Many business intelligence decisions and activities are based on data. Data will help you tell a convincing story that supports your stance and shows the importance of your goods or services. Whether you’re engaging with an internal team or a customer the use of crafted storytelling techniques will resonate within your content and engage your desired audience.
Raw data, on the other hand, is incapable of completing the task on its own. You can’t just hand the audience a bunch of spreadsheets with rows of numbers and expect them to find out what you’re trying to say or act on it. To get to the point and fulfil your goals, you must deliver the details in a convincing manner that engages your audience.
This is where data-driven storytelling comes in, transforming data into a strong, engaging, and persuasive communication tool. Let’s take a look at what data-driven storytelling is, the advantages of doing so, how to efficiently use data to tell a story, and some data visualisation examples.
- What is Data-Driven Storytelling?
- The Benefits of Data-Driven Storytelling
- Best Practices for Telling an Effective Data Story with Data Visualisation
- 1. Develop Your Goals and Get to Know Your Audience
- 2. Come up with a plausible story
- 3. Incorporate Key Elements of Analysis Storytelling
- 4. Be Objective and Transparent
- 5. Choose the Best Data Visualisation Method
- 6. Adhere to Best Practices in Visual Design
- 7. Make Use of the Appropriate Data Visualisation Software
- 8. Make Your Data Story Insightful and Human
- 9. Create Synergies Between Content and Data Storytelling
What is Data-Driven Storytelling?
Data storytelling is the practice of blending hard data with human communication to craft an engaging narrative that’s anchored by facts. It uses data visualization techniques (e.g., charts and images) to help convey the meaning of the data in a way that’s compelling and relevant to the audience.
The process of analysing and filtering massive datasets to discover insights and expose new or different ways to interpret the information results in data-driven stories.
They’re made for a particular audience and consumed in a specific way. This can help you convey knowledge or a point of view efficiently while producing the least amount of cognitive load, which influences how much mental energy the audience requires to understand your message and, as a result, how well it’s received.
The Benefits of Data-Driven Storytelling
In today’s world, where we’re bombarded with data and can’t seem to make sense of it, data-driven storytelling is a useful tool that not only introduces the data but also adds context, significance, relevance, and clarification to help the viewer understand and draw value from it.
Some of the advantages of using data storytelling as a communication method are as follows:
- Data stories add value by giving data significance and context, enabling the viewer to link the dots and translate statistics into actionable insights. As a result, the experiences help people make better decisions and take action.
- You will strengthen the authenticity of your content by using statistics and evidence to back up your statements. This increases your audience’s confidence in you and their probability of being persuaded by your point of view.
- Data stories built with internal and proprietary data help you stand out and get noticed. In a world full of regurgitated material, the original ideas, valuable perspectives, and surprising angles allow you to cut through the clutter.
- Since the graphic elements appeal to the media, the material is more likely to be picked up by high-profile outlets or influencers. This will help you raise brand awareness, reach new markets, and establish your brand as an authority figure.
- The combination of narrative and visual elements engages both sides of the brain, creating an intellectual and emotional experience that helps your audience retain knowledge through understanding, retention, and appeal.
- Using different strategies, such as interactive data visualisation, can help to improve audience interaction. By leading them to a conclusion or exploring the part of the data story that is most important to them, for example.
- Data-driven storytelling can be used in a number of ways. Annual reports, brochures, case studies, presentations, photographs, website material, white papers, social media articles, and other external and internal communication platforms will all benefit from it.
Best Practices for Telling an Effective Data Story with Data Visualisation
Data visualisation is important for creating a data narrative that elicits emotions, engages the viewer, and motivates people to take action. Here are nine top strategies for integrating powerful visualisation into data-driven storytelling:
1. Develop Your Goals and Get to Know Your Audience
You must first identify the objective of your data story. This will allow you to spot particular patterns, concentrate on a subset of data, organise the data to support a study, demonstrate the efficacy of a strategy, or emphasise the importance of your goods.
Often, depending on the audience and their interests, you can need to tell different stories from the same collection of data. What matters to board members, for example, is unlikely to be the same as what matters to the floor workers. What the marketing department finds useful is unlikely to be the same as what the finance department finds useful.
You should also think about what your audience already knows about the topic and frame your data visualisation around that, so you can reach them where they are. Then adjust the narration and visual elements to emphasise the audience’s main message and evoke the desired behaviour.
2. Come up with a plausible story
Good stories have solid plotlines, and the same is true when using visualisation tools to tell a data tale. For instance, you might begin with a hook (such as a question or a conundrum), then lead the audience on a journey that builds momentum and culminates in a solution.
Here are some examples of data-driven narratives and data storytelling:
Trends: Generally, trend stories concentrate on how statistics change over time. A flattening pattern, on the other hand, can represent key insights that entice the audience to dig deeper. Here’s an example of global Covid deaths.
Comparison: You can explore patterns in a broad sense by analysing various sets of data and how they evolve over time. In this example, we look at the comparison of CO2 emissions between 2019 and 2020.
Rank order, also known as a league table, is a useful method for communicating hierarchy based on a number of factors in order to digest a large volume of data. Here’s an example of a league table used for digesting big data within football for the Premier League.
Statistical Relationships: You can predict how one factor will influence another by looking at the connection between sets of data. From Berkeley Earth, this example shows the number of confirmed Covid cases per million vs. Gross Domestic Product per Capita.
Counterintuitive data stories and visualisations will grab your audience’s attention, pique their interest, and encourage them to dig deeper into the material. In this example, we can see the various Digital Marketing channels being used to engage with readers in 2020.
3. Incorporate Key Elements of Analysis Storytelling
Include these key elements in your data table to make it more convincing:
- The plot: This entails the questions you’ll be answering, how you’ll react to them, and how you’ll guide the audience to the conclusion. Your data visualisation can bring your audience from point A to point B as easily and as effectively as possible.
- The context: This is the environment in which your audience can view the data. Historical evidence, current procedures, and market metrics are just a few examples. Meeting the audience where they allow your data and insight to have the greatest effect.
- The characters: The way you tell the story will be determined by the audience you’re speaking to. The story should be written in the appropriate tone, discuss the audience’s priorities, and clarify how the insights will assist them in achieving their objectives.
- The end: Come to a conclusion that is in line with the subject or problems you set out to solve. It may be a description of what the viewer has taken away from the data tale, as well as suggestions about how they should achieve improvements or what they can do better in the future to maximise outcomes.
4. Be Objective and Transparent
Even if you’re using the data to help a certain point of view, the data visualisation should present the evidence objectively. Any untruthful manipulation, whether intentional or unintentional, may lead to inconsistency, lower your credibility, and decrease the audience’s confidence in your details.
To view the data objectively, prevent ambiguity by using proper marking, matching graphic dimensions to data dimensions, and ensuring that the design elements do not compromise the data.
Additionally, when choosing details for your data tale, be objective. For example, when using arbitrary temporal ranges, capped values, volumes, or intervals, don’t use discrete values; be explicit about how you handle incomplete, outlier, or out-of-range values; and be straightforward when using arbitrary temporal ranges, capped values, volumes, or intervals.
5. Choose the Best Data Visualisation Method
Using a visualisation tool that is suitable for your data will help you better display the information and make your point. Here are some examples of popular data visualisations:
When dealing with a limited dataset, text may be used to draw attention to the main message in a clear and concise manner. Text can also be used in infographics to help the viewer understand the data and tell a more nuanced tale.
A line chart helps you to visualise changes in continuous data over time. A line chart is a valuable method for showing patterns or linear progression in a dataset. Here’s an example of a Google line map.
A bar chart is one of the most widely used tools for visualising categorical data due to its simplicity and familiarity. To suit the design of your data and demonstrate your concept, various types of bar charts (e.g., vertical, horizontal, and stacked) can be used. Here’s an example of the popularity of games that Nintendo has published on its platform and the number of units it has shipped up to September 2020.
Table: Helps the viewer to take in several levels of detail at a glance by viewing a variety of categories at once. It can also be used to present data to several stakeholders at the same time (e.g., at a corporate meeting.) In the example, you can see such a table which conveys sales figures per quarter.
Map: It displays data in a spatial format to highlight concepts such as regional patterns or the effect of a position on outcomes. In this example, Forbes tracks how Coronavirus spread across the globe in early 2020.
6. Adhere to Best Practices in Visual Design
Regardless of the visualisation approach you use, keeping your message focused requires simplicity and transparency. Reduce the friction involved in reading and decoding the graphic elements to help the audience gain full comprehension at a glance.
Use white space to focus the reader’s attention, delete visual elements that don’t add value to the plot, and highlight data vital to your thesis with bold colours. When deciding where to put the details, keep visual hierarchy in mind. In Western culture, most people read in a Z-pattern. You will help the viewer follow the knowledge more easily if you incorporate this behaviour into your design.
You can also use design elements to reduce cognitive burden, making it easier for your audience to communicate with and consume your content. For example, by being consistent in the presentation, using familiar visualisation strategies, distilling information to the essentials, and presenting the audience with relevant context or background information.
7. Make Use of the Appropriate Data Visualisation Software
Using the right tools will help you interpret data for your data stories more effectively.
Some of our favourite free data visualisation resources are listed below:
- G Suite: Google Sheets and Google Slides can be used for plotting and annotation in G-Suite. Google Data Studio also integrates with Google Analytics and has useful features for time series visualisation.
- Microsoft Power BI: With this app, you can combine data from a variety of sources to create interactive and immersive dashboards and reports.
- Tableau Public: You can get the full version of this app for free if you agree to make everything you make with it public via Tableau Gallery.
- Datawrapper: This programme, which is used by journalists in major newspapers, helps you to make charts, maps, and tables out of complex datasets.
- Open Refine: This framework cleans and transforms data as well as expanding it with web services and external data, going beyond data visualisation.
8. Make Your Data Story Insightful and Human
Healthy stories are easy to relate to. You will effectively reach the audience with the right level of specificity and evoke the desired actions by understanding their desires and emotions. To make an idea more concrete, you can use real-life situations or a personal tale to explain abstract numbers and observations.
Good stories may also be instructive. Delivering high-value content will help you break through the clutter, as most people struggle with information overload. Will your audience, for example, be able to get answers to their questions, make an educated decision, or boost a result after reading your data story?
Your data story should concentrate on a single theme and offer new insights that are useful and meaningful to your audience to catch and hold their attention. It’s important to note that it’s not just about the data, but also about how the insights will influence your audience’s lives.
9. Create Synergies Between Content and Data Storytelling
Data analysis storytelling is a perfect addition to many forms of content because it offers evidence to back up the ideas while also growing viewer interaction through visual elements. To increase the credibility of your content and place your brand as an authority, you can integrate data visualisation into documents, white papers, reports, ebooks, videos, presentations, infographics, social media posts, and more.
Your data visualisation graphics should be self-explanatory so that they can be used as a standalone image or embedded in a piece of material (e.g., on social media.) To have a consistent user experience, the design should match the brand image. You can also use immersive data visualisations to add interest and interaction, such as animation, maps, or word clouds.
Also, use social sharing to reach a wider audience and increase traffic to your content. By having social sharing buttons on your website and a call-to-action in your posts, you will encourage your readers to share the data visualisation on social media.
Data is Everywhere, so Tell Your Story
For enhancing internal and external interactions, data visualisation and storytelling with data are essential tools. It will back up your claims with evidence and give your content more legitimacy, all while engaging your audience and encouraging them to take action.
If you’re not sure where to start, the good news is that data can be found almost anywhere thanks to the numerous technology we have at our disposal for gathering data from different sources.
You may get ideas from internal sources such as website analytics, consumer surveys, and insights from various departments (e.g., advertising, product creation, and human resources) or from external sources such as business forums, Google Trends, think tank studies, research reports, and more.
You can create engaging data stories to support your concept and enrich your content by integrating trustworthy data sources with a compelling story, unique perspectives, an understanding of your audience’s needs, and powerful data visualisation techniques.
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