How video can help with data visualization

Abstract: Data visualization has its origins in the desire of human beings to understand, interpret, and communicate complex realities….

Data visualization
Elisabetta Severoni
Data Telling How video can help with data visualization

Abstract: Data visualization has its origins in the desire of human beings to understand, interpret, and communicate complex realities. Therefore, it was born to provide, through visual support, all the information necessary to describe a given phenomenon or process. Today, it provides an essential contribution to optimize the management and sharing of knowledge contained within the huge and often chaotic amount of data to which companies and organizations have access. Video takes this mode of communication to a level that was inconceivable before: it integrates data into narrative structures, projects them into stories and “humanizes” them by enriching them with emotional nuances. The most advanced result of this process of progressive articulation of data visualization modes, made possible by data analysis applications, is represented by personalized videos.

Data visualization: translating data into visual knowledge

Data visualization refers to the practice of translating information (about phenomena, processes, mechanisms) into a schematic representation using design conventions.

What is the outcome of data visualization?

The result of data visualization is an image, such as a map or graph, that makes the data easier to understand. The visual context that results from this conceptual abstraction and reorganization forms the basis for extracting the knowledge needed to describe a particular situation, theme, or topic in a more immediate way.

What is the main goal of data visualization?

The main objective of data visualization is to simplify the identification of patterns, trends, and values within large data sets. In this sense, it is now an integral part of data science methodologies, constituting a particularly important phase: after the data has been collected, processed, and modeled. In fact, it must be visualized in order to draw conclusions and proceed to its publication and dissemination.

Data visualization is fundamental not only in academic and research fields, but also in business, in any economic sector.

Because companies are now accumulating huge amounts of data as a result of the modernization of their technological infrastructure and widespread digitization, they increasingly have the need to quickly and easily obtain an overview of how their processes progress. From this point of view, visualization tools now represent practically a mandatory choice.

Data visualization and machine learning: the role of advanced data analysis

Data visualization also plays a very important role for advanced data analysis: when a data scientist writes predictive analytics or machine learning algorithms, it is necessary to visualize the output in order to monitor the results and ensure that the models work as intended. In fact, complex algorithms are generally easier to interpret if they are reproduced in visual form rather than in numerical form alone.

Contrary to what we often tend to think, data visualization is not new, but has a long history: its origins date back to the dawn of visual culture and it has evolved over the centuries, until it became established as an interdisciplinary field with numerous applications. It is worth retracing the fundamental stages of this history.

A brief history of data visualization

  • We can identify the beginnings of data visualization in the geometric diagrams and tables that indicated the position of stars and other celestial bodies, and in the creation of maps to aid in the navigation and exploration of the skies and newly colonized lands.
  • The Turin Papyrus Map (1160 BC) is perhaps the world’s earliest documented data visualization. It is considered one of the oldest surviving topographical maps, an extraordinary example of thematic cartography showing the operation of an ancient mining and quarrying district in Upper Egypt. By conveying a complex set of geological information through a system of conventional signs, it testifies to the level of knowledge of mining and quarrying activities of an entire historical era.
  • Systems for locating stars by coordinates (using something like latitude and longitude) date back to at least 200 BC.
  • The cartographic projection of a spherical earth on latitude and longitude by Claudius Ptolemy, who lived at the turn of the first century in Alexandria, would serve as a standard of reference until the 14th century.
  • In 1644, Flemish astronomer Michael Florent van Langren provided the first representation of statistical data.
  • Thanks to statistical theory in the 18th century, the idea of graphic representation is consolidated, giving rise to new domains and new visual forms. Cartography invented new ways of visualizing data and the thematic mapping of physical quantities took hold in the fields of geology, economics, and medicine.
  • The use of geometric figures (squares or rectangles) provides a further visual encoding to return quantitative data in relation to each other, while the introduction of various technological innovations favors the production and dissemination of more accurate graphic works.
  • William Playfair (1759-1823) is generally regarded as the inventor of most of the graphic devices still used today: the line graph and bar graph, the pie chart and circle graph, and the combination of these elements with each other.
  • In 1854 the famous dot map by Dr. John Snow makes it possible to draw a correlation between the many deaths caused by the cholera epidemic that plagued England in those years and the use of unsafe water.
  • At the beginning of the 20th century, data visualization methods were successful: graphs and diagrams began to appear in scholastic textbooks and were used by professionals as a visual support for their activities.
  • During the 20th century, the computer processing of statistical data, which began in 1957 with the creation of FORTRAN and the spread of mainframe university computers, offered the possibility of creating graphical representations of large amounts of data.
  • From the end of the last century to the present day, increasingly sophisticated graphics software allows the information contained in the data to be visualized in a creative and accurate manner, until arriving, as far as the relationship between brands and their consumers is concerned, at the creation of increasingly personalized user profiles.

Videos, as we shall see, are an extremely interesting development, perhaps the most sophisticated, of this mode of communication which, as we have seen, comes from far away.

How does this format, which is the favorite of companies today – and there are many categories of professionals who can capitalize on the potential of video – enhance and enrich data visualization practices? What is the relationship between the stories that explain the data and the trend towards personalization? Let’s try to give some answers.

(Source: A Brief History of Data Visualization, Michael Friendly, Chun-houh Chen, Wolfgang Karl Härdle, Antony Unwin)

The video revolution: from data visualization to data storytelling

Communicating effectively with data is a necessity for organizations of all sizes, types, and orders. It’s often an activity that proves crucial right at that last mile of the analysis process where you determine whether or not insight will yield action.

Transforming data into visual communication is just one part of a richer, more articulate process because a visual context is just as important to engage the audience with a memorable and persuasive story. In fact, narratives are, without a shadow of a doubt, more powerful than raw data because they allow you to capture and hold the attention of your target audience more than infographics or other static representations.

Numbers and data visualizations are powerful means of revealing insights and communicating them in business environments, but they are often more effective and able to inspire and persuade if organized into narrative structures: we’re talking, as you can easily guess, about storytelling, or to be precise, data storytelling.

Effective data storytelling, made possible by data storytelling approaches and technologies, is a resource that anyone who regularly communicates with data, including business professionals such as analysts, marketers, sales, and managers, can no longer do without.

To significantly impact and transform customer experiences, every organization must find solutions that maximize the benefits of intercepting increasingly elusive consumers.

Focus on the audience: the importance of story-driven and customizable data

If the passage from a simple list of data to its graphical representation makes it possible to identify the focus and extract insights, the real secret of an effective and engaging story lies elsewhere, namely in the possibility of knowing the actual recipients.

In today’s interconnected and digitized market, brands and companies can count on a valuable asset: information about their target audience that comes from a variety of sources to an extent never known before. These are the online traces of users left at different times through interaction with a myriad of touch points.

Companies have now equipped themselves with the data collection and management systems necessary to update the profiles of their target audience, which in this way can no longer be traced back to vague and generic demographic segments but can be assumed to have a higher granularity and a much better descriptive capacity.

It is toward these new micro samples, which tend toward unity, that the personalized, data-driven storytelling we have been discussing is directed.

The most advanced data storytelling allows you to structure the story by leveraging acquired data that proves functional to the story itself and is personalizable for each customer.

This is one of the pillars of contemporary one-to-one marketing, which favors a market approach based on the personal and direct relationship between brand and consumer, and finds its most complete realization in video.

In videos, data visualization becomes history and emotion

Data storytelling does not coincide with data visualization, not only because it represents a development in terms of storytelling, but also because it incorporates a further dimension, that of data analysis. In order to become insight, i.e. relevant information, the data must be interpreted, and only at this point does data visualization intervene, i.e. the element of design.

The graphic representation acquires a dynamic quality and further meaning from the application of narrative structures, which allow the transmission of data not only in numbers or graphs, but in the form of stories.

All of us are physiologically programmed to recognize stories: this is the way we interpret the world around us, from the moment we are born. We are predisposed to understand reality by applying categories such as the beginning, the progression, and the end, and by actively seeking the presence of characters, challenges, and goals.

Video helps data visualization because it allows a further step: based on the actual needs, concerns, and preferences of viewers-customers and drawing on their visual imagery, it draws them into a richer and more stimulating context of sensory enjoyment. It gives data depth, depth, movement, and emotion.

Why video is the ideal format: data, moving images, narration

To data visualization, video adds a dimension that is functional for the communicative success of storytelling: the dimension of moving images, which presides over the organization in logical sequences and facilitates and maximizes the emotional projection of the viewer within the story.

Videos are therefore configured as visual stories whose functioning is activated and fed by data. These “data stories” can concretely influence decisions and drive change by relying on a powerful mix: three elements – data, narrative, and moving images – combined in a narrative that is both coherent and engaging. It is engaging because it is targeted.

The type of video that makes the most of these narrative and emotional mechanisms is personalized video, which, by making full use of the knowledge made available by data visualization, manages to:

  • transform data visualizations into engaging and high-impact stories;
  • engage each viewer on an individual level, offering them truly relevant content because it is designed based on their specific profile (most frequented touchpoints, interests, preferred navigation paths);
  • develop the best interactive features for each user in order to offer him/her all possible opportunities to participate in the conversation with the company or institution in a simple and direct way.

Babelee does just that, thanks to its platform dedicated to the creation and distribution of personalized videos. Thanks to ad-hoc developed videos that are tailored to the specific needs of the customer, which adapt to the characteristics of the individual recipient and his navigation choices, Babelee makes it possible to create a user-oriented storytelling that is interactive, exciting, and therefore really effective.

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