A well-designed chart or interactive visualisation is a great way to see an overview of all the possibilities in your firm’s data. However, simple narrative text can describe, highlight or explain features in ways that many users will find helpful. And for certain cases, such as writing reports and reviews, detailed descriptions may be essential.
If you’re an experienced developer of business dashboards, you may feel you can illustrate all of your needs by just adding new visualisations or adding more detail to existing examples. However, in practice, we find that even good visualisations have three significant limitations.
Limitation 1: Visual language
Firstly, the viewer must understand the visual language in use. Data analysts easily forget that not everyone shares their insightful reading of even a simple bar chart. Visualisations of a long-tail distribution for example, may need an explanation to highlight the significance of what we see there. And once we get used to seeing such visualisations, we may too readily jump to conclusions based on patterns we have learned before.
Narratives can draw attention to significant features that we may not immediately understand, such as distributions. In this way, we can learn the visual language for the future. Yet the text descriptions also help to keep us grounded in the details in case we are prone to over-interpret what we are seeing.
Limitation 2: Clarity
The second limitation of data visualisation is, ironically enough, also one of the factors which make a good chart or graphic usable in the first place. A good visual representation has clarity: a clear purpose, and that purpose is clearly shown.
If we consider our long-tail distribution again, we could add indicators of total values to our chart, along with average and median values, standard deviations and so on. However, each addition reduces the clarity of our work. A narrative gloss, describing these details, can be a handy addition to the visual overview.
Limitation 3: Whole story is not told
Finally, visualisations simply do not tell the whole story in your data. They cannot capture the flow and context of a human conversation which is, in fact, our most fundamental form of collaboration. Their clarity and their specialised visual language illustrate the story, but they are not the story we are trying to tell.
This is why narrative interpretations of data are so powerful in a report or review—it is able to draw data-driven possibilities to unfold the scenario over time. Our verbal language is highly tuned to assist memory and understanding through this unfolding. And, when it comes to debate and disagreement—the most important and informative aspect of any collaboration—verbal language is our natural instrument of choice.
