Introduction
Data, we hear about it every single day. What is it? Data is a collection of facts that describe something. Today, we are surrounded by data. It’s big and complicated. According to Forbes, more data has been produced in the years 2016 and 2017 than in the entire previous history of the human race. That’s shocking, isn’t it? But, most of the data that is produced is not used properly. The data experts who are trying to extract meaning from data and interpret it are good data storytellers. The key to being a good data storyteller is a focused understanding of the problem you are trying to solve. So, what actually is data storytelling?
What is data storytelling?
Data Storytelling is all about making the decision-makers in organizations understand the data analyses by data experts. The decision-makers will not have the ability to understand and interpret data, so it’s basically connecting the dots between them and the complex data analyses. So, organizations can make informed decisions if the data experts tell a compelling story and put their point across.
While businesses invest a lot of money in analytics and BI (business intelligence) tools, they are not acquiring the information they actually need to improve decision making. We can understand the complex information by transforming it into data visualizations but different people interpret them differently. So, data visualizations will not answer all the questions and fail to provide contextual information. Data storytelling solves this problem. Data storytelling translates the analysis of data into simpler storytelling like terms so that any type of individual can understand it.
Ryan Fuller, the general manager at Microsoft says, “Data storytelling weaves data and visualizations into a narrative tailored to a specific audience in order to convey credibility in the analytical approach, confidence in the results, and a compelling set of insights that is actionable to the audience.” Thus, the data experts emphasize critical elements to create convincing stories and thus support stakeholders in making business decisions.
The Process of Data Storytelling
Let’s see how you can tell a story with data.
When you have a lot of data, finding a story about the data analysis is extremely difficult. So, how do you do it? Finding a story becomes easier with proper insight into the challenges faced by the audience and the questions they need to answer. So, if you have a proper understanding of the challenges and problems, you can tell a compelling story.
Next comes the audience; the people to whom you are telling the story. It is important to remember your key stakeholders. It is not just the story’s subject, there should also exist appropriate terminology and relevant acronyms. There is no point in telling a great story if your audience doesn’t understand it.
You need to make sure you have the right data that supports your facts. You can say that particular insights are important, but is your data factual? So, your data needs to bring credibility while story should add engagement.
You need to use appropriate visuals - graphs, maps and charts that enable the audience to instantly see the data trends and spot patterns. When visuals are applied to data, they should make the audience clearly understand the data insights.
So, you have the right data. But, you should also see that whether you are interpreting it correctly or not. Now that you have got your audience hooked to your story, allow them to tell the story themselves or tell their own stories. Automated reporting and self-service analytics encourage this interaction.
Stories entertain us and create emotion. So, ensure that your data tells a story that drives a change, influences, and engages the audience.
Common Mistakes in Data Storytelling
This section points out the common mistakes that few storytellers make. Let’s have a look at them.
One of the major mistakes in data storytelling is not getting to the point quickly. This is because storytellers spend so much time on data analysis, as so much time was invested in this behind the scenes, except If you find the right data analysis tool. However, the explanation of effort that is put in and the effort itself needs to be weighed in a different manner. Complex graphs and charts that don’t offer context are not helpful. So, take out the most important points and utilize data to back it up.
Another mistake is misleading stories. If you tell your story with lack of clarity, or a lack of context, the decisions taken based on the story will affect the business badly.
The third mistake is the usage of different conventions, labels, and colors in your story’s visualizations and statements. This is a mistake because doing so won’t create a natural language for your data and it becomes difficult for the audience to learn.
So, common mistakes like these must be avoided to tell an effective story with your data.
Conclusion
Data storytelling takes data visualization to another level by inviting conversation, offering an interpretation, and connecting visuals with a narrative. Data stories must address a particular goal and depend only on data that actually supports that goal. The data storytellers will make their story hard for the readers to understand if they cloud it with findings that don’t address the actual objective of the analysis. So, the stories must always be impactful, simple, and clear.
Author Bio: Savaram Ravindra is working as a Content Contributor for Mindmajix.com. His passion lies in writing articles on different niches which include some of the most innovative and emerging software technologies, digital marketing, businesses, and so on. Follow him on LinkedIn and Twitter.
