Many companies have made the same observation: data is an indigestible resource, difficult for non-specialists to decode and understand. This is where data storytelling and data visualization come in, with the aim of transforming raw data into an attractive, comprehensible story.
What is data storytelling?
Telling a story to better convince your audience… Widely adopted in marketing, advertising, and sales, the concept of storytelling is not new. But in the age of digital technology and Big Data, a new way of constructing a story has emerged.
Data storytelling is a set of techniques for telling a story with data while tailoring it to the target audience. For example, a marketing director is not addressed in the same way as a CFO.
Formatting information is nothing new in itself; graphs and dashboards have been created to represent data more clearly for many years. However, the tools that have served this purpose up until now are looking increasingly archaic, even outdated (Excel in particular).
Data storytelling, on the other hand, is intended to be very user-friendly and simple to handle while showing data in a more aesthetic and striking way. The stakes are high, given that human beings have limited concentration time (especially during a meeting or training session); ultimately, the message has to be clear and conveyed in the blink of an eye.
But this requires an appropriate format… Excel spreadsheets and PowerPoint slides are a thing of the past: With 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: two complementary tools
Data storytelling is inextricably linked to data visualization and can even be seen as the logical evolution of the latter.
Definition
Data visualization: the practice of transforming raw data into visual representations, such as graphs, curves, maps, diagrams, or infographics.
Indeed, these two concepts are based on the same fundamental idea: information is more perceptible and memorable for the human brain when presented in an organized way. The use of clear, aesthetically pleasing visual objects therefore increases the impact of the information you are trying to communicate.
In this respect, business intelligence solutions using Dataviz must offer their users an elegant and user-friendly interface, allowing easy data exploration, with perfectly legible dashboards and intuitive graphical representations.
However, data storytelling adds an extra level of narration. Where data visualization is “content” with aesthetically shaping raw figures, data storytelling is about telling stories to convey messages.
The 3 components of data storytelling
Data storytelling is based on three interrelated elements:
- Data
- Narration
- Visuals
The data
Business Intelligence is all about revealing the important information hidden in a complex database, in order to measure corporate performance and support the decision-making chain.
This means selecting the most relevant data to present a comprehensible and convincing analysis, depending on the target audience. After all, a sales manager and a human resources director will not be interested in the same key performance indicators (KPIs).
The task is daunting, as organizations often possess scattered sources of data, which need to be aggregated and processed to be truly exploitable. Hence the importance of using a high-performance business intelligence tool, enabling data to be explored and relevant metrics to be derived.
However, if the choice of data is key, it must be combined with a good narrative to simplify its presentation.
Narration
Data analysis is a powerful business tool, but it is not a directly actionable decision-making aid. Indeed, the main challenge is to present analyses that are easy to understand, without drowning the audience in a mass of complex data.
In this context, narration simplifies the process of reasoning by adding a logical structure to the data. The latter breaks down into three main phases:
- The initial situation: this consists in presenting the data and setting up the stakes of the story.
- The plot twists: these are used to highlight problems and elements that disrupt the initial situation.
- Resolution: this concludes the story’s twists and turns and provides a lesson to be learned from the analysis.
In this way, the narrative energizes the graphic representations to hold the audience’s attention throughout the presentation. This makes the conclusions of the analysis more understandable and memorable.
The visual
Dataviz allows you to use a wide range of visual representations to make your company’s datasets speak for themselves.
However, to highlight key information, it’s not enough just to create graphics. They need to be carefully chosen to grab the audience’s attention and guide them through the decision-making process.
In other words, it’s not just a matter of making the data easy to visualize and the presentation pleasant to follow. With clear, relevant graphics, the audience is able to understand the concrete implications of the analysis for the company’s business. Key decision-making criteria are highlighted to help managers act quickly and effectively.
How do data, narrative, and visuals work together?
- The combination of data and narration enables information to be explained and commented on, providing it with more context and making it more readable.
- The combination of visuals and data enhances the latter, formalizing it and making it more accessible. This is the very principle of data visualization.
- Finally, when narration and visuals come together, we’re right in the middle of data storytelling: we’re looking to captivate the audience by offering them attractive, even entertaining content, to convey a message more effectively.
The question is, what can data storytelling and data visualization actually do for your business? Much more than a tool for creating tables and graphs “to look pretty“, it’s a real lever for mobilizing all the organization’s departments and supporting managers at all levels in their decision-making.
Data storytelling and data visualization: advantages for the company?
Combining data storytelling and data visualization enables more effective communication within the company, more informed decision-making, and better anticipation of future trends.
A tool for better understanding data
The need for data storytelling originates from a problem encountered by many organizations: IT specialists, data scientists and other experts in the field have difficulty in promoting their findings and sharing them with other company departments (management, marketing, sales, etc.). The cause of this failure is the difficulty, for most of those involved, in understanding figures and analytics.
Furthermore, if data processing is a major undertaking, it is also necessary to be able to present the results to a non-specialist in a synthetic way, which is by no means easy.
Indeed, the different actors in the company don’t want to hear the same story, and don’t need the same arguments to be convinced. Whereas a management controller will be interested in the optimal allocation of his budget, a manager will want to monitor the indicators of his company’s good health.
Fortunately, this can be remedied by using data storytelling and data visualization. To do this, we need to sort the data to retain only coherent information, according to the target audience, and then build a story from this data.
Once the story has been established, the next step is to format the data using textual elements and visual objects, always with the aim of creating an aesthetically pleasing and synthetic medium.
Only by following this process can raw figures and indigestible data be transformed into a narrative that can be understood across all departments and hierarchical levels of the organization. This gives data specialists a powerful visualization tool with which to communicate the results of their work, and enables them to have a tangible impact on the decision-making process.
A decision-making tool
Whatever the audience for the story it tells, data storytelling doesn’t just simplify data: it also involves an emotional aspect. The scenario presented and the way the information is formatted are designed not only to educate, but also and above all to raise awareness and provoke a reaction on the part of the audience.
To illustrate this, let’s take an example of the use of dataviz in retail.
A new merchandising concept was tested in a pilot store.
With the support of data storytelling and data visualization, we can visually and synthetically demonstrate the impact of the new merchandising on sales and the store’s customer experience over the past month.
The merchandising manager can then more easily decide to apply this new product layout to the whole store, or at least to carry out a larger-scale test.
It’s at the decision-making stage that data storytelling really comes into its own. Not only is it a tool for visualizing information and drawing conclusions, it is also a way of projecting confidently into the future, thanks to predictive analysis. In this way, managers at all levels can anticipate the impact of implementing a new action, launching a new product, and so on.
By systematically processing data in this way, “waste” is eliminated: data is no longer neglected or unused, but is constantly passed on to decision-makers, whether on a national scale or at the level of a single sales outlet, for example. The company can therefore move forward on the basis of complex information (made comprehensible by data storytelling and data visualization), with a clear vision of its future.
A growth engine for the company
Making data available to as many people as possible is also a way for the organization to grow. Indeed, everyone involved can visualize data, measure the impact of their decisions and actions, and quickly adjust their strategies. As a result, the company is much more flexible, capable of constantly adapting to changes in its environment.
According to BARC’s “Interactive Analytical Storytelling” study, 85% of companies say they use data to optimize resource allocation, enabling them to cut costs and act more effectively throughout their value chain. In this way, data storytelling and data visualization enable optimal management of human, material and financial resources, helping the organization achieve its strategic objectives.
To maximize the benefits of data storytelling and data visualization, it is important to select the right performance indicators (KPIs) according to the needs of each business. Then, these metrics need to be represented using relevant graphs, which can be consulted in an interactive dashboard. In this way, the company can monitor its performance over the long term, while keeping an eye on its market and competitors, to support its development.
With data storytelling and data visualization, data is no longer an inaccessible resource reserved for IT specialists with specific training. Data storytelling enables all employees, whatever their function, to understand complex figures and information. But this practice doesn’t just democratize data: combined with a SaaS Business Intelligence solution like DigDash, it becomes a powerful tool for making informed decisions and supporting a company’s growth.
