Raw data isn’t something ordinary humans can read. Even plots can seem meaningless without proper context. No matter how good your MS Excel skills are or how thorough you are in creating your presentation, it never feels complete.
You need to add a human element to bridge the gap between computerized data and your human audience. This human element is the story you tell to help your audience interpret your data in terms of things they care about.
In this guide, we’ll give you the fundamentals of data storytelling and walk you through the process of creating a narrative about your analysis—but first, let’s take a quick look at what data storytelling is and why it’s important.
Data Storytelling: What It Is And Why It’s Important
Data storytelling is the ability to communicate the results of data analysis in layperson terms effectively. The goal is to present all the key insights to the audience clearly and concisely to convince the decision-makers to consider these insights. Data storytelling can include narratives, phrasing, illustrations, and motion graphics.
Whether you’re a small business or a large corporation, the results of your data analysis must get properly communicated to the relevant people. This can include business runners, stakeholders, investors, donators, and the general audience.
Even if some personnel in the audience are qualified to interpret data, you have to assume that most won’t have a single clue about what they’re looking at. Communicating your findings to lay people in simple language is a part of your job.
The Fundamentals of Data Storytelling
There are three elements fundamental to data storytelling that you can’t do without; data, narrative, and visual representation.
Data
This is where you decide what information to show and what’s irrelevant. You don’t want to over-explain things and overburden the audience with useless facts, but you also don’t want to give so little that it looks like you don’t have any data to support your claims. If you used big data analysis, you can’t possibly show all the data you used anyway. Just stick to the results of the analysis in this case.
Narrative
This is where you connect your data to factors that matter to the audience. The story forms the written and verbal parts of your presentation. You first have to give background information about your research area before communicating your findings. Interpreting your findings and offering possible action plans are also part of the story. We’ll get into these details later in this article.
Visual Representation
Different types of graphics are suited to conveying different types of information. X-Y plots are a good way to show an association between two variables, column charts are best to show time progression, and pie charts show distributions of limited resources like no other. Visual representation of data can help you bridge the gaps between the data and your story, making it easier to convey your ideas to the audience.
Creating a Data Story
Create a Background
The first part of your data story focuses on commuting all the background information needed to put your research into perspective. It may prove different to guess how much the general audience knows about your area of research. You can interview someone who will sit in the presentation and create the background based on what they don’t know.
Report Your Findings
You can show your data and report your findings once you’ve communicated everything your audience needed to know. You can use technical language in the part of the story since you’ll be going through it all again in the next part.
Interpret the Consequences
Once the gist of your data analysis is out there, it’s time to relate your findings to the background. Interpret the results and explain what it means for your organization. This is the most important part of your presentation since this is when you’ll convince the audience of the importance of your results.
Suggest Suitable Responses
If you can offer a good course of action based on the data and your expertise, end the story with that suggestion. This part is optional since it’s not a part of the data analysis.
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