Data Storytelling: meshing narrative techniques with Data Science Smarts

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

Along with the explosion of enterprise data analytics, there is growing evidence that insight, without action, is 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 untick and contextualize results, usually using a range of charts presented in a dashboard format. However, a recent industry survey found that while 80% of companies use data visualization to communicate their results, only half of these dashboards were effective.

Dashboards don’t tell a story.

There’s an old saying that goes, “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 on in my heart forever.

Data storytelling takes data visualization and adds context, empathy, and storytelling techniques. Data stories aren’t new and don’t necessarily require nifty charts. According to Gartner, Florence Nightingale’s call for better sanitation during the Crimean War is a classic example of a big data story. Based on her analysis of death rates, she realized that most soldiers did not die in battle but from preventable diseases caused by unsanitary hospital conditions. She convinced the British government and Queen Victoria, using compelling diagrams while telling a story.

Fast forward to today, and we’ve got dozens of great examples of how data, charts, and storytelling can combine to shine a light on issues, whether serious, light, commercially useful. or just a little unusual.

Connecting, Driving Change

The marketing industry has quickly embraced data storytelling, understanding better than most the need to connect, empathize and engage with customers and stakeholders as a precursor to evolving 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 more general skills required for storytelling. For much of the past decade, the data skills recruitment campaign has been heavily focused on hiring people with all-important data preparation skills rather than skills that interpret results into actionable messages. .

Data storytelling in the age of self-service

As the adoption of no-code, no-code software accelerates, so does the number of tools available to easily present data in a compelling way.

This move towards self-service tools is not limited to data storytelling. The latest innovation at the data layer makes all aspects of data analysis much more accessible. For example, building and running machine learning algorithms previously required a high degree of proficiency in different BI languages ​​and systems. Data professionals can perform machine learning queries using standard SQL skills with in-database machine learning. This democratization of computing makes it easier for data scientists to perform advanced analytics and opens the door to people with less traditional data science training.

The future is bright for people who can assimilate the paired skills of data science and data storytelling. With more people than ever searching for the story behind the data, how can data scientists master the soft skill of storytelling?

1. Mindset over matter

“The biggest problem with communication is the illusion that it has taken place,” wrote 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 with their audience and get their data-derived messages across. You might 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 on the rise. James Richardson of Gartner predicts that data storytelling will become the dominant way to consume analytics by 2025. There is an overwhelming amount of innovation in the market, both within traditional BI platforms or the growing number of solutions designed to be easily usable closer to the data layer. Data scientists should prioritize exploring possibilities with tools and techniques to help them create engaging stories.

3. Embark on a data storytelling mission

It’s not as intimidating as it sounds. Organizations are desperate to better connect to their data, and is there anyone better positioned to facilitate this mission than a data scientist? According to a recent HBR article, 90% of business leaders recognize the importance of data literacy, but only a quarter of workers are confident in their data skills. Mentoring or buddy programs are a great way to get started – for example, pairing up a data scientist and a marketing expert to listen to and learn from each other.

4. Culture is key

Does your organization support data reporting efforts? No matter how hard a data scientist works to tell stories, the impact will be limited if the organization doesn’t have the people and processes in place to understand and implement data-driven recommendations. Look for experienced companies that build effective cross-functional teams to complete strategically important work. The classic example is Agile software delivery: small teams made up of different business stakeholders bring different perspectives to the table.

Data scientists have traditionally been the gatekeepers of the data domain. Our next challenge will be to improve how we communicate and convince the rest of the company to act on our findings. Data scientists have relied on visualization dashboards for much of this communication. While great at distilling a large volume of information into a snapshot, as storytelling tools they fall short. They are being replaced by products that allow for much more sophisticated storytelling. Although initially exciting, the storytelling tools are no panacea. Ultimately, storytelling will require a mindset shift among data scientists, in which they embrace and hone the skills and techniques needed to successfully convince the public of their data-derived findings.


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