The Data Storytelling Myth: The Fallacy That 'All Data Tells a Story'
While the familiar adage 'every dataset tells a story' sounds appealing, in reality, most data is passive and means nothing on its own. The real task isn't 'extracting' the story from data, but 'building' it.
We live in the age of data. According to Statista's 2021 report, global data creation is projected to reach 180 zettabytes by 2025. In the face of this staggering volume, the phrase “every dataset tells a story” has become an omnipresent mantra. Yet, as catchy as it sounds, this statement is a profound fallacy that oversimplifies—and even misguides—the complex process of data storytelling. Data does not tell a story on its own; the story is built through the right questions asked and connections drawn by the analyst.
Why Data Doesn't Tell a Story on Its Own
Raw data is just like bricks, cement, and rebar on a construction site. They are components of a potential structure, but they are not a house on their own. Creating a house requires an architect's blueprint, an engineer's calculations, and the craftsmanship of builders. Similarly, data is merely a collection of facts—passive, silent, and devoid of context. Consider an e-commerce website's daily sales figures: “Today, 150 items were sold, with an average order value of 300 TL.” This is a data point, an observation. But is this data a story on its own? Why 150 items? Was it 200 yesterday, or 100? Is an increase good, or a decrease bad? How are competitors performing? Without answering these questions, what we have is just a number. In 'The Functional Art', Alberto Cairo emphasizes that data visualization is not equivalent to storytelling; effective visualization can be part of the story, but the story itself is constructed through the narrative and context surrounding the visual. That is precisely why data is not a story by itself; it is merely a potential ingredient of one.
Where Does the Story Come From?
The story comes to the data from the outside. This external source is the analyst's curiosity, critical thinking, and deliberate drive to investigate. Beyond merely visualizing raw data, data storytelling is the process of critically analyzing relationships, anomalies, and potential insights among data points to build a coherent, persuasive narrative that serves a defined purpose. This process involves the following steps:
- Asking Questions: Every good story begins with a good question.