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Organizations that harvest massive amounts of data are continually seeking ways to sort and interpret the information. These companies are swimming in a sea of big data and can do a lot more with their information if they understand it better and determine the best ways to present it. While businesses continue to invest big money into business intelligence programs and analytics tools, they aren’t necessarily getting their money’s worth from the information they are getting.
The purpose of data analysis to extract valuable insights and improve business decision-making. Business intelligence offers discovery tools such as data visualizations but can only explain what data trends are forming. While these data visualizations tend to answer the “what” questions, they can’t explain the “why,” or provide any contextual information. Data storytelling is the bridge that can help make the connection between what the trends are and why they are happening. Data storytelling is crucial to the success of data analytics. Data storytelling joins data and visualizations to form a narrative that conveys data credibility, presents compelling insights, and spurs actionable results. A good data story can transform analytical results. Let’s take a look at how to tell stories with data.
Following Storytelling Rules
Effective data storytelling is very similar to storytelling in general. A good story should have a beginning, a middle, and an end. A good data story should include those elements. It should also include a question, supporting facts, a logical structure, and a compelling interpretation. A common mistake in data storytelling, however, is spending too much time on the methodology and not providing an element of creativity in pointing out how the data can help the business make the right decisions. While data visualization tools are effective, the data story can provide context, interpret results, and present data insights and opportunities. Following general storytelling rules is a critical factor to influence key stakeholders and impact strategic decisions.
Knowing Your Audience
Solid data analysis can be built on a strong foundation but can quickly unravel when assumptions are made about how analytical results should be presented. One common mistake that some data scientists make is to build a one-size-fits-all presentation that doesn’t fall in line with the needs of a particular audience. Knowing your audience and their point of view is key when presenting data insights. Often, the data visuals and insights will make sense to those individuals making the reports but not to those who might read them. A good rule about data storytelling, as with any storytelling, is to have your audience in mind when writing the narrative of the data story.
Using Collaborative Methods
There are different opinions about who is responsible for creating data stories. Sometimes, the best analytical minds like data scientists aren’t necessarily the best storytellers. Likewise, the best storytellers aren’t necessarily the best data scientists or business analysts. This is why corporations could have data scientists, business analysts, and marketers collaborate on a data story. With several collaborators will come several versions of the same story. Like going through the editing process of a creative writing piece, multiple versions of the story will be whittled down to the story that presents the most important insights in the best way possible.
Being Concise
Good data stories include accurate analytics information that makes a case for data-driven decisions. But there also has to be balance so as not to provide so much information that the audience gets lost in the data science. Data storytelling needs to address a specific goal and rely solely on data and findings that support the goal. A compelling data story avoids clouding the narrative with extra findings and ancillary material that doesn’t address the objective. Simply put, it is best not to distract the audience. A good story is clear, simple, and impactful. Ineffective data stories fail to get to the point fast enough. They also spend far too much time explaining some kind of back story. When doing data storytelling, it is best to stick to the storytelling format of beginning, middle, and end and present only relevant data.
Data storytelling is an essential skill for data analysts and the best strategy for storytelling is to let the data tell the story. The best data narratives will avoid trying to use data to justify an existing decision or tell an existing story. If lead by the data, the data story should tell itself. In order to impact business decisions and present impactful data insights, it is vital to tell a good story with the data.
