Data Storytelling: Meshing Narrative Techniques with Data Science Smarts By Jorge Torres, Co-founder and CEO of MindsDB
Alongside the explosion in enterprise data analytics is the growing realisation that insights, without action, are not enough.
One of the biggest stumbling blocks has been effective communication between data teams and the rest of the business. Data visualization tools do, to some extent, help unpick and contextualise findings, typically using a range of charts presented in a dashboard format. However, a recent industry survey found that while 80 percent of enterprises use data visualization to communicate findings, only half of these dashboards were effective.
Dashboards don’t tell a story
There’s an old proverb which says, “Tell me the facts and I’ll learn. Tell me the truth and I’ll believe. But tell me a story and it will live in my heart forever.”
Data storytelling takes data visualization and adds context, empathy and narrative techniques. Data stories aren’t new and they don’t necessarily require whizzy graphics. Florence Nightingale’s appeal for better sanitary conditions in the Crimean War is a classic example of a great data story, according to Gartner. Based on her analysis of mortality rates, she realised that the majority of soldiers were not dying in combat but of preventable diseases caused by unhygienic hospital conditions. She succeeded in convincing the British government and Queen Victoria, using diagrams compellingly while telling a story.
Fast forward to today and we have dozens of excellent examples of how data, graphics and storytelling can combine to shine a light on issues whether serious, light-hearted, commercially useful or just a bit unusual.
Creating connections, driving change
The marketing sector has been quick to adopt data storytelling, understanding better than most the need to connect, empathise, and engage with customers and stakeholders as a precursor to changing buying behaviours.
While savvy data scientists could learn a lot from marketers about the value of data storytelling, it’s perhaps surprising that data scientists struggle with the softer skills required for storytelling. For much of the past decade, the data skills recruitment drive has been heavily weighted towards hiring those with the all-important data preparation skills, rather than the skills that interpret the findings into actionable messages.
Data storytelling in the self-service era
As the adoption of low and no-code software gathers pace, so does the number of tools now available to easily showcase data in a compelling way.
This shift towards self-service tools is not restricted to data storytelling. The latest innovation at the data layer is making all aspects of data analytics far more accessible. For example, to create and execute machine learning algorithms used to require a high degree of proficiency in different languages and BI systems. With in-database machine learning, data pros can now perform machine learning queries using standard SQL skills. Not only is this democratisation of IT making it easier for data scientists to execute advanced analytics, it is opening up the field to people with a less traditional data science background.
For people who can assimilate the twin skills of data science and data storytelling, the future is bright. With more people than ever chasing the story behind the data, how can data scientists master the soft skill of storytelling?
1. Mindset over matter
“The single biggest problem with communication is the illusion that it has taken place,” wrote the Irish playwright George Bernard Shaw.
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Source: https://www.datasciencecentral.com/author/jorgemindsdb-com/