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Effective Data Storytelling Through Interactive Visualizations
Back to POV

Effective Data Storytelling Through Interactive Visualizations

By Jake Stevens

The total amount of data created and consumed worldwide is forecasted to increase rapidly over the next few years, from 59 zettabytes in 2020 to an estimated 149 zettabytes by 2024. What’s a zettabyte, you ask? A zettabyte is a measure of storage capacity that is 2 to the 70th power bytes or 1,000,000,000,000,000,000,000 bytes. While it’s hard to comprehend an astronomical number this large, just know there is a lot of data out there, and the amount of data is growing exponentially.

In a 2009 interview, Google’s Chief Economist Dr. Hal R.Varian predicted, "The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades." Fast forward to today and that statement couldn’t be more accurate.

To extract value from large and complex datasets, data visuals have evolved over the past decade from static charts and graphs to interactive and immersive visuals that tell a story. This allows the audience to modify elements of the data being presented and manipulate the graphical representation. Static charts and graphs do not have the capability to adjust the visual, such as hovering, sorting and scaling. Interactive visualizations allow users to generate transformative insights, identify relationships, view trends and create meaningful stories through data.

What is data storytelling?

Data storytelling is the execution of describing data through visualizations by building a compelling narrative around a dataset. This adds meaningful context to the data and helps the audience easily understand the information.

"The skill of data storytelling is removing the noise and focusing people’s attention on the key insights."
Brent Dykes – Data Strategy Consultant

The Need for Data Storytelling

Data storytelling is a useful way to simplify complex datasets. The main goal of the visual is to make actionable insights from the data that are engaging and easy to understand and absorb.


In a world where we are besieged by data, it’s hard to grab an audience’s attention with static data graphics. Data storytelling engages an audience by allowing them to interact with a graphic or view the information in a logical and visually interesting flow.

For example, the static image below shows energy consumption in the United States from 1776-2019. This image shows a lot of statistics at once. Without including any additional data, the graphic can be transformed into an engaging visual that keeps the audience’s attention. The moving line chart allows the audience to focus on change through time. Additionally, this graphic has interactive features; clicking on one of the lines highlights it and lets you focus on the story of one of the variables.


It is easier to remember data when presented in a story than single data points. An experiment by Stanford professor Chip Heath found that 63% of participants could remember stories, but only 5% could remember a single statistic.

For example, to show the U.S. energy consumption by end-use, the static image below represents the data. By including an additional 4 data points for each sector, we were able to transform this data into an interactive visual. This allows for new insights by letting the user interact with each branch inside the tree map. By spending more time with the visual and clicking through each branch of the graphic, the engaged user is more likely to remember the content.

Easier to Understand

Communicating your story effectively means you convey a story with as minimal data as possible. Key insights, even those critical to make business decisions, can be ignored or overlooked when too much data is presented at once. When insights are translated into a data story, it allows the information to be easily understood and translated into data-driven decisions.

The data visual below takes a large dataset and gives the user the ability to choose how they view and consume the information.

Characteristics of Interactive Data Visualization

Static visualizations do not have the ability to make adjustments to gain further insights, such as hovering, sorting and scaling. Interactive data visuals use these characteristics to convey their story effectively to their audience and allow for additional insights to be uncovered.

“If you want people to make the right decisions with data, you have to get in their head in a way they understand."
Miro Kazakoff – Lecturer, MIT Sloan


Hovering or identification refers to using your mouse to hover over an element in your visual to view additional information or metadata. This technique can also be used to view labels attached to data elements. Hovering over an element in a graphic to view the metadata attached allows the audience to explore the data and generate new insights.


When your data can be separated into categories or groups, sorting and filtering are techniques that allow you to connect aspects of your data. Sorting your data into groups lets you link any similar data. For example, if different markets are in the data, sorting by sector brings additional insight. Interactive data visualizations allow you to sort data and adjust the story being told in your visual.


Scaling lets the audience view the plot from a different perspective. When your visual has many data points, such as a scatter plot or icon map, it can be challenging to view specific points. Scaling allows the user to zoom in on densely populated regions to gather insights that were not previously visible.

Telling a Great Story Through Data

At Hahn Agency, we believe great data storytelling is the most efficient way to help your audience understand and engage with your data. It can act as a language that everyone can comprehend, not just those with degrees in data analytics. After all, people remember stories, not data.


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