What Is Data Visualization?
Data visualization is the process of displaying information or data in a visual environment that provides useful insights from the data. In other words, it entails displaying a large amount of data in a clear and understandable manner that reveals trends and patterns from the raw data.
The slicing and dicing of data down to the tiniest granularity are made easier using data visualization tools and technology. This is significant because it facilitates the detection of trends and patterns. As big data grows more widespread, we’ll need to be able to grasp progressively larger quantities of data.
Machine learning makes it easier to run analyses like predictive analysis, which may then be displayed as useful visualizations. Data visualization, on the other hand, is not only important for data scientists and analysts; it is also vital for any vocation.
Whether you work in finance, marketing, technology, design, or anything else, you must be able to visualize data. This fact exemplifies the value of data visualization.
Importance of Data Visualization
We need data visualization because looking at a visual overview of information rather than thousands of rows on a spreadsheet makes it easier to spot patterns and trends. It’s due to the way the human brain functions.
Because data analysis aims to get insights, data that is visualized is far more valuable. Even if a data analyst can extract insights from data without using visualization, communicating the meaning will be more difficult. Even if you can detect the trends without them, charts and graphs make communicating and sharing data results easier.
Data visualization is an effective technique for explaining huge, complicated, and esoteric data sets to corporate leaders and other non-technical data consumers for data scientists (i.e., front-line staff, customers, etc.). It allows data scientists to create an engaging narrative around the data, a process known as data storytelling, rather than merely displaying stats.
Communicating Ideas Of AI And IoT Through Data Visualization
The Internet of Things (IoT) and Artificial Intelligence (AI) technologies are fast evolving and impact various businesses, either directly or indirectly. The amount of data generated by business-specific IoT devices throughout the world is rapidly expanding, posing one of the IoT industry’s primary challenges: collecting, processing, analyzing, and visualizing data acquired from various sensors in the IoT environment.
As a result of the ever-increasing volume of data, the need to evaluate and derive usable insights from it has made Data Visualization an essential aspect of IoT.
Data visualization can help illustrate various types of data sets, and some of the most common insights that DV highlights include:
- frequency of occurrence
- risk and reward
- correlations in relationships
- changes over time
- network analysis
- market trends and opportunities
- distribution and demand
Data dashboards, also known as “dashboards,” are a subset of data visualization tools that visually track, analyze, and display business intelligence (BI) such as key performance indicators (KPI), metrics, and other relevant data points to monitor the progress of a business, department, or specific process.
Dashboards are designed to make it easier for non-technical users to evaluate and make decisions based on data and analytics. Although some integrated dashboards are customizable and can connect data from different systems, the vast majority of them are only suitable for displaying data within a specific focus area (i.e., marketing, finance, HR, etc.).
Users can use graphic design tools to create static visualizations like those found in news articles, whitepapers, infographics, and presentations. While some of these tools are user-friendly and intuitive, making them ideal for non-technical business users who want to generate simple data visualizations like bar charts or heat maps, others require more technical knowledge. In the case of website implementation, Google Charts, for instance, necessitates some minor code.
Data Visualization Techniques
Data can be visualized in an unlimited number of ways. While basic design components like bar charts and trend lines are commonly used in BI reporting dashboards, some DV projects require more complicated visualizations like hyperbolic trees and polar charts.
The following are a few most commonly used data visualization techniques:
- Pie Chart – A circle or “pie” is sliced into slices to demonstrate numerical proportion in a circular statistical image. Simple proportions, percentages, and part-to-whole comparisons are best illustrated with this tool. Although pie charts are one of the most simple images, they are ineffective for more complicated, multi-variable representations.
- Bar Chart – Simple, straightforward charts that use a number of bars to illustrate data, each representing a different category. They’re primarily used to compare and display the number, frequency, or another measure (like mean) for different discrete categories of data. Histograms are similar to bar charts, and however, they arrange numbers into ranges rather than bars.
- Pictorial Unit Chart – The use of pictograms (relatively sized icons or graphics) to show data patterns and trends.
- Line Chart – The line graph, which has a horizontal x-axis and a vertical y-axis, is frequently used to represent the value of something over time. In a “dot-to-dot” form, data points are plotted and connected by a line.
In the current digital age where acting on data-driven insights is critical to success, any forward-thinking firm should ensure that everyone within the organization has access to data and analytics. This should be their topmost priority.
Data visualization is a practical and straightforward approach to conveying information to a broad audience using visual data. Businesses can use the method to figure out what elements impact client behaviour, identify areas that need to be improved or given more attention, make data more remembered for stakeholders, figure out when and where specific goods should be placed, and anticipate sales volumes.
Therefore, when integrated correctly, data visualization helps organizations use their rational evidence in presenting data and enables them to develop and maintain an emotional connection with their audience.