A storyteller with interesting information.Group of children in school class with teacher
A storyteller with interesting information. A group of children in a school class with a teacher.

Alongside the explosion of enterprise data analytics is a growing realization that insights alone are not enough without action.

One of the biggest obstacles is effective communication between data teams and other business units. Data visualization tools are usually useful to some extent in extracting and contextualizing findings through various charts presented in a dashboard format. However, according to a recent industry study, 80% of companies use data visualizations to communicate their findings, yet only half of these dashboards are effective.

Dashboards don’t tell stories.

There is an old saying that Tell me the truth and I will believe. But tell me one story. Then it will live forever in my heart. ”

Data storytelling takes data visualization and adds context, empathy, and narrative techniques. Data stories are nothing new and don’t necessarily require fancy graphics. According to Gartner, Florence Nightingale’s appeal for better hygiene during the Crimean War is a prime example of a good data story. Based on a mortality analysis, she found that most soldiers did not die in combat, but from preventable diseases caused by unsanitary hospital conditions. She used compelling diagrams to tell the story and persuaded the British government and Queen Victoria.

To this day, there are many great examples of how data, graphics, and storytelling can be combined to shed light on serious, lighthearted, commercially useful, or offbeat problems.

Create connections and drive change

Marketing departments best understand the need to connect, empathize, and engage with customers and stakeholders as a precursor to changing buying behavior, and have been quick to adopt data storytelling.

Savvy data scientists can learn a lot about the value of data storytelling from marketers, but it’s perhaps surprising that data scientists struggle with the softer skills needed for storytelling. That’s it. Over the past decade, data skills recruitment efforts have focused more on hiring people with the all-important data preparation skills than on the ability to interpret research findings into actionable messages.

Data storytelling for the self-service era

As adoption of low-code and no-code software accelerates, so too do the number of tools available to easily present data in compelling ways.

This shift to self-service tools isn’t limited to data storytelling. The latest innovations in the data layer make all aspects of data analysis much more accessible. For example, creating and running machine learning algorithms required a high level of proficiency with different languages ​​and his BI system. Data experts can use standard SQL skills with in-database machine learning to run machine learning queries. This democratization of IT makes it easier for data scientists to perform advanced analytics, opening the field to those with less traditional data science background.

The future looks bright for those who can absorb the two skills of data science and data storytelling. With more people than ever following the stories behind their data, how can a data scientist master his soft storytelling skills?

1. Way of thinking

Irish playwright George Bernard Shaw said, “The greatest problem with communication is the illusion that it has taken place.

As a general rule, understanding the need for effective communication is not the same as communicating well. In other words, a data scientist must work hard to connect with an audience and deliver a data-derived message. Do you have what it takes to be a data storyteller?

2. Leverage data storytelling tools

Fortunately for data scientists, data storytelling tools have the upper hand. Gartner’s James Richardson predicts that data storytelling will be the primary way he uses analytics by 2025. There is an amazing amount of innovation on the market, both within the traditional his BI platform and a growing number of solutions designed to be easy to use near the data layer. Data scientists should prioritize exploring possibilities with tools and techniques that help create compelling narratives.

3. Embark on a data storytelling mission

It’s not as hard as it sounds. Organizations crave a better connection with their data. Is anyone better positioned to further that mission than a data scientist?According to his recent HBR article, 90% of business leaders recognize the importance of data literacy. , only a quarter of his employees are confident in their data skills. A mentoring or buddy program is a great way to start. For example, pair a data scientist with a marketing expert to listen to and learn from each other.

4. Culture is key

Does your organization champion efforts to communicate data insights? No matter how hard data scientists work on storytelling, your organization must have the people and processes to understand and act on data-driven recommendations. limited impact. Look out for experienced companies that form effective cross-functional teams to accomplish strategically important work. A classic example is Agile Software Delivery. A small team of diverse business stakeholders brings different perspectives to the table.

Data scientists have traditionally been the gatekeepers of the data realm. Our next challenge is to improve the way we communicate and convince the entire company to act on our findings. Data scientists have relied on visualization dashboards for much of this communication. They are great for extracting large amounts of information into snapshots, but they fail as storytelling tools. Replacing them are products that enable far more sophisticated storytelling. It’s exciting at first, but storytelling tools aren’t a panacea. Ultimately, storytelling requires a shift in data scientist mindset. A data scientist must embrace and hone the skills and techniques necessary to successfully convince an audience of findings derived from data.

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