Data storytelling: Using narratives to build engaging stories for your business

Radhika Madhavan

Director of Marketing

Table of Contents

What is data storytelling?

Data storytelling refers to the ability to communicate information from a dataset using compelling narratives—and visualizations. 

Most readers find it challenging to understand complicated datasets even when using effective visualizations like charts, graphs, etc. The reason is that they usually lack the necessary context and are written in a way only a knowledgeable audience would understand.

The data storytelling approach fixes this issue. It helps put the dataset’s information into a narrative accessible to your audience.

There are three components in this approach:

  • Data: Your data acts as the foundation for the story. Once you’ve analyzed the data, you can identify patterns, trends, and angles for your next story. You can create a fruitful narrative for your readers based on this information. You can analyze the data using techniques like diagnostic, prescriptive, predictive, and descriptive analytics—to truly understand what it has to say.
  • Visualizations: Visual representation is vital as it supports the story you’re building. The data visualizations can be in the form of images, infographics, videos, animated graphics, and more. 
  • Narrative: Based on the story angle you’ve identified; you can build the narrative or storyline for your audience. It’s key to keep your audience at the center of the story and end with actionable insights to solve the issue they’re facing—because the data is there to help them make decisions—not just tell them the state of things.

This article will discuss the specifics of why data storytelling is important in today’s business environment—with actionable steps to create your next story.

Why is data storytelling important for business?

A survey of 500 executive and data professionals indicated that 82% of these respondents use dashboards to share data insights. Yet 53% of them indicated that they don’t use these dashboards because they cannot interpret the data—even though it’s visualized.

It shows the importance of using an accessible format to deliver critical information.

Stakeholders can access information in a format that’s familiar to them. Everybody loves stories—and it’s a part of our social cohesion as a society. Stories sell, so why not use them to your advantage?

As humans, we tend to remember stories a lot more easily compared to dry facts. The reason is that reading an engaging story releases positive hormones like oxytocin and serotonin in our bodies. It’s why we feel happy when we watched a gripping movie or read a stimulating book.

If humans, in general, are more attuned to understanding stories, it only makes sense to incorporate such methods in your marketing efforts. At the end of it, you’re marketing to humans—so leverage techniques that will help you connect with them.

Business leaders are always looking for context, or rather the “why” behind their data—and rightfully so. Even a recent report by Gartner indicated that only 1 out of 5 analytical insights leads to business outcomes. 

Generating and analyzing data is an expensive process which is why data is called the “new oil .”The idea is to maximize the value of the data you’ve generated—and by addressing the “why,” you’re one step closer to it.

8 steps to create a compelling story for your business

Now that we’ve established why data storytelling is essential for companies—let’s look at how you can create a stellar data-backed story.

1. Decide the objective of your story—and set the base

Once you’ve analyzed the raw data from your study, you’ll be able to identify certain angles and trends. Businesses generate data using a specific objective, so think about how it would be useful for your audience.

Create a user persona based on similar questions—and set the objective of your story. Here are a few questions you can answer:

  • Do the stakeholders think about specific KPIs?
  • Are they interested in understanding the industry landscape?
  • Are they looking for high-level reporting or a deep dive?
  • How can your data’s insights help them?

It’ll help you understand what your audience wants—and how you need to measure the success of your story. Once your objective is set, you can start crafting the rest of your story.

2. Provide context based on your industry

It’s essential to add context to your story—without which it’ll be hard for your audience to understand why this story is necessary. It sets the backdrop for the narrative and helps you communicate critical information before the reader dives in.

An excellent way to do this is by addressing common pain points and the current state of the industry. You can reference previous research studies or benchmark reports to open a loop in the story. It can create curiosity for the reader—and keep them hooked throughout.

3. Create an engaging narrative for your reader

Once you’ve opened a loop in your story, you need to bridge the gap between where your audience is—and where they need to be. Ideally, your data’s insights should be able to do that. The challenge is to get your audience to read the whole story.

You can start by identifying the story’s components. Let’s say you’re writing an article on how a legal documentation product can save thousands of billed hours by automating document classification and review. Here’s how you’ll incorporate it into your story:

a. Characters

They’re the protagonists of your story. In this case, there are two different audiences. The first is the workforce employed to sort these documents manually, and the second is the company’s stakeholders who are investing in this workforce.

You don’t have to call them out in the story explicitly, but at least you know who it’s targeted towards.

b. Setting

You can set the scene by talking about how lawyers and paralegals spend days, if not months, segregating documents and analyzing manually to extract crucial data. You can even use an animated data visualization to show the exact number of hours spent—and the number of incoming documents in a day.

c. Conflict

Here, you’ll explain the root cause of the problem. In this case, the lack of a data extraction process and the huge influx of documents makes it harder to solve. It wastes too much time and money on aspects you can eliminate. 

d. Resolution

Here, you present your solution. You can discuss how software powered by intelligence augmentation can help extract data from different documents, classify them, and store them.

Not just that, it can also create a timeline of events, saving them thousands of hours in legal analysis. Support these points using data visualizations of how many hours are saved and what the classified documents look like.

Similarly, you can decide which aspect of your story comes under each component—and build a narrative based on that.

4. Combine hard-hitting facts with your narrative

It’s easier for people to register what you’re saying when you add the necessary visuals. Your visuals should include the analyzed data along a scale that they understand. Here, using standardized units depending on the data type is crucial—but you can compare it with analogies for better understanding.

For example, it’s difficult for stakeholders to truly understand what a 63% decrease in billed hours means, but a reduction of $5000 in monthly bills would convince them more easily. It helps them know what the data is saying and how it applies to their business context.

5. Understand your audience’s needs & perspective

One dataset doesn’t always cater to one audience. It’s why you need to segment your audience based on who you’re targeting and the story’s final takeaway.

Not all readers would have the same level of understanding, but the dataset is relevant to multiple factions of the industry. So, it’s essential to segment your audience (beginner, generalist, executive, expert) and then create various stories keeping the end reader in mind—and in this case, the protagonist. 

6. Create pleasing visuals that support your story

A picture is worth a thousand words. In data storytelling, it’s even truer. It can act as a great branding exercise and distills your argument in an engaging format. Use clear graphics with a marked scale to ensure that you maintain the accuracy of the dataset.

Here are a few tools to help you create these visuals:

  • Tableau
  • Microsoft Power BI
  • Microsoft Excel
  • Whatagraph
  • Datawrapper
  • Visme
  • Infogram
  • Datapine

7. Be honest with what you portray

It’s easy to mess up your numbers and even exaggerate a little. But this could do more harm than good. It’s best not to mislead with fractioned data and portray bigger numbers just because you want to round them off.

Another aspect to note is that every study has limitations and negatives—be honest about it and add a side note for the reader too. Let them know in which contexts the results won’t apply.

8. Be concise and precise

Even though it’s storytelling, you don’t have to write a 10000-word essay on it.

Instead, keep the message short, crisp, and accurate. As long as it delivers the right message, you’re good to go.

Here’s how we do it at High Peak Software (A case study)

High Peak created a data visualization software that helps legal firms like 44th Street Technologies improve their internal operations. It does everything from automating its document classification to extracting data using Optical Character Recognition (OCR) technique.

In the case study, we set the premise by showing how lawyers (the characters) struggled with multiple challenges in the manual review process (setting). Some of them could include spending thousands of hours reviewing materials, rising overhead costs (labor + storage space), and more (the conflict).

After presenting the facts and numbers, we presented the solution (resolution) to their problems—the matter intelligence augmentation system. It did everything they wanted—and more. It automated all their internal workflows, and gave them more time to do the tasks that mattered. In this case, it was making the case for their clients.

We combined it with clear visuals of each of the features and stellar testimonials from the users themselves. It solidified the entire narrative for our readers and was backed with precise visuals on what to expect. Here’s an example of one of its features below.

A picture depicting the timelines of events from a single legal case as a data storytelling example
Timelines of events from a single case
You can read the entire case study on our blog—[Case Study] High Peak enables 44th Street Tech with cutting-edge legal tech platform and interactive data visualization.

Other examples of impactful data storytelling

Here are a few ways in which several industries have used the data storytelling approach to create compelling stories.

1. Healthcare

COVID-19 reporting was chock full of stories, which helped in drilling the message into the public domain. But it’s a result of a lesson learned from old cases of health reporting.

Dr. Ignaz Semmelweis (Father of Hand Hygiene) conducted studies in 1844 to prove that hand washing can save lives. Yet, he could not convince his superiors and the general public of its benefits. 

On the other hand, Dr. John Snow (a U.K physician) could convince local authorities to shut down a local water pump when he realized it spread contaminated water.

So, what was the difference? Data storytelling. Dr. Snow had created an entire narrative on how his research data pinpointed the root cause of the issue—and supported it with background information on how it could’ve happened. But, Dr. Semmelweis focused on the numbers, not the story behind them.

Data visualization of the pump that causes water contamination issues (as identified by Dr. Snow)
Data visualization of the pump that causes water contamination issues (as identified by Dr. Snow)Source

2. Food

Google was interested in understanding the seasonal trends for different kinds of food. So, to help companies identify that, they analyzed search data for various food items from 2004 to 2018—and created the Rhythm of Food.

Here, you can find what’s popular, when, and the trends to expect.

Data visualization of different food items and what their demand looks like in June every year
Data visualization of different food items and what their demand looks like in June every year (Source)

3. Finance

In this case study, High Peak talks about how they created an invoice factoring automation software that helps businesses cut overhead costs—and extract data easily.

Using OCR, the software extracts information from unstructured and structured data—and classifies them as needed. 

The case study depicts precisely what the product’s features look like—and how it performs the necessary functions. Using those visuals, they tie it with the impact numbers (given below), which makes it a powerful and convincing story.

Data visualization of the impact numbers achieved using the invoice automation software 
Data visualization of the impact numbers achieved using the invoice automation software 

4. Marketing/ Advertising

Wordstream released their 2021 Advertising Benchmark Report that analyzed advertising trends for 21 industries. They created separate graphics for each industry for simple metrics such as average click-through rate, cost per click, and more.

It’s an excellent example of creating streamlined visuals for similar metrics but categorizing them based on a specific category. They also coupled relevant analogies depending on the industry throughout the article to make it an engaging read.

Data visualization of the different advertising metrics observed in the Computer & Electronics industry in 2021
Data visualization of the different advertising metrics observed in the Computer & Electronics industry in 2021 (Source)

1. Focus on automated data storytelling

A report by Gartner indicated that by 2025, 75% of data stories would be automatically generated. 

Because of the increasing need to create stories out of existing data, technology vendors are focusing on using automation to achieve this. They use augmented analytics techniques like Natural Language Generation (NLG). 

We’re not saying that it would replace humans entirely, but rather it can quickly identify insights, and then data analysts can build meaningful stories out of it.

2. Moving away from the dashboard to strong narratives

As mentioned, while stakeholders share insights using dashboards, they’re not necessarily absorbing the information. Dashboards weren’t designed to convey information—but to analyze them.

It’s why they’re not eliminating dashboards. Instead, they will combine short snippets like articles with these visualizations to add context to the data they see.

3. Increasing need for training on data storytelling

Companies and business leaders have already identified the need for data storytelling. It also means that they need to focus on training employees on the necessary skills for the same. 

If given a chance, 78% of surveyed employees in this field are willing to invest the time to improve their data skills. For this purpose, each company would need a data champion like a Chief Data Officer (CDO) to oversee the training programs and ensure accurate information is being passed on. 


The adoption of the data storytelling approach is rising for a good reason. It helps businesses build engaging narratives around the data they generate—which in turn helps them make critical data-driven decisions.

A thoughtful and meaningful approach to educating your reader is essential in today’s competitive world. It helps establish a connection with your audience and increases their trust in your capabilities. Combining factual data with compelling stories can help you make the most of your data—and be memorable to your audience.

If you’re looking for a content marketing partner to help you create such compelling content, get in touch with us today!

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