Why Dashboards (and Dead Men) Tell No Tales

If you’ve been to a Disney theme park, you probably experienced the popular Pirates of the Caribbean ride. As your boat drifts towards a dark, foreboding tunnel, a ghostly voice echoes above: 'Dead men tell no tales...' The meaning is clear—secrets stay buried when no one’s left to tell them. The same can be said for dashboards. No matter how much data they contain, they remain silent. Without a storyteller, they reveal nothing.

Yet, one recurring theme I still hear often is “dashboards tell stories.” It has become a popular mantra in business intelligence communities, which has been spread by BI software vendors and embraced by a host of dashboard designers. With the popularity of Power BI, Tableau, and other analytics tools, most organizations are now inundated with a wide array of dashboards. You would think business leaders have been blown away by the valuable stream of stories they’re receiving from these tools—but that’s not what has happened.

The silence of dashboards and the dead

Just like a lifeless body in a murder mystery, a dashboard will sit there, full of information, yet remain silent, offering no explanations, no insights, and certainly no tales—unless someone steps in to find and interpret the data. If the dashboard is well-designed, it could contain a variety of clues, but it won’t reveal the full story on its own. It needs an investigator—someone to piece the puzzle together, connect the dots, extract meaning, and give it a voice. In other words, dashboards don’t tell stories; people do.

Some people will argue AI and natural language generation can bridge this gap and help dashboards to finally tell data stories. While AI can act like a forensic assistant, automatically summarizing clues and quickly identifying anomalies, it still lacks the instincts of a seasoned detective who can uncover the motive. It can highlight a dodgy alibi or a suspicious behavior, but without human context and judgment to frame the bigger picture, the real story remains untold. All we have is data without direction, numbers without nuance.

Why dashboards fall short as storytelling tools

Because many companies rely heavily on dashboards to disseminate information across their organizations, it only seems logical they could be enhanced with storytelling capabilities. Even though they serve a critical role in business reporting for tracking key metrics and monitoring business performance, they do not excel at storytelling for the following reasons:

1. ‍Exploratory vs. explanatory. Dashboards primarily let users explore the data on their own, leaving the interpretation to each user. While they offer people flexibility to drill down and filter the information, they do not explain what the numbers mean. Data stories actively guide audiences through the meaning of the numbers, providing context and interpretations that expand the audience’s understanding. ‍
2. Descriptive vs. diagnostic. Dashboards display key metrics and dimensions, showing ‘what happened.’ Their descriptive nature makes them effective for monitoring and reporting. Users can answer many basic questions like, “how many sales did we have last month?” or “what was our customer churn rate?” A data story provides a deeper diagnostic approach to explain “why” something occurred and what it means for the audience. It adds valuable layers of context, interpretation, implications, and next steps.‍
3. Broad focus vs. narrow focus. Dashboards often provide a broad perspective, covering a comprehensive set of metrics and dimensions at various granularities. While breadth of coverage can be beneficial for reporting purposes, it becomes a limitation for storytelling. A data story limits the scope to focus attention on specific anomalies, trends, and patterns. It doesn’t feature irrelevant and distracting information that doesn’t support the narrative or core messages. A dashboard’s broad focus forces users to filter and prioritize what they examine, while a data story’s narrow focus removes this burden from the audience to focus on the meaning.‍
4. Dynamic vs. static. Dashboards are mostly built to continuously update with live or recent data. Because they’re always refreshing, notable patterns in the dashboards come and go—fluctuating across time periods. They’re better suited to surface-level monitoring and quick decision-making. Data stories focus on sharing a snapshot or static moment in time, breaking it down to provide deeper perspectives. Once a data story has communicated its insight, it has served its purpose.
5. ‍Hierarchical vs. linear layout. Dashboards offer users flexibility as they can jump between different sections and follow various exploration paths based on their needs and interests. For experienced users, a hierarchical layout enables them to navigate where they please, but for newcomers, it can be daunting and overwhelming. Data stories guide people down a pre-determined path in a linear sequence. The audience builds their understanding in a progressive manner as foundational concepts and context are shared before revealing the more complex insight. Without a linear format, you can’t establish a narrative flow, which is an essential trait for storytelling. ‍
6. Factual/informative vs. emotional/persuasive. Dashboards present information objectively and let the numbers speak for themselves. For straightforward, quick decisions, this approach simply provides supporting evidence that’s needed and nothing more. Knowing that emotions play a critical role in decision making, data stories are designed to connect with key stakeholders on an emotional level and inspire them to act. Data stories leverage narrative techniques, such as conflict, contrast, and tension/suspense to humanize the data and make the insights more engaging, memorable, and persuasive.  

By highlighting the many reasons why dashboards don’t tell tales, I don’t mean to diminish the essential role they play in democratizing data within organizations. They’re intended for a specific reporting purpose, which limits their effectiveness for other tasks like storytelling. Just like you can’t criticize a fork for not being a spoon, it’s not a dashboard’s fault it isn’t well suited to storytelling.

It's like using tables in Microsoft Word to do spreadsheet work. While possible, you wouldn’t be using Word for its true strengths. You would be better off using Excel instead.

The false promise of automated data storytelling

At the time I was writing my book on Effective Data Storytelling, I worked for a BI vendor that started touting the storytelling capabilities of its platform. While I expressed my concerns internally, it put me in an uncomfortable position because I found myself the heretic amidst faithful believers. Interestingly, it forced me to confront what was the relationship between dashboards and data stories. If I didn’t believe dashboards could tell tales, did they still complement or contribute to data storytelling in some way?

I determined that dashboards help with ‘storyframing.’ In other words, they help frame and focus attention on key metrics and dimensions, which are aligned with the key business objectives of the team or organization. Rather than having to comb through all the enterprise’s data, dashboards offer business users a curated subset of KPIs to focus on. From these curated reports, meaningful potential insights and stories could emerge.

When you observe an interesting anomaly, trend, correlation, or pattern in a report, your curiosity leads you to analyze the data more deeply, connect the dots, and, hopefully, uncover a meaningful insight—a process I call ‘storyforming’ (or storyfinding). When you need to communicate your insight to others, that’s when storytelling comes into the picture to complete the process of turning data into action.

The allure of automated dashboard storytelling is understandable—it promises to scale insight generation and reduce the analytical burden on teams. However, truly impactful data stories are formed by four critical human elements that can't be automated:

‍‍

• ‍Analytical intuition: The ability to separate meaningful signals from noise, distinguishing between routine fluctuations and insights that could drive real business value or indicate important changes, often developed through years of domain expertise.‍
• Contextual awareness: The capacity to understand broader business implications, market dynamics, and industry trends, connecting data patterns to real-world situations and strategic objectives. ‍
• ‍Emotional intelligence: The ability to understand and address the human side of data, including how findings might affect different stakeholders and how to present the data in ways that resonate emotionally.‍
• Stakeholder understanding: Deep knowledge of decision-makers' priorities, communication preferences, and decision-making styles, enabling stories to be tailored for maximum resonance and impact.

Rather than attempting to automate storytelling through dashboards, organizations would be better served by empowering analysts with more time to find and craft powerful data stories with these essential human capabilities. Quality and relevance trump quantity when it comes to influencing decisions through data.

The next time someone says, 'This dashboard tells a great story,' ask them: Does it? Dashboards, like dead men, tell no tales. The real magic happens when a storyteller transforms raw numbers into a narrative that drives action. And that’s a tale worth telling.

‍