Data Visualization: Why It Is One of The Top Data Skills For 2024

What is Data Visualization?

Data visualization is the process of communicating and translating data and information in a visual context, usually employing a graph, chart, bar, or other visual aid. Visualization also uses images to communicate the relationships between various sets of data.

Data visualization is also called information visualization, information graphics, and statistical graphics. It is a step in the process of data science, which tells us that after all data has been collected, processed, and modeled, the information must be visualized so that users can use it to draw conclusions.

Also, data visualization is part of the broader discipline of data presentation architecture (DPA), whose purpose is to identify, find, manipulate, format, and deliver data in the best way possible.

The Importance of Data Visualization

Visually representing insights derived from data provides a means for people to see and understand data patterns, trends, and outliers. Consider the rejoinder “Do I have to draw a picture for you?” aimed at someone who’s not grasping the speaker’s point. Well, data visualization draws us that picture, presenting facts and figures in a clear, visually appealing manner.

More importantly, it is a valuable tool in the ongoing process of mastering the vast volumes of information created by big data. It’s challenging enough to sift through the floods of big data to find relevant, useful information, let alone looking for patterns and trends. That’s why data visualization is critical for today’s data analysts and other users—it helps the data collectors communicate results easier and enables readers to see the trends and patterns easily.

Our brains are wired to respond to visual stimuli and look for patterns in everything we see. Data visualization takes advantage of this human instinct and offers an easier way for people to see the information clearer and draw more accurate conclusions faster.

The benefits of data visualization include:

• Gives the reader the means to quickly absorb information, improve insights and make faster decisions
• Provides an easy means of distributing information that offers users more opportunities to share their insights with everyone involved in the project
• Imparts an increased understanding of what steps an organization must take to improve itself
• Offers the ability to attract and maintain the audience's interest by giving them the information they can understand
• Gives the decision-makers the means to quickly act on findings, deliver successful outcomes faster, and have fewer errors
• Eliminates the need to excessively rely on data scientists because it is more accessible and easily understood

The Five Categories of Big Data Visualization

We’ve been talking a lot about how big data needs data visualization, so let’s keep that train of thought going by looking at the five main data visualization categories found in big data scenarios.

• Geospatial. This category is particularly appropriate because the United States has recently held a Presidential election, and pollsters, advisors, and pundits often use geospatial data for polling and election results. Geospatial data visualization overlays maps with data points, referencing real-life physical locations. Market penetration studies also often rely on geospatial data visualization. Examples include:

o   Cartograms

o   Density Maps

o   Flow Maps

o   Heat Maps

• Hierarchical. The hierarchical method groups data within larger groups and is often used when displaying information clusters, especially if the data comes from a single point of origin. Examples include:

o   Ring Charts

o   Sunburst Diagrams

o   Tree Diagrams

• Multi-Dimensional. This method relies on two or more variables combined to create a three-dimensional data visualization. These multi-layered, concurrent datasets are especially pleasing to the eye. Multi-dimensional visualization is best suited for breaking down large amounts of data into salient points. Examples include:

o   Histograms

o   Pie Charts

o   Stacked Bar Graphs

o   Venn Diagrams

• Network. The network method shows relationships between datasets without resorting to lengthy explanations. Network visualizations connect datasets, illustrating how they relate to each other within the network. Examples include:

o   Alluvial Diagrams

o   Matrix Charts

o   Node-Link Diagrams

o   Word Clouds

• Temporal. Temporal data visualizations must be linear and one-dimensional. This method uses lines that stand alone or overlap, existing with a start and finish time framework. Examples include:

o   Line Graphs

o   Scatter Plots

o   Time Series Sequence

o   Timelines

A Look at Some Data Visualization Techniques

Although we have already discussed data visualization categories, some specific techniques mentioned previously deserve further explanation. These techniques are often used in many types of data visualization projects.

• Infographics. Infographics are a collection of charts, images, and minimal words that create an easy to read, memorable, visually pleasing data visualization display. Infographics are typically very entertaining to see, although they still do an excellent job of relaying useful, relevant information.
• Scatter Plots. Scatter plots rely on an x- and y-axis to show the relationship between two variables.
• Population Pyramids. This data visualization technique employs a stacked bar graph to illustrate a population’s social narrative and is especially useful for showing population distribution.
• Fever Charts. Despite the ominous-sounding name, fever charts are quite benign! Although it references charting a patient’s fever, the term has expanded to include any chart that tracks data that changes continuously over time. For instance, brokers use fever charts to track stock prices.

How is Data Visualization Used?

Nothing speaks more effectively about data visualization’s versatility than real-world examples, and there are plenty to be found. Such as:

• Determining correlations. The best way to determine the relationship and correlations between two variables is to compare them visually.
• Network examination. In this context, “network” refers to the whole market audience. By examining the network, analysts can spot audience clusters, including any influencing factors and the bridges between them, and statistical outliers.
• Tracking changes over time. This use is a simple yet essential data visualization function. Visualization helps people see and analyze how data trends change over a given period.
• Frequency determination. Frequency is related to tracking changes but differs because it examines how often a given event happens.
• Timeline scheduling. Using a resource like a Gantt chart, project leaders can illustrate each assignment within the project and how long the tasks will take.

Also, many fields benefit from data visualization, including:

• Data Science/Research. Data visualization helps data scientists perform complex data analysis, recognizing patterns, and understanding datasets. Data scientists have many data visualization tools to choose from to help them complete their tasks, including using programming languages such as Python. You can learn more about how to accomplish this through this data visualization in Python tutorial.
• Finance. Data visualization is a handy tool for the investment world, showing how commodities, bonds, and stocks perform over time.
• Healthcare. Choropleth maps show different geographical areas in different colors to illustrate numeric values. They are a good resource for physicians and epidemiologists who track health hazards such as pandemics or heart disease.
• Logistics. Data visualization aids shipping companies in determining the best shipping routes.
• Politics. Pollsters and campaign workers can get a clear picture of who voted for a specific candidate in each region.
• The Sciences. SciVis, or Scientific Visualization, makes it easy for researchers to better understand their experimental data.

Would You Like a Career in Data Analytics?

Today’s society increasingly depends on data to help make life run smoother and develop sounder strategies for tackling the commercial sector’s numerous challenges. Consequently, data analysts are among the most sought-after professionals in today’s business world. They are skilled IT data smiths who know how to take the mountains of information generated by big data and turn it into actionable, useful information.

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