From cave paintings to automated data analysis: how data storytelling evolved
Automated data analytics are the next evolution of business intelligence
Many years ago, the earliest humans shared their knowledge with each other through spoken word, albeit through grunts and gestures.
They were confronted with the same issues many modern-day professionals come across when trying to share information in the workplace: others would struggle to understand, they would find themselves repeating the same grunts, etc. We’ve all been there.
In my last blog, I talked about how the modern data analyst is a storyteller. There’s a reason storytelling has primarily been the most effective communication approach throughout human history. It’s now scientifically proven and widely accepted that we are chemically wired for storytelling. When we hear a story, our brains release chemicals. Oxytocin, the “hug hormone” (linked to empathy and relationship building), dopamine, the “feel-good neurotransmitter” (makes us want to engage with the content) and cortisol the “stress hormone” (helps us remember) all contribute to ensuring the information sticks.
In other words, storytelling helps us understand things more clearly. The author and researcher Brené Brown writes, “Our brains reward us with dopamine when we recognize and complete patterns. Stories are patterns. The brain recognizes the familiar beginning-middle-end structure of a story and rewards us for clearing up the ambiguity.”
So let’s go back to the cavemen. They explored new more efficient ways to gather data and alternative mediums to share the insights they discovered. They started painting in caves, in locations as far-flung as France, Indonesia and Australia. The paintings matured and developed into stories. Important information was passed down from generation to generation.
Human history is a story of exploring new ways to share our insights. Great inventors and scientists like Leonardo Da Vinci, Galileo and Einstein were masters of discovering new insights, but it was how they shared their knowledge that had the biggest impact on the broader population. Using clear yet detailed explanations supplemented by illustrations and data, they made it possible for a much wider audience to grasp concepts that previously would have been out of reach.
Moving on to the more recent past. In the last two decades, as computers have shrunk in size and grown in capability, we are now able to collect and analyse more data than ever before.
How people have gathered data, developed insight and shared their knowledge has evolved, hand-in-hand, with the technology available. And the technology that catches on is always the technology that allows us to tell better stories.
“The definition of genius is taking the complex and making it simple.”
– Albert Einstein
But there’s a catch. As our technological capabilities have grown, so has the complexity of every-day life. As a result, organisations around the world are now often overwhelmed by an embarrassment of riches when gathering data about their businesses.
We are facing a new challenge to understand the data we are collecting; analysing large volumes of data has scaled up the difficulty in translating data into knowledge. Business intelligence is a function of gathering the data generated by your business and having the means to understand it. Genius is evident when the insights can be communicated with simplicity and accuracy. Like a perfect cadence in music, insights are useful when they can be turned into stories.
Right now, business intelligence (BI) in many organisations has one foot in the past, gathering big data from legacy systems, and one foot in the future, gathering large data sets with automated data pipelines. For those who don’t act quickly to step with both feet into the future of data science, the ability to tell good stories is being impeded and this has a knock-on effect across the board – in marketing activities, generating reports and investor relations.
And the pressure is building on BI teams and their tools. People will soon find out whether their data infrastructure and processes are robust enough. Forward looking organisations will move towards greater automation. Well designed, fit-for-purpose automation can amplify the impact of insight, by effective distribution of timely knowledge to the right decision makers across their organisation. Most crucially this allows them to access and piece together the stories they need to run their businesses.
The next step in the BI evolutionary chain is to take a unified approach to data, coupling leading edge technology in machine learning and cloud data warehousing. This allows people to make sense of the chaos going on around us today, the huge swathes of rich data we are all gathering, and get our stories – a beginning, middle and end. So we can answer the questions: what happened? Why did it happen? And what can I do about it? That’s what we mean when we talk about actionable insights.
Author: Jeremy Williams
Date: 15 May 2020