For thousands of years, storytelling has been an integral part of our humanity. Even in our digital age, stories continue to appeal to us just as much as they did to our ancient ancestors. Stories play a vibrant role in our daily lives—from the entertainment we consume to the experiences we share with others to what we conjure up in our dreams.
The psychology behind stories
Stories bring us together. We can talk about them and bond over them. They are shared knowledge, shared legend, and shared history; often, they shape our shared future. Stories are so natural that we don’t notice how much they permeate our lives.
When we’re immersed in a story, we let down our guard. We focus in a way we wouldn’t if someone were just trying to catch us with a random phrase or picture or interaction. (“She has a secret” makes for a far more intriguing proposition than “He has a bicycle.”) In those moments of fully immersed attention, we may absorb things, under the radar, that would normally pass us by or put us on high alert. Later, we may find ourselves thinking that some idea or concept is coming from our own brilliant, fertile minds, when, in reality, it was planted there by the story we just heard or read.
Modern-day storytelling is often associated with the popular TED conference series and its slogan of “Ideas Worth Spreading.” Analysis of the most popular 500 TED Talk presentations found that stories made up at least 65% of their content. Throughout time, storytelling has proven to be a powerful delivery mechanism for sharing insights and ideas in a way that is memorable, persuasive, and engaging.
What is data storytelling?
Data storytelling is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative.
When narrative is coupled with data, it helps to explain to our audience what’s happening in the data and why a particular insight is important. When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Finally, when narrative and visuals are merged together, they can engage or even entertain an audience. When we combine the right visuals and narrative with the right data, we have a data story that can influence and drive change. When we package up our insights as a data story, we build a bridge for our data to the influential, emotional side of the brain.
What makes a good data scientist?
Owen Zhang, ranked #1 on Kaggle (2015), the online stadium for data science competitions, lists his skills on his Kaggle profile as “excessive effort,” “luck,” and “other people’s code.” An engineer by training, Zhang says that data science is finding “practical solutions to not very well-defined problems,” similar to engineering.
He believes that good data scientists, “otherwise known as unicorn data scientists,” have three types of expertise. Since data science deals with practical problems, the first one is being familiar with a specific domain and knowing how to solve a problem in that domain. The second is the ability to distinguish signal from noise, or understanding statistics. The third skill is software engineering.
The bad news is that the more time spent in meetings (even for non-managers), the more money a data scientist makes. And the meets mainly involving weaving stories around insights from raw data to get the CXO tribe on board to making a decision or making clients commit on further investments.
Why data storytelling is essential?
Data visualization expert Stephen Few said, “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” Any insight worth sharing is probably best shared as a data story.
The need for more data storytellers is only going to increase in the future. With the shift towards more self-service capabilities in analytics and business intelligence, the pool of people generating insights will expand beyond just analysts and data scientists. This new breed of data tools will make it easier for people across business functions to access and explore the data on their own. As a result, we’re going to see an unprecedented number of insights being generated within companies than ever before. However, unless we can improve the communication of these insights we will also see a poorer insight-to-value conversion rate. If an insight isn’t understood and isn’t compelling, no one will act on it and no change will occur.
Data Science Failure: Dangers of Letting Data Speak on itself
For some people, crafting a story around the data may seem like an unnecessary, time-consuming effort. They may feel the insights or facts should be sufficient to stand on their own as long as they’re reported in a clear manner. They may believe the revealed insights alone should influence the right decisions and drive their audience to act. Unfortunately, this point of view is based on the flawed assumption that business decisions are based solely on logic and reason.
Ignaz Semmelweis, a mid-nineteenth century obstetrician, who discovered hand washing could save countless lives but failed to communicate his findings effectively to a skeptical medical community. His data was ignored, his life-saving ideas were rejected, and he was sadly discredited by his colleagues.
Semmelweis couldn’t scientifically prove why his handwashing policy worked—that wouldn’t happen until chemist Louis Pasteur discovered the germ theory of disease in the mid-1860s. What the doctor had was more than 18 months of statistical data showing his handwashing approach worked and that such practices could save the lives of thousands of expectant mothers.
He had the truth—but was it enough?
Key takeaways:
a) If we possess insights that are critical to our business success, we have a duty to communicate them clearly in a timely manner. Semmelweis waited too long and allowed others to inadvertently cloud his message.
b) Knowing our audience and striving to understand their existing attitudes and beliefs. Not everyone is going to accept our data, especially if it is disruptive to commonly held practices or beliefs. Instead, we may want to focus on identifying open-minded allies who can help build internal support and consensus for our ideas.
c) Relying on just logic and reason to make our points. Decisions are more often made by emotion, and an effective narrative can touch our audience in ways the numbers alone never will.
d) Data can often be communicated more powerfully with data visualizations than just tabular data. Charts should reinforce our key points and make it easier for our audience to follow our data story.
References:
- These Are The Skills You Need To Become A $240,000+ Unicorn Data Scientist, Gil Press, Forbes, Oct 3, 2015.
- How Stories Deceive, By Maria Konnikova December 29, 2015, The New Yorker.
- A History Lesson On The Dangers Of Letting Data Speak For Itself,Brent Dykes,Forbes, Feb 9, 2016.
- Data Storytelling: The Essential Data Science Skill Everyone Needs, Brent Dykes,Forbes, Mar 31, 2016.