What is data storytelling?

A narrative that simplifies complex insights from the data can effectively and efficiently communicate information in a way that most people can understand. While many analysts understand the concept of telling a story with data, too many fall short in delivering a solid narrative around their analyses. And insights that are not communicated and do not lead to business action are essentially worthless.

A good data storyteller will craft a narrative around the process and outcome that engages non-data experts within an organization. People love stories because they add emotion and are more compelling than simple logic. So, turning data and analytics into stories that everyone can understand helps to make the insights more accessible and actionable for an organization. It is an essential skill for a data scientist or business analyst to master today. Especially as big data analysis becomes more popular, insights will need to be communicated to those who have had no interaction with the data but need to be familiar with it and understand what it means for the company.

Why is data storytelling important?

Stories make information more memorable, persuasive, and engaging for audiences unfamiliar with the details being shared. While charts and graphs can present information and show what is happening, only a great story around that information can tell the audience why those trends are occurring and why they are important to the organization. Stories hold immense power of the human mind and are therefore an excellent tool for sharing information. Stories capture attention and keep it.

While it may take greater effort to craft a clear narrative around the data and insights, it can save time and money through faster decision making. Data storytelling is more persuasive in representing an idea leading to less uncertainty and more decisiveness. It also presents a clear path for discussion and prevents wasted time on unimportant details. In many ways, it is the most effective form of sharing information within an organization.

If data storytelling is not employed, significant information could be lost or misunderstood. For companies who have made significant investments in analytics and data science, the lose of valuable insights can be extremely costly over time. But this is a very common problem for organizations today: struggling to deliver insights from their data to the business for faster, smarter actions. Although the rate of data being generated today continues to grow, that data is too often collected but never fully taken advantage of. Data storytelling allows organizations to discover more valuable insights and communicate them more effectively.

The benefits of data storytelling

Successful data storytelling can easily transform a company. Data storytelling represents a huge opportunity to break down silos between business teams and data experts, leading to more information sharing and better business outcomes. Companies that employ data storytelling can gain a competitive advantage, capitalizing off of the following benefits:

• Valuable Insights: Insights are arguably a company’s most valuable asset. Losing that value due to problems in communication is unacceptable. Data-driven companies need a data storytelling culture to move from insights to action faster. With stronger data stories, analysts and data scientists can present the business with a clear picture of the situation and a direction to move forward.
• Focused Discussions: Instead of an hour-long discussion that is full of tangents, data should be presented in a way that focuses the discussion and ends with key actions to take. Data storytelling can help with this by targeting the message and narrative around the data and insights found in the data, clearly indicating why they are so important.
• Increased Collaboration: By speaking the language of their audience, data experts can more effectively communicate and collaborate with other teams and stakeholders throughout the organization. This increased collaboration can lead to greater innovation and better cooperation in solving problems together.
• Faster, Better Decision Making: Not only will stories clear up confusion and make teams more decisive when determining between different alternatives, it will also help teams act smarter with data guiding their thinking. Instead of working off gut instincts, the business can better understand what the data is telling them and make more intelligent, data-informed decisions.

How to start telling stories with data

While data storytelling can be a powerful tool, like any other business tool it requires some instruction and practice before it can be implemented successfully. Read on for helpful guidance on how to start using your data to tell stories.

Transform Metrics into Concepts: To tell a story with your data, you will need to turn metrics and quantitative data into actionable concepts or insights. This can be done by thinking of metrics as key performance indicators (KPIs) and pulling them into a narrative about why they are important to driving business success and reaching certain set goals. This can breath new life into your data and add a level of importance to the discussion of basic metrics.

Convey a Compelling Plot Line: Every interesting story needs a plot that is easy to follow and compelling to the desired audience. By pulling in different visuals and outline a story that follows the data from beginning to end, you can add logic to a discussion and emphasize important points.

Present Simple Ideas: Data storytelling can help you to simplify the story you are trying to tell and focus on the dialogue you are hoping to open concerning the data. By being strategic and straightforward, you can make it easier for audiences to understand and leave with key takeaways.

Data storytelling dos and don’ts

Do: Craft an impactful story that offers key takeaways
Don’t: Present a single fact and expect action

Do: Lead the audience to the main point
Don’t: Lose the logic in the emotional message

Do: Add emotion along with the logic
Don’t: Get distracted by inconsequential tangents

Do: Drive the message with data always
Don’t: Make leaps in your story that don’t follow the data

Do: Try different perspective of viewing the data
Don’t: Assume that the data can only be viewed one way