Leveraging management dashboards for storytelling: a viable pathway? | by Michał Szudejko | Aug, 2023


Unleashing narrative power: can data storytelling and dashboards coexist?

The phrase “storytelling with data” has been enjoying a surge in popularity recently. This trend is evident for me in the growing viewership of my articles and the increasing number of attendees at the workshops I facilitate on this topic. Concurrently, there has been an encouraging uptick in the valuable resources in this field. This represents a stark contrast to the landscape in 2018 or 2019 when I first delved into this subject and found scarce useful information except for excellent books and other content published by Cole Nussbaumer Knafflic or Brent Dykes.

Photo by Campaign Creators on Unsplash

In one of his posts, Brent Dykes[1], splits the process of crafting a compelling story into three steps:

  1. Storyframing: this stage involves an in-depth exploration of data, with a specific focus on unique dimensions and metrics.
  2. Storyforming: at this phase, any anomalies or trends detected previously are subjected to a thorough investigation. Essentially, this is where we cultivate insights. It’s vital at this stage to evaluate the importance of our findings for the business, understand their origins, and identify the reasons behind their occurrence. Typically, this process requires multiple cycles of discovery, analysis, and conclusion drawing, which further refines the research scope and necessitates a more focused analysis of the data.
  3. Storytelling: at this stage, we make decisions about how to use the insights we’ve unearthed. If we decide to share these findings with a broader audience, the act of storytelling becomes crucial. In this step, we polish the narrative, design supportive visuals, and prepare appropriate commentary.

According to Dykes, management dashboards primarily demonstrate their worth during the initial stage. They facilitate multi-dimensional data analysis through effective filtering techniques and enable the user to delve into details or take a broader view of the data using drill-down or drill-up functions. Consequently, they assist in identifying patterns or anomalies in the data.

Dykes says:

… there was a major problem — I



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