What is Data Visualization?

The graphical depiction of information and data is known as data visualization. Data visualization tools, which include visual components like as charts, graphs, and maps, give an easy method to observe and comprehend trends, outliers, and patterns in data.

Data visualization tools and technologies are critical in the Big Data environment for analyzing enormous volumes of data and making data-driven choices.

Advantages of Good Data Visualization

Colors and patterns catch our attention. We can rapidly distinguish between red and blue, and square and round. Our culture is visual, including everything from art and advertising to television and movies. Data visualization is another sort of visual art that draws our interest and keeps our eyes on the message. When we look at a chart, we can quickly identify trends and outliers. If we can see something, we immediately assimilate it. It’s a narrative with a purpose. If you’ve ever looked at a big spreadsheet of data and couldn’t find a trend, you understand how much more useful a visualization can be.

Types of data visualizations

The first type of data visualization may be dated back to the pre-17th century Egyptians, who primarily employed it to aid with navigation. People began to use data visualizations for larger purposes as time passed, such as in economic, social, and health sciences. Perhaps most significantly, Edward Tufte wrote The Visual Display of Quantitative Information, which demonstrated how people may use data visualization to communicate data more effectively. His book has withstood the test of time, especially as companies increasingly rely on dashboards to report real-time performance metrics. Dashboards are powerful data visualization tools for tracking and visualizing data from numerous sources, allowing visibility into the consequences of an individual team or neighboring team activities on performance. Some of the common visualization techniques are described here:

chart types

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Tables:

This is made up of rows and columns that are used to compare variables. Tables may display a large amount of information in a systematic manner, but they can also overwhelm visitors who are seeking for high-level patterns.

Pie charts and bar charts:

These graphs are organized into sections that depict different aspects of a larger picture. They give a straightforward method for organizing data and comparing the size of each component to one another.

Line charts and Area charts:

By charting a set of data points across time, these graphics depict changes in one or more quantities. Line graphs use lines to show these changes, whereas area charts link data points with line segments, stacking variables on top of one another, and utilizing color to differentiate between variables.

Histograms:

This graph represents the amount of data that falls inside a specific range by plotting a distribution of numbers using a bar chart (with no gaps between the bars). This graphic aids the end-user in identifying outliers within a given dataset.

Scatter Plot:

These graphics are useful for revealing the relationship between two variables and are frequently used in regression data analysis. However, these are frequently mistaken with bubble charts, which employ the x-axis, the y-axis, and the size of the bubble to depict three variables.

Heatmap:

These graphical displays aid in the visualization of behavioral data by location. This might be a map location or even a website.

Tree maps:

It displays hierarchical data as a collection of stacked forms, most often rectangles. Treemaps are excellent for comparing proportions between categories based on their region size.

Usages of Different Chart Types:

Presenting Distribution:

Distribution charts show how objects are dispersed to various parts. Line charts, histogram charts, and scatter charts, which illustrate item correlation, are the best charts to use for this type of data.

Visualizing Composition:

Three sorts of charts are useful for visualizing an issue’s makeup. Pie charts are obviously made to display compositions because different pieces of a pie might represent one composition and the entire pie is the completeness of an item. Different color sections can also be used to show compositions in area charts and stacked bar charts.

Representing Relationships:

Finding links between data is critical for all of the data. Spider charts and bubble charts are ideal for studying relationships since they show the link of one data variable to the entire group or other variables.

Trend Representation:

When you need a chart to show the trend of a set of data over a specific time period, there are two basic charts to choose from: Column Chart and Line Chart. Both charts depict a shifting trend with data disparities. You can also combine them into a single chart known as a Pareto chart, which is useful for identifying relevant trends.

Comparison:

The majority of charts are designed with data comparison in mind. Because data always appears in big quantities, the comparing function aids in the visualization of enormous amounts of data. Divided into two halves, various objects may be compared using bar charts, column charts, six sigma charts, and spider charts, while one fixed item can be compared using line charts and column charts at different times.

data visualization methods

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Open Source Data Visualization Tools

It has never been easier to gain access to data visualization tools. Open-source frameworks, such as D3.js, enable analysts to display data in an interactive manner, allowing them to reach a wider audience with fresh information. The following are some of the most prominent open-source visualization libraries:

D3.js:

It is a JavaScript package that allows you to create dynamic, interactive data visualizations in web browsers. D3.js creates visual representations of data that can be seen in any browser by combining HTML, CSS, and SVG. It also has interactive and animation elements.

ECharts:

A sophisticated charting and visualization library that makes it simple to add intuitive, interactive, and highly configurable charts to products, research papers, and presentations, among other things. Echarts is written in JavaScript and utilize ZRender, a lightweight canvas framework.

Vega:

Vega describes itself as a “visualization grammar,” offering support for customizing visualizations across huge datasets accessible over the web.

deck.gl:

It is a component of Uber’s open-source visualization platform. deck.gl is a framework for exploratory data analysis on large datasets. It aids in the development of high-performance GPU-powered web visualization.

Data Visualization Best Practices

Because there are so many data visualization technologies accessible, there has also been an increase in poor information visualization. To guarantee that your data visualization helps your target audience arrive at your intended insight or conclusion, visual communication should be straightforward and purposeful. The key practices listed below can assist guarantee that your data visualization is helpful and clear:

Set the context:

It is critical to offer basic background information to help the audience understand why this particular data point is significant. For example, if e-mail open rates were failing, we may show how a firm’s open rate compares to the industry as a whole, suggesting that the company has a problem with this marketing channel. To motivate action, the audience has to understand how current performance relates to something real, such as a goal, benchmark, or other key performance indicators (KPIs) (KPIs).

Know your audience:

Consider who your visualization is intended for, and then ensure that your data visualization meets their requirements. What is that individual attempting to achieve? What kinds of questions are they interested in? Do you think your visualization addresses their concerns? You’ll want the information you present to drive individuals to behave within the parameters of their roles. If you’re not sure if the visualization is clear, show it to one or two individuals in your target audience for feedback, which will allow you to make extra changes before a huge presentation.

Choose an effective visual:

Specific images are created for different sorts of datasets. For example, scatter plots clearly show the relationship between two variables, whereas line graphs clearly show time-series data. Ensure that the image aids the audience in comprehending your key point. Misalignment of charts and data can have the opposite effect, further confounding your viewers rather than giving clarity.

Keep it simple:

Data visualization tools may make it simple to include a wide range of information in your graphics. However, just because you can, doesn’t mean you should! In data visualization, you should be very deliberate about the additional information you provide to draw the user’s attention. Do you, for example, require data labels on each bar in your bar chart? Perhaps you only need one or two to make your point. Do you require a selection of colors to convey your message? Are you utilizing colors that are accessible to a wide range of audiences (e.g., taking colorblind audiences into account)? Design your data visualization to be as impactful as possible by removing any material that may distract your target audience.


Additional Reading: Google Data Studio Chart Types

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