Data visualization is merely a graphical representation of information and data. Visuals like tables, charts, and maps are the data visualization tools for understanding trends, sticky values, and data trends.
In the stream of big data, data visualization tools and technologies are essential for analyzing massive amounts of information and making data-driven decisions.
Our eyes are drawn to colours and patterns. We can quickly identify the red from the blue, the square of the circle. Our culture is visual, ranging from art and advertising to television and movies. If you've ever looked at a spreadsheet full of data and couldn't see a trend in it, you know how much more effective visualization can be.
Data visualization is thus another form of visual art that captures our interest by keeping our eyes captivated by the message. When we see a chart, we quickly translate it through the trends and values that emerge from it. So let's go back to the basics.
If it is difficult to give a precise definition, it is partly because data visualization encompasses several fields of study. It is the result of combining several disciplines such as statistics, computer science, and visual communication. In addition to this, the term data visualization refers to several different visualization techniques, some of which are very distant from each other.
However, there is one common aspect with all of these techniques: the graphical representation of a set of data. It sounds obvious, but it's important to remember it because data visualization is usually associated with charts and tables of data.
The term data visualization first appeared in 2001 when Edward Tufte published The Visual Display of Quantitative Information. This book popularized the idea that charts and tables of data could not be viewed as data visualizations but rather as a means of representing data.
Since then, the term data visualization has become a keyword to define information visualization. However, it is still used differently in academia and the professional world. In academia, it is often used synonymously with information visualization or business visualization. In the professional world, it mainly refers to interactive charts or dashboard charts.
To arrive at a precise definition of data visualization, we must consider all the stages of creating a graph. This involves thinking about all of the steps after obtaining and analyzing the data. In particular, we should be interested in the creative process, which is often neglected in books dealing with data visualization.
Data visualization is indeed also a creative discipline that includes several aspects such as the collection and analysis of data, the creation of charts and colorimetry, the structuring of the visualization, the design of the narrative, and the visual design in general.
Indeed, it is essential to specify that data is the basis of all visualizations. In addition to this, it is crucial to clarify that data is not always directly used in a visualization. This is the case, for example, if the visualization structuring step allows additional data to be generated.
If we understand what data visualization is, we need to consider all the steps between getting the data and the result. It is important to note, for example, that data can be presented at several levels: at the raw data level, at the structured data level, and the advanced data level.
The data-visualization process usually begins with the collection of raw data. This step can be done by several methods, such as manual collection, automatic collection, or data extraction.
One of the difficulties of data visualization is to define the different types of visualization. The question is essential because this definition is the basis for understanding the work of Data Visualists.
To answer it, it is interesting to point out that data visualization is a creative discipline that encompasses several techniques and several types of visualization. To see more clearly, it is helpful to classify them into a few main categories: tables and charts, maps, infographics, and dashboards.
This category groups together a set of techniques aimed at representing data in a table or graph. These are defined as visual representations of data. They have the particularity of allowing data visualization in many formats (proportions, ratios, frequencies, etc.). Examples of visualizations: bar, column, dashed charts, etc., data charts, crosstabs, cartograms, etc.
This category groups together a set of techniques aimed at representing data by writing it on a map. Maps are visual representations of spatially organized data that allow data to be compared and from the same point of view. Examples of visualizations: map of the world, map of the metro in Paris, map of cafes in Madrid, etc.
This category groups together a set of techniques aimed at representing data by inscribing them on a plane. Infographics are visual representations of spatially organized data that allow data to be compared and from the same point of view. Examples of visualizations: time scale, time diagram, etc. *
The dashboard is a visual representation of the data. NASA developed it to help it achieve its goals. Today, the dashboard is an indispensable tool for businesses. The dashboard allows the different departments and the different teams of a company to have a fair and precise vision of the market, competitors, and objectives to be achieved. It also makes it possible to measure the effectiveness of the actions taken to achieve these objectives. Find out how you can build your dashboard based on your goals.
By reasoning "decision aid."
By showing the invisible, data visualization facilitates and accelerates decision-making. It is a valuable tool that is more efficient than simple Excel tables. Thanks to data visualization, we have access to the essentials! Data visualization simplifies the dissemination of information. It provides points of comparison and analysis on trends. It then refines the predictions on future trends.
You certainly know that colors affect our emotions! These influence our decision-making. To convince, do not neglect the choice of your colors! One of the basic rules in data visualization is using a single color to represent the same type of data.
Data visualization makes it easier to understand the world. It is an excellent tool for analysis and projection into the future. Today it is becoming accessible to as many people as possible. It is imperative to have an adequate data visualization solution.
All must easily understand a good data visualization! It must also be intuitive so that the user can take ownership of the information. Finally, data visualization must be good simplicity so that everyone understands it. That said, data visualization remains a language!
Data visualization is not an end in itself! It must bring value to the user! Data visualization must fit the needs of the user. It must be used wisely. The important thing is not to get locked into a predefined pattern. Thus, needs change and data visualization must evolve!
The best way to have fun in data visualization is to have a dashboard! A dashboard is an efficient tool! It allows you to visualize at a glance the leading indicators of an ecosystem. Thus, it facilitates decision-making and the analysis of results.
In addition, data visualization must be interactive! It should allow the user to make choices or to analyze the data more finely. This will enable him to appropriate the information, understand it, and interpret it. Interactivity allows the reader to choose the data they wish to analyze.
Data visualization must remain simple and easy to use! Remember, you are dealing with users who are not data visualization experts. However, even if the user is not a data expert, he does have some good ideas. Setting up a dashboard will allow him to understand the data and quickly analyze the results!
Finally, good data visualization must be sustainable! Data visualization must be scalable to adapt to new data and to changing needs internally or externally.
However, dressing up a graphic is not enough to make it more attractive. Effective data visualization is a delicate balance between form and function. The most straightforward graph might be too dull to notice or bring out a strong point. The most fantastic visualization might completely fail to convey the right message, or it could say a lot. Data and visuals have to gel together, and there is an art in combining good analysis with good storytelling.
Skills change to adapt to a data-driven world. It is increasingly valuable for professionals to use this data to make decisions and use images to tell stories as data informs who, what, when, where, and how.