Welcome to an insightful conversation with Vernon Yai, a renowned data protection expert whose work in privacy protection and data governance has shaped innovative strategies for safeguarding sensitive information. With a deep focus on risk management and cutting-edge detection techniques, Vernon brings a unique perspective to the world of data visualization and business intelligence (BI) dashboards. In this interview, we dive into the common pitfalls of BI dashboards, exploring why they often fall short of expectations, the hidden costs behind their development, and how to design them for maximum impact. We also discuss the importance of tailoring dashboards to specific audiences and ensuring data is presented with actionable context. Join us as we uncover practical solutions to transform underwhelming dashboards into powerful decision-making tools.
What do you believe are the primary reasons BI dashboards often fail to meet the expectations companies have for them?
In my experience, the biggest issue is a lack of alignment between the dashboard’s purpose and the users’ actual needs. Companies often build dashboards with a one-size-fits-all mentality, trying to pack in every possible metric without considering who’s looking at it or why. This leads to cluttered, confusing interfaces that overwhelm rather than inform. Another key factor is insufficient training or context—users don’t always know how to interpret the data or apply it to their decisions. Lastly, there’s often a disconnect in maintenance; dashboards aren’t updated to reflect changing business priorities, so they quickly become irrelevant.
How common is it for dashboards to be underutilized in organizations, and what’s typically behind that lack of engagement?
It’s incredibly common—probably more than half the dashboards I’ve seen in organizations are barely touched after the initial rollout. The root cause is usually poor design or a lack of user buy-in. If a dashboard feels like a chore to navigate or doesn’t directly address the pain points of the team using it, people will ignore it. Often, there’s also a cultural issue where data isn’t embedded into decision-making processes, so the dashboard becomes more of a shiny toy than a practical tool.
What are some effective ways companies can evaluate if their BI dashboard is truly serving its purpose for their teams?
One of the best indicators is usage metrics—check how often people are logging in and interacting with the dashboard. If it’s low, that’s a red flag. Beyond that, look for qualitative feedback: are decision-makers referencing the dashboard in discussions or meetings? Are they requesting tweaks or additional features? That shows engagement. Finally, tie it to outcomes—has the dashboard led to faster or better decisions? If you can’t point to tangible impacts, it’s probably not working as intended.
Dashboards often come with a hefty price tag in terms of both money and time. Can you walk us through the major costs involved in creating one?
Absolutely. First, there’s the platform cost—licensing fees for BI tools can be steep, especially for enterprise solutions. Then, you’ve got the labor cost, which is often underestimated. Specialists spend countless hours gathering requirements, designing the interface, and integrating data sources. Add to that the time spent in meetings with stakeholders to define metrics and get approvals—those staff hours pile up fast. Finally, there’s the opportunity cost of delayed decisions while waiting for the dashboard to be built. You’re essentially paying for inaction during that period, which can hurt more than the direct expenses.
How can organizations strike a balance between the high investment in dashboards and the urgency to make timely decisions?
It starts with prioritization. Focus on building a minimal viable dashboard that addresses the most critical needs first, rather than aiming for perfection out of the gate. Use iterative development—roll out a basic version, get feedback, and refine it over time. This cuts down on upfront costs and gets something usable into decision-makers’ hands faster. Also, leverage existing data tools or templates where possible to reduce build time. The key is to treat dashboards as evolving tools, not one-and-done projects.
Why do you think dashboards designed to please everyone often end up failing to meet anyone’s needs?
When you try to cater to every department or role with a single dashboard, you create a mess. Each user group—whether it’s executives, managers, or analysts—has different priorities and levels of data literacy. A CEO wants high-level insights at a glance, while a manager might need detailed drill-downs. Combining all those needs into one interface leads to visual overload and dilutes the focus. Users end up wading through irrelevant data to find what matters to them, and most just give up. Specificity is crucial; a dashboard can’t be a jack-of-all-trades.
How can developers better understand and address the unique needs of different roles when designing dashboards?
It starts with deep collaboration. Developers need to sit down with representatives from each role—executives, managers, frontline staff—and ask pointed questions about their daily challenges and decision-making processes. What metrics do they check most often? What frustrates them about current tools? Shadowing users or observing their workflows can reveal unspoken needs. From there, create user personas to guide the design process, ensuring each dashboard or view is tailored to a specific audience’s priorities and skill level.
Why is context so critical when presenting data on dashboards, especially for users who aren’t data experts?
Raw numbers without context are meaningless to most people. Take a figure like Monthly Active Users—if it’s just sitting there as a big number, only a data analyst might know if it’s good or bad. For everyone else, it’s just noise. Context—like historical trends, comparisons to goals, or benchmarks—helps users understand the story behind the data. It turns a static figure into something actionable. Without it, non-experts can’t make informed decisions, and the dashboard becomes more decorative than functional.
What are some alternative ways to present data when a dashboard isn’t the right fit for certain audiences or information?
Sometimes simpler is better. A static one-page handout with a clear narrative can work wonders for audiences who just need a snapshot with explanations—like board members during a presentation. For more dynamic data, a well-designed website format allows for scrolling, mixing visuals with text to provide deeper context. Email reports with curated insights can also be effective for teams that don’t need real-time access. The key is to match the format to the audience’s habits and the data’s purpose, rather than forcing everything into a dashboard just because it’s trendy.
Looking ahead, what’s your forecast for the future of BI dashboards and how companies will approach data visualization?
I think we’re heading toward more personalized and adaptive dashboards, powered by AI and machine learning. These tools will automatically adjust to user behavior, showing the most relevant metrics based on role, past interactions, or even current business goals. There’s also a growing push for simplicity—less cluttered, more intuitive designs that don’t require a data degree to understand. Companies will likely focus on integrating dashboards into everyday workflows, making them a seamless part of decision-making rather than a standalone tool. The future is about accessibility and relevance, ensuring data visualization truly drives action.
