Why Storytelling is a Key Skill in Data Science

Storytelling is one of the most powerful professional skillsets, irrespective of the industry or position.

Humans have evolved through storytelling. Our minds are attuned to a good story; they hold the power to engage audiences, capture interest and compel action. The mind, however, has not yet evolved to view data, statistics and facts in the same capacity. Nor have humans evolved to fluently read or understand data. Technical jargon and methodologies remain alien to most.

This creates a communication barrier for Data Scientists.  Ranked above any technical knowledge or statistical acumen, communication is the key data science skill according to Harvard Business Review. So, when communication through storytelling and data science meet, Data Scientists are empowered to share their knowledge and findings with engaged audiences – a winning formula.

Companies are gradually capitalising on the financial return brought through advanced data capabilities, however translating data into business meaning is where companies are falling short. The skill to extract understanding from raw, complex datasets and then translate the findings is referred to as Data Science storytelling.

Data Science storytelling is an art. It requires practice, structure and consideration to turn data into emotions, and then into actions. The goal for Data Scientists is to influence their audience. Information alone rarely is powerful enough to influence, research, however found engaging stories to be the most effective vehicle for exerting this influence.

Before Data Scientists begin to ‘create’ their story, LiveStories.com recommends creating a GAME plan, this outlines the Goal, Audience, Message and Engagement. This should answer the questions; what is the goal? Who is your audience? What is the message? And, how do you engage? Of these four points knowing your audience is undoubtably priority. When the audience is known, Data Scientists can shape their story based on the said audiences’ priorities. Are sales and revenue top concern? Are they technical, or are they uncomfortable with numbers? The audience should drive the narrative. The fancy algorithms or statistical models used have little value or relevance to stakeholders for example. They do not want to know how solutions work or how you implemented it. They instead care for results, wanting to hear how the company’s top and bottom line will be impacted.

These data-driven stories should mimic that of a traditional story, with a beginning, middle and end, conflict and emotions. The SUCCESs is another model which was coined by Chip and Dan Health in their book Made to Stick. The model identifies six key considerations to make the data story stick. Ensure the story is Simple, Unexpected, Concrete, Credible, Emotional and an actual Story.

The GAME and SUCCESs approach are two among dozens of structures, all of which share an overall goal to “encourage and energise critical thinking for business decisions” as defined by James Richardson, Senior Director Analyst at Gartner.
 

Data Science is a powerful tool, but when data insights are not widely understood, a Data Scientist’s work is somewhat pointless. It is when Data Scientists translate the complex, abstract aspects of data into a comprehensible and rememberable story that the real impact is seen.