Importance of Data Visualisation: A Comprehensive Overview

Data Visualisation helps in simplifying vast amounts of data into various graphical representations. The Importance of Data Visualisation can be understood in every industry. It helps companies understand their sales, allows them to make informed decisions and, most importantly, will enable them to derive strategies to improve business operations. 

According to Statista, the amount of data generated is forecasted to increase to 180 zettabytes by 2025. To understand and analyse these vast amounts of data, Data Visualisation is used every day in fields like finance, healthcare, etc. In this blog, you are going to learn about the Importance of Data Visualisation and the pros and cons of using it. 

Table of Contents 

1) What is the Importance of Data Visualisation? 

2) Tools and techniques used for Data Visualisation 

     a) Tools used for Data Visualisation 

     b) Techniques used for Data Visualisation 

3) Conclusion 

What is the Importance of Data Visualisation?  

Data Visualisation is defined as the process of simplifying data into visual or graphical representations so that they are easy to comprehend by businesses or organisations. It mainly comprises graphs, pie charts, maps, etc. These visual representations can be used in various contexts, such as for business operations and conducting scientific explanations. 

You can understand the Importance of Data Visualisation through the following points:
 

a) Data Visualisation uncovers trends from raw data. It becomes easy for companies to understand trends when the data is simplified and presented in the form of graphics or visuals. For example, if you have an e-commerce business, you will want to know which of your products are selling well or which products are less in demand.  

It would be best if you made decisions according to the data that has been generated so that you do not incur any considerable loss. You can input the raw data in any of the best Data Visualisation tools to show you the upcoming trends and even get a summary of your sales. From this visual representation, you can also uncover which products may be trending in the next month or even in the next quarter. 

b) Data Visualisation helps a lot in the healthcare industry. The primary purpose of the healthcare industry is to analyse patients' data so that the administration can make decisions regarding the supply of medicines, what types of equipment they can buy to aid the patients, the cost of the treatments, etc. 

For example, suppose a hospital is present in an area where the general demography of people is between 50 years and 80 years, and their average economic condition is sub-par. In that case, the administration can take some measures to give them the best treatments so that they can afford those treatments, and they can decide on the price of medicines, insurance policies, etc. These data representations help them avoid significant economic losses by considering these criteria. 

c) It helps to understand massive amounts of data by mapping them into relevant graphical representations. For example, if your manager wants to know how your one branch of the company is performing in one area and the second branch in a different location, they will need help comprehending the rows and rows of data. When you put this massive amount of data into Data Visualisation tools, they convert them into graphs, charts or maps, which are considerably much more accessible and easier to comprehend. 

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d) Data Visualisation is an integral part of Data Analysis. With the help of Data Visualisation tools, the Data Analysts are able to comprehend the rows and rows of data. They can advise businesses on how to streamline their operations smoothly.  

With the help of Data Visualisation, these Analysts can draw relations and compare differences and similarities against past and recent trends. This comparison helps them give a forecast of future trends so that businesses are better equipped when they are delivering products or services. 

e) The primary role of Data Analysts is to convert complex data into accessible insights so that it is easy to comprehend. Here, you will understand the Importance of Data Visualisation. In addition to converting complex data into visual representations which are easy to understand, it also helps to reduce the time that would have been wasted unnecessarily by comprehending that complex data. 

For example, you want to present a representation of the areas where your company is incurring losses. If you had just delivered the data in a simple chart or Excel Sheet, it would have been difficult for you to make your team and your manager understand the pain points. However, with Data Visualisation tools, you can easily distinguish the pain points which will be highlighted in your visual representation. 

f)  Data Visualisation is an essential tool that can be used in storytelling. Wondering how? Let's take an example. You have to present a detailed business analysis to the senior management. If you don't use Data Visualisation in your presentation, there will be a high chance that people will be left confused and bored.  

However, with Visualisation, you will be able to create exciting and interactive colourful charts, graphs, maps, etc. These visual representations will highlight the pain points in the business operations, profits incurred in spite of the disadvantages and strategies on how you can improve those pain points. Using Data Visualisation will also help you to simplify many technical jargon.

g) There has been a lot of conflict on how Data Visualisation in Data Science can be used to extract essential insights from large datasets. However, it has been proven that Data Visualisation plays a crucial role in Data Science. It helps Data Scientists understand and visualise their data. 

There are several Data Visualisation techniques which these scientists can utilise to group their data and categorise them accordingly. For example, suppose you are a Data Scientist, and you want to understand the anomalies in genetics. In that case, you can utilise Data Visualisation techniques to separate and group them according to your requirements. 

h) With a large amount of data that is generated every day, businesses must analyse and understand them. Machine Learning (ML) has become one of the most popular tools to analyse these datasets. However, ML algorithms work better when the data is of high quality and clean. Data Visualisation helps these algorithms by making the data more readable, constructed and simplified.  

For example, you want to understand how Netflix increases its subscriber count, and you want to use ML. However, the massive and clustered data generated globally can pose a hindrance in the process. You can simplify and sort the necessary data that is required for you to work with. After implementing the techniques in Data Visualisation, you are now able to draw trends, patterns, and even processes that can be utilised, which might have been lost during other analyses. 

i) The importance of Data Visualisation can be understood better when it is used in Business Analytics. For instance, if you are trying to predict the probable sales growth of your company, you will require past sales data as well as present data. If you want to analyse the sales data for the last ten years, you can imagine the amount of data you have to simplify to derive information. There is going to be a massive amount of data, which is practically only possible to study by using tools or techniques of Data Visualisation. 

However, after you utilise the tools, you will be able to spot the dips and rise in your company's sales curve. These points will help you to study and analyse them so that you can understand and formulate strategies. Moreover, Data Visualisation will also help you to explore and compare the sales report from the last ten years to the present time. This is especially helpful if you are deciding to change your sales and marketing tactics to promote your products or services and improve the brand image of your organisation. 

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Tools and techniques used for Data Visualisation 

To utilise Data Visualisation, here are some tools and techniques that you can use: 

Tools for Data Visualisation 

Data visualisation tools are essential for transforming complex data sets into easily understandable visual representations. These tools enable businesses, researchers, and individuals to gain valuable insights from their data. Here are some various tools used for Data Visualisation: 

a) Microsoft Power BI: A powerful tool for creating interactive dashboards and reports, integrating seamlessly with various data sources.  

b) Tableau: Known for its user-friendly interface, Tableau allows users to create interactive and shareable dashboards with diverse data sets. 

c) Google Data Studio: Offers collaborative features and integrates with Google's suite of products, allowing real-time Data Visualisation and sharing. 

d) D3.js: Used for creating interactive Data Visualisations in web browsers, providing complete control over the visual elements. 

e) Matplotlib: It is a popular Python library for creating static, interactive, and animated plots and charts. 

f) Plotly: Provides interactive, web-based visualisations with support for multiple programming languages, including Python, R, and JavaScript. 

g) QlikView/Qlik Sense: Qlik tools offer associative data modelling and allow users to explore and visualise data dynamically.  

h) Excel: Widely used for basic Data Visualisation through charts and graphs, suitable for small-scale data analysis. 

i) R programming language: Offers various packages like ggplot2 for creating high-quality, customisable visualisations. 

j) SAS Visual Analytics: Allows businesses to explore and visualise data for better decision-making using an intuitive interface. 

Techniques used for Data Visualisation 

Data Visualisation techniques are essential for transforming raw data into meaningful insights, aiding decision-making processes across various fields. Here are crucial techniques that can be employed in Data Visualisation: 

a) Bar charts: Utilised to compare data across different categories. These bar charts represent data by using rectangular bars. The lengths of these bars are proportional to the values they represent. 

b) Line charts: Ideal for showing trends over a continuous interval or time, connecting data points with lines to illustrate patterns and changes. 

c) Pie charts: Depict parts of a whole, representing data in a circular graph where each slice represents a percentage or proportion of the total. 

d) Scatter plots: Display relationships between two variables, plotting data points on a two-dimensional graph to reveal patterns or correlations. 

e) Heat maps: Visualise data in a matrix format, using colours to represent values. They are helpful for displaying complex data sets and spotting trends. 

f) Histograms: Represent the distribution of a dataset by providing a visual summary of the underlying frequency distribution of continuous or discrete data.  

g) Bubble charts: Extend the capabilities of scatter plots by adding a third variable, representing data points with varying sizes of bubbles to convey additional information. 

h) Treemaps: Display hierarchical data using nested rectangles, enabling viewers to understand the proportions and relationships between categories. 

i) Box plots: Show the distribution of a dataset and highlight its central tendency, providing insights into variability and potential outliers. 

j) Choropleth maps: Use colour shading or patterns to represent statistical data in specific geographic areas, allowing for regional comparisons. 

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Conclusion 

With this blog, we hope that you understand the Importance of Data Visualisation. The graphical and visual representations will help you and your organisation to understand trends and patterns and even formulate strategies to relieve the pain points of your organisation. You can also utilise the various tools and techniques to use Data Visualisation easily.