Nowadays, companies need to manage a large amount of data flow in an increasingly hyper-connected world. Still, in order to extract value from a business point of view, it’s necessary a system that allows them to visualize that information making them accessible and easy to understand.
Given that individuals have a limited capacity to process information, data visualization is what they need to represent data graphically.
The very crucial point now is “what is data visualization?” To answer the question, here’s a definition:
Data visualization is the graphic representation of small and significant amounts of data and information. Thanks to visual elements, such as diagrams, graphs, and maps, data visualization tools make an accessible solution in order to observe and interpret trends, outliers, and occurrences within data.
Data visualization tools and technologies are essential to analyze large amounts of information and make decision-driven choices in the big data world.
A bit of history about data visualization
Even if there is no comprehensive discussion of the graphical representation of data, nor are there any reports encompassing the entire development of visual thinking and graphing of data, we can say for sure that data visualization is quite ancient discipline.
The first documentary form of data visualization was maps, ideograms, and hieroglyphs that provided and allowed the interpretation of illustrated information.
One of the most ancient graphical representations of data is the Turin Papyrus Map, made in Egypt as far back as 1160 B.c., which illustrates the distribution of geological resources, and provides information on their extraction. A map of this kind can be considered a thematic cart, a type of data visualization that presents and communicates specific data and information through a geographic illustration designed to show data of a particular topic connected to particular geographic areas.
Data visualization techniques have become more sophisticated, particularly during the Nineteenth century. A historical diagram from Charles Joseph Minard, a French civil engineer, in 1812-1813.
Finally, data visualization became active research, teaching, and development after the digital revolution. The introduction of digital data processors brought new possibilities, including:
- analyzing a more significant amount of data than ever before
- creating interactive visualization that allows visualizing temporal data evolution
- generate real-time visualization that automatically represents newly updated data without the user monitoring; deepen a subset of data or visualize an alternative set of filters, movements, etc.
In general, digital technologies increase the possibility of the graphical representation of data.
Why is data visualization important?
Another crucial question is why data visualization matters and the benefits for companies.
First of all, data viz provides a quick and easy way to communicate information by using visual information. This method can be helpful for companies to identify what factors influence consumer behaviors; identify areas that need improvement; make data more memorable and engaging; and forecast sales volume. In a nutshell, companies must make better decisions.
The various data visualization tools on the market, such as BStreams, ensure that the business decision-maker becomes autonomous in creating interactive dashboards and uses fewer IT resources to approach more awareness of data culture.
Finally, users that approach descriptive and predictive analytics experience additional benefits as data preparation and data exploration times are reduced. Together with a reduction in dependence on IT, this aspect ensures greater efficiency of decision-making processes: business decision-makers will be able to rely on timely – shorter time-to-market – and customizable analyses, representative of current and future dynamics.
What makes a good data visualization?
According to the American statistician and Yale professor Edward Tufte in his book The Visual Display of Quantitative Information, he affirms that excellent data visualizations consist of ‘complex ideas communicated with clarity, precision, and efficiency.’
To quote another author who expresses himself on the same topic, the four key elements that need to be present for a successful data visualization are the following:
- information: that data to work with
- story: a compelling concept
- goal: a relevant e specific function for the visual
- visual form: effective use of metaphor
The four elements combined are essential. The result would be sketchy by picking only two of them; the result lacks something with just three.
In fact, according to the author of Information is Beautiful, combining just information, function, and visual form without adding a compelling story, the graphical representation of data will be bland and lacking interest for the final audience.
The same happens if a data visualization combines just information, visual form, and story lacking a goal and functionality. In this case, it wouldn’t be relevant.
What are the types of data visualization?
When talking about data visualization, the first thought is column, bar, or pie charts. They are certainly a way to represent data visually, but they are not the only method. The most common types of data visualization to convey information effectively include:
- area chart
- line chart
- ring chart
- data tables
- stacked area chart
- grouped column chart
- stacked bar chart
If you’re in doubt about which type of graph to pick, we have written an article to explain how to choose the right kind of data visualization.
Common data visualization use cases
There are many fields in which data visualization can be adopted within companies of every size. Some of these fields include:
Politics: data visualization is commonly used to create maps with the aim to visualize the final results of the election depending on the state.
Marketing: marketers deal with considerable amounts of data every day, and they can’t help but use data visualization to convey information more effectively and make it easily accessible. Marketers can use data viz for creating reports, dashboards, infographics, sentiment analysis; or again identify trends, outliers, and patterns.
Data science: as is well known, data scientists need to analyze large amounts of data and extract knowledge from them. For this reason, data visualization is mainly used for data cleaning and checking, exploration, and discovery, and finally for quickly communicating results to stakeholders.
Finance: correctly visualizing data, it’s crucial for those who work in finance. Finance professionals have to track the performance of their investment decision when deciding whether to buy or sell an asset.
Healthcare: one of the most common data visualizations used in the healthcare field is choropleth maps to visualize relevant health data. They allow healthcare professionals to see how a variable changes across specific countries.
Data visualization’s key role: data literacy
Even before learning how to visualize data correctly, it’s fundamental to know how to read, understand, create, and communicate data as information. This is what data literacy is about. In a nutshell, it consists of giving meaning to data, interpreting them correctly, and telling a phenomenon through data, selecting the most relevant information appropriately.
This competence should not be exclusive to data analyst professionals but should involve the various company figures at multiple levels. Data literacy, also called data alphabetization, is essential because it offers a company the advantage of responding promptly in competitive contexts in which consumers are increasingly demanding, thanks to data-driven decision-making processes.
A data-driven company is more competitive in the global economy thanks to the skills of its employees. Data literacy must become a transversal skill, accessible to various company figures at multiple levels and no longer just reserved for specialists.
To sum up, what was written above, data viz is the practice of translating data and information into visual context in an easy, accessible, and effective way. The graphical representation of data allows stakeholders, business owners, and companies of every size to interpret data quickly and make better and faster decisions.
At BStreams, we strongly believe in the power of interpreting and visualizing data; in fact, our purpose is to help professionals communicate better and validate their concepts through objective data since we retain that a thesis is sustainable only if supported by facts.