Data storytelling: what is it, examples, how to do storytelling through data

How to organize and convey data through storytelling with data storytelling.

An innovative service from People’s University LUCE, in collaboration with SHARING CUBE

Data storytelling has been identified as a major trend in the field of data. Companies are implementing it because it is an easier way to inform a particular audience about the content of complex data and analytics through storytelling. There are ad hoc tools such as Tableau for doing data storytelling in business, which is a testament to how it is an increasingly central tool in the marketplace.

So with this service we help our partners understand what data storytelling is with a specific definition, look at some examples and success stories, and try to understand how to help organize and convey data with storytelling.

What is data storytelling?

Data storytelling is the exposure and sharing of the content of complex data and analytics through storytelling in order to inform and influence a particular audience. It is a very effective way to share business information, target new actions and drive results.

We have already seen how storytelling is the discipline that uses the principles of rhetoric and narratology to tell stories, and it is employed by companies and brands through storytelling marketing through the construction and distribution of stories for the purpose of creating an emotional and trusting bond with the brand. Indeed, stories have the ability to leverage emotions, which are involved in the perceptual and evaluative processes that drive purchasing decisions. Even more so in the current context, where marketing requires an increasingly experiential approach.

Therefore, data storytelling has the purpose of organizing information in order to simplify its reading in a process that we could call the “democratization” of data: that is, an act of transparency on the part of companies towards employees, consumers or partners who do not have data analysis skills.

If initially, with the emergence of big data, data analysis and understanding was thought of as something elitist, data storytelling has emerged as a trend reverser: audiences can understand, interact with content and make decisions faster and with more confidence, so the interactive factor of data is very important. But how to respond to this purpose? Data storytelling makes use of data visualization, which we will explore further below.

Three basic elements are needed to structure data into an engaging narrative: narrative, visual and data:

  • Narrative (storytelling), a story helps the audience understand insights by transforming complex information into narrative elements.
  • Visual (images), serve to illustrate, make the story more imaginative and thus real and concrete. Narrative and visuals must always be connected, that is, visuals are a support to the narrative.
  • Data, which, as we have mentioned, are the basis of narrative and support the narrative itself.

By combining narrative, visuals, and data a data storytelling narrative is able to create an emotional response in the audience, and as mentioned, emotion plays a significant role in decision making. So if these three elements are combined perfectly, you get a story that can influence people and drive change in the direction of business goals. This is why data storytelling is becoming increasingly central to companies that want to compete in a highly complex market.

Why is data storytelling important?

Data storytelling is a vital tool in various contexts, not only in storytelling marketing for business. Indeed, stories and storytelling make information easily accessible and memorable: through the emotional, persuasive, and entertaining nature of a story, data storytelling makes data easily understandable and digestible even to a non-expert audience.

 Data, organized in graphs and tables, may indeed be immediate and logical, but it is only through stories and storytelling that it is possible to contextualize them and explain their importance, to explain why certain situations occurred. Through storytelling gimmicks an organization can therefore share data in a meaningful way, building around it a story that will attract the attention of the audience and be able to make that data linger in their minds. It becomes crucial, for example, in sustainable marketing strategies.

Undoubtedly, data storytelling requires greater efforts of resources and time on the part of a content writer, as it is not easy to reconcile insight and data in an impactful narrative, however, this tool also brings with it numerous advantages at the decision-making level in a company’s internal processes: it allows metrics and results to be presented in a more immediate way, facilitating discussion and guiding it on the path to action. In fact, data storytelling is thus not only a way to share information with one’s audience, but to communicate information effectively within the insight organization and make decisions in real time, overcoming superfluous details and allowing understanding even to those who have not had contact with the data before.

The fundamental elements of data storytelling

What are the key elements, or steps, of data storytelling?

We have mentionsed a few of them in the previous paragraph, but let’s dive deeper into te topic:

Big Data

Big Data is the vast amount of computer data collected through technology. It is handled by data science and data scientists (one of the new digital professions most in demand by companies) who define algorithms and use software to relate this huge amount of data. When used for business purposes, we talk about business intelligence. They are defined as Big based on 3 characteristics:

  • Volume, the amount of data, structured or unstructured, generated every second;
  • Speed at which new data are generated and arrive at the system that performs analysis on them;
  • Variety, that is, various types of data that are generated, accumulated and used.

In relation to the last characteristic (variety), the data used can be of various types:

  • Structured, are the data used before the advent of Big Data, i.e., collected for the same purposes for which they are processed, according to predefined fields and with ad hoc formatting;
  • Unstructured, data stored in their native format and not processed until they are used. The advantage lies precisely in the accumulation rates (higher than structured) and the freedom of the native format. Examples are e-mails, social media posts, chats, images, etc.
  • Semi-structured, meaning they have metadata that identifies certain characteristics so they have enough information to catalog, search and analyze them, a middle ground between the first two.

Data science is concerned with discovering the links between different phenomena, very often correlations, and predicting phenomena on the basis of statistical processing, also precisely in business. Big data is thus the basis of data storytelling: in order to build a narrative that has a solid foundation and whose purpose is to highlight the performance of a business activity or process, put a problem into relevance, or produce insights useful for future decisions, it is necessary to start from a concrete and statistically meaningful, hence generalizable, basis.

Searching for patterns among the data

This is the second important element of data storytelling: it is not enough to collect data, but obviously the next step is to interpret them to draw conclusions or rather insights. This requires looking for meaningful relationships among the data and exploring them for patterns that are meaningful.

Big data allows for different types of analysis:

  • Descriptive analysis, where the tools used are used to describe the current and past situation of business processes and/or functional areas.
  • Predictive analysis, where the tools, based on statistical predictions develop hypotheses and forecasts
  • Prescriptive Analytics, in addition to data analysis and statistical prediction, they are capable of proposing solutions;
  • Automated Analytics, the tools independently implement the proposed action as a solution.

Data visualization

The third step in data storytelling is data visualization: it is not enough to collect, sort, and find patterns with meaning if we do not have an understandable way to visualize the results and thus make decisions.

It is for this reason that data storytelling has its own independent existence, which we distinguish from data analysis.

While the latter simply consists of data collection and analysis, Data Storytelling does not stop there.

Data visualization fulfills the function of telling the story of the data, in this case visual, to an audience that is not necessarily an expert with specific data science skills. This is why storytelling and visualization go hand in hand in data storytelling.

Communicating data effectively

After the process of collection, organization, and visualization, it becomes critical to communicate the data.

At this point, data visualization can be coupled with textual or auditory content.

The channel, medium, and context will depend on the purpose and audience. We could imagine doing data storytelling in front of an audience of partners and prospects, in online or live meetings, to highlight results (common activity reports are examples of this) or targeting an end consumer directly (B2C communication), to communicate a company’s actions or achievements as part of, for example, a partnership activity or social responsibility campaign. At this point it is important then to communicate with the user:

  • Establish interactive communication, then arrange for the return of feedback (whether live via question-answer or online, e.g., by filling out questionnaires);
  • Personalize content, again tailoring data visualization and all communication in relation to the target audience. Useful then to know his skills, tastes, interests, for effective communication. Personalization is one of the principles of one-to-one marketing (approach based on the personal and direct relationship between brand and consumer);
  • Invite to action, stakeholders should feel involved, thus part of the narrative that leads to an outcome, a call to action can be effective in this regard. One could involve them in brainstorming, answering short questions, hypothesizing scenarios.

In communication, we need to use typical storytelling techniques (we have explored them in this article): so follow a narrative arc and the various stages leading to the climax and then resolution. Useful to ask: what tension exists for my audience? In this sense, the resolution to this tension, the ending of the story, is what we need to arrive at together. The action we ask the audience to take is the way to resolve the tension or the “magic tool” that helps toward the resolution.

Data-driven actions and decisions

Data storytelling has goals: all the work of collecting and organizing, analyzing, visualizing, and communicating data is about explaining what is happening in order to make data-driven, data-driven decisions. So a consultant who does Data Storytelling also provides operational guidance on future actions to be taken to gain an advantage or achieve a purpose.

This is also possible depending on the type and purpose of the analysis performed (descriptive, predictive, prescriptive, and automated).

The benefits of data storytelling

Data storytelling is certainly not a straightforward activity and requires a variety of skills and professions working together for the purpose, but then why juggle this activity? How can data storytelling impact a company’s performance? Here are a few good reasons to do data storytelling to benefit a brand (and beyond):

  • Improves and memorization of data
  • Turns boring data into interesting content
  • Increases engagement with respect to the topic being analyzed
  • Leverages a universally understandable language (that of narrative and visual)
  • Creates an experience
  • Leverages the effectiveness of stories
  • Enhances the relationship with the recipient (customer or partner) based on emotions aroused
  • Is a measurable methodology
  • Facilitates collaboration
  • Speeds up decision making
  • Adds value to data
  • “Humanizes” the data
  • Builds credibility

Examples of successful data storytelling

We provide here some examples of data storytelling, starting with Spotify wrapped, US Gun Deaths by Periscopic, to Brexit by number: three very different cases for the subject of the narrative. Respectively a topic as light as music tastes, as serious and tragic as US gun deaths, and as economic and geopolitical event as Brexit. In all three cases, data storytelling was an effective tool to achieve the informational purpose for which it was created and to give resonance to the specific case.

Spotify Wrapped

Speaking of data storytelling, Spotify is undeniably the standard. Let’s talk in particular about its annual “Spotify Wrapped” campaign-the Spotify marketing campaign that goes punctually viral every year. It was first released in December 2016 and allows Spotify users to view a set of data about their activity on the platform including: the five musicians listened to most often, the songs they listened to the most, and their favorite genres of music. Content producers have access to additional data regarding, for example, the number of times their content was streamed that year.

Spotify Wrapped has led millions of people to become advocates for the campaign by sharing their insights on the use of the platform on social. This also triggers a rise in Spotify’s app store ranking.

US Gun Deaths by Periscopic

Periscopic has developed a visualization that shows how many deaths there were and equivalent “stolen years” due to firearms in the US in 2013. It is a very effective visualization in hinting at the severity and frequency of the phenomenon.

Brexit by Numbers

When England was asked to decide on the outcome of Brexit, there were so many arguments for both options, respectively staying in or leaving the European Union. This led to a great deal of confusion about what the impact of Brexit would actually be for both The United Kingdom but also for the European Union itself.

That is why Sky News produced “Brexit By Numbers” a narrative with real data on the immediate impact of Brexit on the UK. Today at the same link one can analyze what the actual outcome was compared to all the predictions made. In numbers, of course.

“Many claims have been made about the impact of a leave vote – but which one has come true so far? Let’s analyze how the UK has changed since it voted for Brexit.”

Data storytelling: books

If you are looking for some useful books or resources (in addition to our storytelling book suggestions) to delve specifically into the topic of data storytelling, here are some pointers:

  • Data Storytelling. Generating value from information representation, by Cole Nussbaumer Knaflic, Apogeo, 2016
  • Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer KnaflicJohn, Wiley & Sons Inc, 2015
  • Browse online Data Storytelling: Learn how to turn your analytics into a narrative that anyone can understand Theoretical-Practical Manual of Effective Communication by Fabio Piccigallo, Dario Flaccovio Editore, 2019
  • Storytelling. The story factory by Christian Salmon, Fazi, 2008

Data storytelling course

If you are looking for courses on data storytelling you will be interested to know that among the 2023 goals the People’s University LUCE has planned the 2023 Masterclass with valuable topics such as those dedicated to the Digital twin, artificial intelligence, data storytelling, and the development of the PNRR, with prestigious and competent guests .

Regarding data storytelling, the time has come for the emergence of a new professional figure, the data storyteller. We need elle Actionable Data Stories, or stories that get people to move, to do something.

(There is still time to get on board! Contact us at or 375-6282095)

“Decisions made by humans are rarely made by data alone. Human decisions are influenced by cognitive biases, emotions, and conceptual leaps beyond what the data can suggest. The best way to educate and persuade decision makers is through stories. Stories built from good data analysis.”

The course aims to teach:

  • How to be strategic in presenting data
  • The basics of storytelling applied to data analysis
  • Creating engaging data presentations with understandable charts and graphs
  • Persuading with data
  • Educating customers with data

Now you have all the basic elements to set out on a journey of discovery of data storytelling, will you include it in your communication campaigns? Contact us and let us know!