Linda came out of the meeting room with a grin shining over her face, alongside the excitedly talking CEO. Each member in the boardroom applauded her monthly sales presentation. Not because she achieved a higher sales number than the last month, but because her presentation today looked promising with a good proposal enveloped around a realistic back-story supporting data.
The young sales lead was low on energy the last month when she proposed her strategy to handle the wobbling sales in the Eastern territory despite measurable actions taken already. However, the scene today is sort of a miracle after the last month’s denial when all the board members were unsatisfied with her approach. So, what changed?
Well, Linda implemented Data Storytelling as a focal style in her presentation. Let us analyze what brought about the reverse flip in the emotions of the C-league team and her success.
The Data Explosion
With the advanced technologies, internet, and apps, the world is rolling out a whopping 2.5 quintillion bytes of data per day. Moreover, this is a year-old figure. So, imagine the change in the scenery in the past year. If this is not enough to overwhelm you with the drowning data we are producing, there are a few more. According to an estimation, data collected in 2016 and 2017 surpassed the overall data available from the inception of the human race until 2016. The world has produced 90% of the total data in the recent 2-3 years.
However, when these exponentially growing numbers in the data repositories make little sense, the analytics and business intelligence (BI) comes into the picture. These techniques make the numbers live on your screens to throw insights and forecasts. But what makes these visual presentations more comprehensible and consumable is storytelling.
What is Data Storytelling?
Let us first segregate data storytelling from visualization. Visualization is not data storytelling but is a significant fraction of the creative process. Storytelling in data is just a newer version of weaving stories, a formal approach used in the business. Stories help the audience interpret the current data, data behind data, and data to be invested in the future on data.
Data storytelling is the ‘why’ and ‘what next’ behind the stunning graphs and diagrams. When data being presented are narrated to the audience in a customized format as a compelling story, the method adopted by the users is called Data Storytelling. The technique induces human touch to the mere facts that only reveals the incidents happening in an organization. However, ‘why an incident occurred’ is hidden in the storytelling.
Steven Levitt, the co-author of Freakonomics, considered it a powerful strategy:
“Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.”
Why is Data Storytelling important nowadays?
Storytelling is an old age practice used by generations to inculcate values and knowledge in their successors. And with time, storytelling has taken various forms from recitation to books to pictorial representation and audio. The latest entrant videos deliver the stories in the easiest consumable way.
Likewise, data have a past and a future, circumstances and reasons. Users get insights from the dashboards. Integration of coming-of-age technologies such as Machine Learning and Artificial Intelligence with the analytics and BI tools foster foresight. But what remains subtle and hidden with data is the story they carry inherently. With the narratives associating the figures, the digital aspects get a real-world touch, a human element to the lifeless numbers.
Beyond the ‘what happened’, when the reasons are explored, and the truth is told to the users in the form of narratives, data appear more agreeable and easily consumable. After all, humans are instinctively inclined to narratives—a powerful medium to convey an idea.
“Data are just summaries of thousands of stories – tell a few of those stories to help make data meaningful.” Chip & Dan Heath, Authors of Made to Stick, Switch.
With data storytelling, goes the most significant rule of ‘show, don’t tell’. Whereas the numbers only tell the facts, the stories unfold some hidden truths. A narrative thrown at the users opens a window for them to propose their recommendations due to the natural inclination towards ideation, and not merely consume your ideas. This opens channels for collective brainstorming.
And the same happened with Linda in the boardroom. When she wrapped a story around data on sales of the past five years, the members concluded not only a primary reason but also a couple of other details, which augmented the fall in numbers. They brainstormed and recommended multiple options instead of just consuming the solution proposed by her.
How to get Data Storytelling done right?
Data stories revolve around data: how numbers changed over time, what incidents influenced the change, what were the secondary outcomes, and who were impacted. According to Gartner, in data analytics, storytelling should have three aspects.
Storytelling = visualization + narrative + context
Every story should have a clear quest that forms the context, which in Linda’s case is the declining sales. When data are represented in visualizations with a narration about what happened and why so, storytelling is completed.
A data analyst or whoever you are reaching out to for data analytics services should explain the quest (the problem), draw the conclusions, and open a discussion to accept inputs. The lifecycle of a data story goes like this:
Problem (What) -------------> Conclusions (Why) -------------> Recommendations (How)
Often, a data story starts with a beginning, has a middle, but unlike fiction with a fixed ending, it has open ends, which invites ideations from the audience. This is the crucial aspect: to solicit ideas from the users that sets a platform for mutual decisions.
Visualization should be wisely chosen when narrating a story. While an animated chart showing the changes in the numbers over the years for various locations look great in one quest, the same might not suit others. Modern BI tools have huge prospects to build visualizations around data like heat maps, animated line charts, and so on. Find a befitting representation that suits data and audience, both.
Engaging the Audience
The sole purpose of creating a narrative around data is to gain audience engagement and thereby get recommendations. So, to open discussion avenues on the issues, the context has to suit them.
CEOs would look at the panoramic view, concentrate on the outcomes, and focus on the summary. They are more inclined towards visuals and the green, amber, and red part of the visuals and complete data. At the same time, a CFO and finance team would love to see more of the figures and less of the graphics. They focus on hard numbers and micro-level data. Sales folks, always on the move, want to have both numbers and diagrams but at their easy reach. In contrast to others, a marketing team would need more data related to customers and customer engagement.
So, choose the visuals and context according to the team.
Moreover, the narrative has to be inviting to create a two-way communication. To engage the spectators, mingle an emotional touch with a few personal anecdotes. Use metaphors, create a conflict in the story, focus on one quest, and invite the ideas.
However, there can be a few downsides to the stories weaved around data. With the focus shifting from the quest to convincing the recommendations by the narrator, the motive can get lost. The analysts not being seasoned storytellers leave gaps in the association between data discovery and the story. Moreover, the narration would only fit if data were complete, right, and sufficient.
To conclude, a robust data story results in an influenced audience, convincing ideas from consensus, and solutions to the problems.
Are you making the most of data storytelling?