Data Visualization and Story Telling (Part 2)

August 10, 2020 Rishabh Saxena

Data visualization is not something that is taught in schools or colleges, and in addition to that, very few people feel naturally adept in this space. While there are numerous tools in the market which can help make charts after crunching data at backend, converting them into a story with insights still remains a challenge. This blog will focus on the methodologies to smoothen out the practice of data visualization and storytelling.

In order to effectively communicate with data, key lessons that you should stick to can be charted out as below:

Let’s discuss the lessons in a little detail.

  1. Understand the context: Before you begin down the path of creating a data visualization, it is important to spend time and attention towards understanding the context to start in the right direction.

    Three basic questions for getting the context are who? what? and how?
    •    WHO is your audience: The more specific you can know about your audience, the better position you will be in for a successful communication.

    •    WHAT do you want to tell: What you tell the audience should be relevant to them and should help them take next steps using your findings. However, the amount of information to be shown depends on the mode of communication, like whether it is a live presentation or perhaps an email or document.

    Source: Storytelling with Data, A data visualization guide for business professionals

    In case of live presentations, you have high control and can drive your story without much data on the slides or document, while in case of written documents or emails, with limited audience control, the amount of detail that is needed is typically higher.

    •    HOW do you want to tell your story: Most important of all is to figure out how you want to tell your story basis the data you have

  2. Choose appropriate visual: While there is a multitude of graphs/ visuals available to choose from, for practical purposes, 15 – 20 of them would work for the greater part of your needs. Based on what you want to show, a simple guide to opting for the most appropriate visual format is shown below.

    Source: Datapine - Designing Charts and Graphs: How to Choose the Right Data Visualization Types

    In addition to different chart types, simple text can also be used to highlight a couple of numbers or KPIs in text format as shown below.

  3. Remove the clutter: In a visual display, there are many redundant elements which increase the cognitive load and can be removed to improve the visual without losing necessary information. This can be explained with a simple example while showing scores of 8 candidates on 2 unique skills.

  4. Design the content and visuals: Have you ever said to yourself, I am not a designer; how can I do that?”. This approach needs to change. There are just 3 designing fundamentals that you need to know, and they can help your visuals look smarter: thus, creating a greater impact on the audience. The three basic fundamentals are:

    •    Choose colors smartly: The use of color should always be an intentional decision; use colors strategically to highlight the important parts of your visual. If you are working on a PowerPoint presentation, try to stick to standard color templates to make a professional document.

    Here is an example of wise choice of colors while showing the performance of applicants in a job assessment.

    While the amount of information given is similar in both the cases, the latter is easier to process for the audience.


    •     Don’t forget to align: Organize elements on the page to create clean vertical and horizontal lines to establish a sense of unity and cohesion.

    For example, if you are putting multiple charts or boxes one down the another, distribute them vertically with equal spaces between each of them.

    •    Don’t over burden slides: Keep margins in mind while putting content and visuals in the document/ deck. Graphics need not be stretched to fill the space, and things shouldn’t be added to occupy the white space available unnecessarily.

    If there is still a lot of space available, try putting some designing effects which would make the slide professional and non-empty without increasing the cognitive load of the content.

    Here is an example to understand this better.

  5. Storytelling: While the art of storytelling is in itself a huge topic, I would like to summarize it in the data storytelling context. Putting your point/ insights in a story structure is a very effective method and has been in practice for a very long time.

    Each story has 3 aspects: the beginning (plot), the middle (twists), and the end (call to action). In the data storytelling context, these can be summarized as:
  • The plot is the problem statement or agenda for doing the analysis.
  • The twists are the insights that you have found – try including and emphasizing any strange trends.
  • The call to action should represent the recommendations/ next steps coming out of your story.

     Other quick tips which should be kept in mind while telling the story can be:

  • Using repetition of certain words to emphasize your story/ finding.
  • Structuring the story in such a way that your audience is the main character; it should feel like the story is about the audience.
  • Putting the main insight or twist on top of the slide and detailing it out using content and data.
  • Practicing your story before presenting it.

As of now, we know the key hit areas for putting together a great story and the basics of how one can tell a story. In the final part of this series, we will be taking up storytelling in detail.

Happy data storytelling!!

Are you interested in data visualization? Read the previous and next article of this series!

About the Author

Rishabh Saxena

Rishabh Saxena is Senior Business Analyst at Aon. He graduated from IIT (BHU) Varanasi and did an Executive Program focused in Business Analytics from IIM Calcutta. Rishabh has a background in insights generation and data visualization through Tableau, PowerBI and Market research.

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