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Data Storytelling: Meshing Narrative Techniques with Data Science Smarts

  • Jorge Torres 
Storyteller with interesting information. Group of children students in class at school with teacher
Storyteller with interesting information. Group of children students in class at school with a teacher.

Alongside the explosion in enterprise data analytics is the growing realization 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. To some extent, data visualization tools 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 that 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 don’t necessarily require whizzy graphics. According to Gartner, Florence Nightingale’s appeal for better sanitary conditions in the Crimean War is a classic example of a great data story. Based on her analysis of mortality rates, she realized that most soldiers were not dying in combat but of preventable diseases caused by unhygienic hospital conditions. She convinced the British government and Queen Victoria, using compelling diagrams 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, empathize, and engage with customers and stakeholders as a precursor to changing buying behaviors.

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 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 available to showcase data compellingly easily.

This shift towards self-service tools is not restricted to data storytelling. The latest innovation at the data layer makes all aspects of data analytics far more accessible. For example, creating and executing machine learning algorithms used to require a high degree of proficiency in different languages and BI systems. Data pros can perform machine learning queries using standard SQL skills with in-database machine learning. This IT democratization is making it easier for data scientists to execute advanced analytics ands opening up the field to people with a less traditional data science background.

The future is bright for people who can assimilate the twin skills of data science and data storytelling. 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.

Understanding, in principle, the need for effective communication is not the same as communicating well. In other words, data scientists must work hard to connect to their audiences and land their data-derived messages. You may have what it takes to do data science, but do you have what it takes to be a data storyteller?

2. Leverage data storytelling tools

Fortunately for data scientists, data storytelling tools are in their ascendancy. Gartner’s James Richardson predicts data storytelling will become the dominant way of consuming analytics by 2025. There’s an impressive amount of innovation in the market, both within traditional BI platforms or the increasing number of solutions designed to be easily usable nearer the data layer. Data scientists should prioritize exploring the possibilities with tools and techniques to help them create engaging narratives.

3. Embark on a data storytelling mission

It’s not as daunting as it sounds. Organizations are desperate to connect better with their data, and is there anyone better placed to facilitate that mission than a data scientist? According to a recent HBR article, 90 percent of business leaders recognize the importance of data literacy, yet only a quarter of workers feel confident in their data skills. Mentoring or buddy programs are a great way to start – for example, pairing up a data scientist and a marketing expert to mutually listen and learn from each other.

4. Culture is key

Is your organization supportive of efforts to communicate data insights? No matter how hard a data scientist works at storytelling, the impact will be limited if the organization does not have the people and processes to understand and action data-driven recommendations. Look out for experienced businesses forming effective cross-functional teams to carry out strategically important work. The classic example is Agile software delivery: small teams of different business stakeholders bring different viewpoints to the table.

Data scientists have traditionally been the gatekeepers to the data realm. Our next challenge will be to improve how we communicate and convince the rest of the enterprise to act on our findings. Data scientists have relied on visualization dashboards for much of this communication. While these are great at distilling a large volume of information into a snapshot, as storytelling tools, they fail. Replacing them are products that enable far more sophisticated storytelling. While initially exciting, storytelling tools are not a panacea. Ultimately, storytelling will require a shift in mindset among data scientists, in which they embrace and hone the skills and techniques required to convince audiences of their data-derived findings successfully.