisions in ways that metrics alone never could have. 

Building judgment means creating space for this kind of reflection. After major decisions, take time to examine not just outcomes but the decision process itself. What did you get right? What did you miss? Where were your assumptions sound and where did they prove wrong? What would you do differently next time? 

This reflection isn’t academic. It builds the decision-making capability you’ll need when your normal information sources aren’t available. Judgment compensated for incomplete data becomes second nature when you practice it regularly. 

Trusted Advisors and Distributed Knowledge 

No leader can personally maintain deep expertise across every critical business function. The alternative isn’t more dashboards—it’s cultivating relationships with people who have developed genuine understanding in specific domains. 

When systems fail, trusted advisors become invaluable. Not because they have different data—they’re working with the same incomplete information you are. But because they’ve built judgment and pattern recognition in their areas that complement your own. A CFO who truly understands your financial position can make sound recommendations about cash management even when reporting systems are down. An operations director who knows your production capabilities can make good calls about commitments even without access to detailed schedules. 

This requires intentionally developing these relationships before crises hit. At Responsive Technology Partners, we see this dynamic constantly in how effective security partnerships work. Organizations that treat us as vendors to call during emergencies struggle during incidents because we lack context about their operations, priorities, and risk tolerance. Organizations that involve us in ongoing strategic discussions handle disruptions far more smoothly because we’ve built shared understanding that doesn’t depend on having perfect information about the specific incident. 

The same principle applies internally. Leaders who regularly engage in substantive discussions with their teams build shared mental models that enable faster, better decisions during crises. Leaders who primarily communicate through dashboards and reports find their teams paralyzed when those communication channels fail. 

Preparation Beyond Planning 

Business continuity planning typically focuses on maintaining operations when specific systems fail. You identify critical functions, document recovery procedures, and establish backup capabilities. This work matters, but it doesn’t address a deeper challenge: ensuring your organization can make good decisions when the context changes faster than your plans can accommodate. 

Plans work well for scenarios you’ve anticipated. They’re less helpful when reality diverges from your assumptions. The value of preparation isn’t just documented procedures—it’s developing organizational capability to respond effectively to situations you didn’t foresee. 

This requires building what military strategists call “commander’s intent”—a clear understanding of objectives and principles that guide decisions even when specific plans become irrelevant. In business terms, it means ensuring your team understands not just what to do in normal conditions but why you do it that way, what you’re trying to accomplish, and what principles should guide choices when standard procedures don’t apply. 

We’ve worked with healthcare organizations navigating compliance requirements like HIPAA where this distinction becomes critical. Organizations that train staff only on specific procedures struggle when situations don’t match their documented scenarios. Organizations that develop genuine understanding of privacy principles and risk management empower staff to make sound judgment calls in novel situations. 

The difference shows up clearly during security incidents. When ransomware hits a network, documented response plans provide structure. But good outcomes require people who understand the business well enough to make rapid decisions about which systems need immediate restoration, what data might be at risk, and how to communicate with stakeholders. Those decisions can’t all be pre-scripted because each incident presents unique factors. 

The Partnership Model 

Part of what makes decision-making during disruptions challenging is that modern business requires specialized expertise that no organization can maintain entirely in-house. You need deep knowledge about cybersecurity, regulatory compliance, infrastructure design, and emerging technologies—but you can’t staff experts in every domain. 

This is where the partnership model shows its value. Organizations that work with specialist providers like RTP develop access to expertise that complements internal capabilities. When systems fail or unexpected challenges arise, you’re not starting from zero. You have established relationships with people who know your environment, understand your constraints, and can provide expert guidance based on experience across many organizations. 

The key is treating these relationships as strategic partnerships rather than transactional vendor arrangements. Vendors you call during emergencies can execute tasks but can’t provide strategic counsel because they lack context. Partners you engage proactively contribute to decision-making during disruptions because they’ve built understanding of your business, your risk tolerance, and your objectives. 

This doesn’t mean outsourcing decision-making. It means ensuring you have access to specialized knowledge that informs your judgment when your normal information systems aren’t available. A security partner who knows your infrastructure can help assess incident scope even when monitoring tools are compromised. A compliance expert who understands your regulatory environment can guide decisions about notification requirements even when your compliance management system is offline. 

Practicing What You Can’t Predict 

You can’t practice responding to every possible disruption. You can practice the underlying capabilities that let you respond effectively to disruptions you didn’t anticipate. 

Some organizations run tabletop exercises that simulate various crisis scenarios. These exercises have value, but their greatest benefit isn’t rehearsing specific responses. It’s forcing leaders to make decisions with incomplete information, communicate under pressure, and coordinate responses without perfect clarity about the situation. These capabilities transfer across different types of disruptions. 

The construction company I mentioned earlier took this further by occasionally introducing unexpected constraints into normal operations—not through actual disruptions but through artificial limitations that forced adaptation. They’d declare certain systems or resources unavailable and challenge teams to maintain operations anyway. This built general problem-solving capability rather than just response procedures for specific scenarios. 

The most valuable preparation is maintaining capabilities you hope you won’t need. Decision-making without dashboards. Communication when normal channels fail. Coordination across teams when standard workflows break down. These aren’t exciting to practice, but they become essential when unexpected challenges arise. 

When the Dashboard Goes Dark 

The next time your systems fail—and they will—you’ll discover whether you’ve built genuine understanding or just learned to read screens. Whether you can make sound decisions with partial information or freeze until the data flows again. Whether your team can function independently or needs constant digital guidance. 

The time to build these capabilities isn’t during the crisis. It’s now, while systems are working and pressure is manageable. Not by abandoning the tools that make you more effective under normal conditions, but by developing complementary skills that work when those tools fail. 

Because the question isn’t whether disruptions will come. It’s whether you’ll be ready to lead through them when they do. 

Tom Glover is Chief Revenue Officer at Responsive Technology Partners, specializing in cybersecurity and risk management. With over 35 years of experience helping organizations navigate the complex intersection of technology and risk, Tom provides practical insights for business leaders facing today’s security challenges.