Telling a story to better convince your audience… Widely adopted in marketing, advertising and sales, the concept of storytelling is not new: Narration has always been used to convey messages, whether moral, philosophical or religious. But in the age of digital and big data, a new way of building a story has emerged: data storytelling.

Its premise? Transforming raw data into an interesting and coherent story.

What is data storytelling?

Many companies have made the same observation: Data is a resource that is hard to digest and difficult for non-specialists to decipher and make use of. This is where the idea of data storytelling originated, its aim being to make data usable by all.

When Data is Re-envisioned

Formatting data is not new in itself: Charts and tables have been used for many years to represent information with more clarity. However, the tools that have been used to date to achieve this objective seem increasingly outdated, even archaic (with Excel at the head of the pack).
Data storytelling on the other hand, is highly user-friendly and easy to handle, and presents a much more aesthetic and powerful approach to representing data than traditional software.

The challenge is considerable, given that human beings have a limited attention (especially during meetings). Ultimately, the message must be clear and conveyed in the blink of an eye.
But this requires an adequate format… The time of sad Excel tables and dreary PowerPoint presentations has come to an end: Thanks to business intelligence and data storytelling, a new way of communicating data is emerging—more narrative, more visual, and more personalized…

Data storytelling and data visualization

Le data storytelling est indissociable de la data visualisation, et peut même être considéré comme l’évolution de cette dernière.

Definition
Data visualization: The practice of transforming raw data into visual representations such as charts, curves, etc.
In fact, both concepts are based on the same fundamental idea: Information is more perceptible and memorable for the human brain when it is presented in an organized manner.

Therefore, the use of clear and aesthetically pleasing visual objects increases the impact of the information we want to transmit. For this reason, BI solutions using dataviz must offer their users an elegant, user-friendly interface, with fully readable dashboards.

However, data storytelling adds an additional element: Narration. Where data visualization contents itself with providing and aesthetic form to raw figures, data storytelling consists of telling stories in order to convey messages.

Data storytelling is thus based on three interconnected elements:

• • Data;
  • Narration;
  • Visuals.

The combination of data and narration makes it possible to explain and discuss information in order to contextualize it and render it more readable.
The combination of visuals and data allows for the latter to be highlighted and formalized, making it more accessible. This is the whole idea behind dataviz.

Ultimately, when narration and visual aspects are articulated, we are immersed in storytelling. We seek to captivate the audience through interesting and entertaining content in order to convey a more effective message.

It remains to be seen how data storytelling can actually benefit a company: It is much more than a tool for creating tables and charts that ‘look pretty’. It is a genuine lever for mobilizing every department of an organization, and to support managers at all levels in their decision-making.

The advantages of data storytelling

Data storytelling should not be considered a persuasive technique, but rather above all else, as a collaborative tool used to communicate more effectively within a company, and to visualize the future with more equanimity.

A story everyone can understand

The need for data storytelling stems from a problem encountered by many organizations: IT experts, data scientists, and other experts in the field often run into problems when it comes to advancing their findings and sharing them with other departments in the company (Management, Marketing, Sales, etc.) The root cause of this issue is the difficulty most involved parties find in understanding figures and analytics. Additionally, if compiling millions of pieces of data is a significant task, it is necessary to be able to present the results to a non-initiated person in a holistic manner, which is not an easy task. Fortunately, this can be remedied by telling a story based on data.

The implementation of data storytelling goes through a variety of stages. The first question that needs answering is who the target audience is (Management? Sales? Management control?), in order to understand their expectations.

Indeed, the various involved parties within a company often need to hear different stories, and varying arguments in order to be convinced: Where a management controller might be interested in the optimal allocation of the budget, a manager might want to monitor the company’s good health indicators.
Data must then be sorted, and only that information which is relevant to the given target audience is kept in order to create a story from it. Once the scenario has been decided on, the data must be formatted using textual elements and visual objects, always with the aim of creating an aesthetic and synthetic base.

Our data story is now complete and can be presented to an audience.
Only by following this process can raw figures and hard-to-digest data be transformed into a narrative that can be understood in all departments and at all levels within an organization, providing data specialists with a powerful tool to communicate the results of their work and the ability to impact decision-making in a concrete way.

A powerful decision-making tool

Regardless of the target audience of the story being told, data storytelling is not just about ‘disseminating’ data: It also has an emotional side. The scenario presented and the shaping of the information are intended not only to inform, but also, and perhaps more importantly, to raise awareness and elicit a reaction from the audience.

Let’s take the example of a new merchandising system being tested in a pilot store of a brand. With the support of data storytelling, it is possible to visually and holistically demonstrate the impact of this emphasis on sales, as well as the store’s customer experience over the past month. The sales manager can then more easily decide whether to extend this new product layout to all points of sale, or at least to conduct a larger scale test.

It is therefore at the decision-making level that data storytelling comes into its own, not only as a tool for presenting phenomena and drawing conclusions, but also as a tool for visualizing the future with more equanimity. Using data storytelling, managers at all levels can anticipate the impact of implementing a new action, launching a product, etc.

By processing data in this systematic fashion, ‘waste’ is eliminated: Data is no longer abandoned or left unused but constantly traced back to decision-makers, whether they are operating at the national level, or at a single point of sale. The company is therefore constantly moving forward on the strength of complex information (made comprehensible by data storytelling), with a clear vision of the future.

With data storytelling, data is no longer a complex resource reserved for IT experts. The transformation of data into narrative form allows all collaborators, regardless of their role, to understand complex figures and information.
This practice not only democratizes data: When combined with data visualization and business intelligence, it is also represents a powerful tool for more informed decision-making on a daily basis.

DigDash provides companies with high-performance business intelligence solutions, adapted to your sector and the specific needs of your company. With our data storytelling tools, we help make your data speak more clearly, and make itself understood within your organization.