Data storytelling is the new language of corporations
Finding and communicating data insights is now a team sport.
However much we automate, however big our dataset, however clever our calculations, if you cannot communicate findings to others, you can’t make an impact with your analysis. This is the power of data visualization. Data visualization is a language and it’s becoming standard for analysts to know how to convey information to decision makers in a way that is actionable and easy to understand. This skill, combined with the ability for analysts to share the steps they took to discover the insights in data, is often defined as "data storytelling."
Data storytelling is a critical element of the analytics process. And a changing workplace culture, where analytics reigns supreme, is refining the definition of data storytelling. As organizations create cultures of analytics, analysts’ data storytelling methods are more about nurturing a conversation around the data and less about arguing for a singular conclusion. These analytical cultures are also fostering data literacy efforts aimed at teaching people to truly understand the data and to be participants in the analytical conversation—from the moment of discovery to the resulting business decision.
Andy Kirk, Data Visualization Specialist and Founder at VisualisingData.com shares the seven hats of data visualization. One of these is the communicator—"a person fundamentally concerned with all the human relationships involved in any project (the commissioners, the stakeholders, and the audience)." Andy explains how "all visualization work, at least in the communication sense, has to be audience centered." Data workers need to understand the audience’s process in forming a conclusion from a visualization. And at the same time, the audience has a responsibility to have the subject knowledge necessary to interpret the data and must be "willing to be informed."
This shift in data storytelling also manifests in data visualization trends. Long-form storytelling formats—through scrolling or multi-page dashboards—become more common, allowing the analyst to display their step-by-step approach to a conclusion. These methods allow analysts to show the progression of their analysis, highlighting the insights they encountered in the data and the resulting assumptions. The next step is to create an open conversation around these insights. This leaves room for people from different roles or departments to bring additional business context and invites a diversity of perspectives before making a business decision.
Data storytelling will continue to permeate workplace culture as more organizations create work streams and teams focused on analytical collaboration. This approach is shaping how organizations use data to engage, inform, and test ideas. And as more people understand how to interpret data and explain their analytical process, it amplifies the potential for business impact.