What Is Data Visualization And Why Is It Important?

Visualization is the representation of data through the use of common charts, and graphics, similar to plots, infographics, and indeed robustness. These visual displays of information communicate complex data connections and Data data-driven perceptivity in a way that's easy to understand. Data visualization can be employed for a variety of purposes, and it’s important to note that it isn't only reserved for use by data teams. Management also leverages it to convey organizational structure and scale while data reviewers and data scientists use it to discover and explain patterns and trends. Data visualization is generally used to goad idea generation across teams.

Types of data visualizations:

Tables: This consists of columns and rows used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-place trends.

Pie charts and piled bar charts: These graphs are divided into sections that represent parts of a whole. They give a simple way to organize data and compare the size of each element to one other.

Line graphs and area maps: These illustrations show changes in one or further amounts by conniving a series of data points over time. Line graphs use lines to demonstrate these changes while area maps connect data points with line parts, mounding variables on top of one another and using color to distinguish between variables.

Histograms: This graph plots a distribution of figures using a bar chart (with no spaces between the bars), representing the volume of data that falls within a particular range. This visual makes it easy for end-users to identify outliers within a given dataset.

Smatter plots: These visuals are salutary in revealing the relationship between two variables, and they're generally used within retrogression data analysis. Still, these can occasionally be confused with bubble charts, which are used to fantasize three variables via the x-axis, the y- axis, and the size of the bubble.

Heat maps: These graphical displays are helpful in visualizing behavioral data by position. This can be a position on a chart, or indeed a webpage.

Treemaps: Which display hierarchical data as a set of nested shapes, generally blocks. Treemaps are great for comparing the proportions between orders via their area size.

Open-source data visualization development tools: Access to data visualization tools has noway been easier. Open-source libraries, similar to D3.js, give away for reviewers to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most famous open-source visualization libraries include

Candela : When it comes to open source as well as JavaScript, candela is surely one of the best packages for data visualization. The package comes with a regularized API for use in real-world data wisdom operations and is made available through the Resonant platform.

Charted: Charted is an open-source tool that can automatically visualize the data. All you have to do is give a link to a data file and the tool will return a shareable visualization of that data. Created back in 2013, by the product science group at Medium, it Charted workshops with lines that are formerly intimately accessible to anyone with the link.

Chart JS : Chart JavaScript is a community-maintained open-source clean charting library. It helps data science professionals vision data using JavaScript. Still, before going forward with the process, you’ll have to include the library in the frontend code. Chart JavaScript gives a good sense to the charts.

D3.js : D3.js is a JavaScript library that's used in the manipulation of documents grounded on data. The library helps in developing data visualizations through the use of HTML, SVG, and CSS. The main focus of the platform is to give its users the full capabilities of ultramodern cyber surfers without tying a personal frame, combining important visualization factors and a data-driven approach to Document Object Manipulation (DOM).

Leaflet : The leaflet is an open-source JS library for mobile-friendly interactive charts. One of the best features of this tool is that it's extremely lightweight and the size is only 38 KB of JS. The tool is designed in such a way that it has nearly all the mapping features most inventors ever need.

Different applications of data visualization:

1. Healthcare Industries

A dashboard that visualizes a patient's history might prop a current or new doctor in comprehending a patient's health. It might give faster care installations grounded on illness in the event of an emergency. Rather than sifting through hundreds of pages of information, data visualization may help in changing trends.

2. Business intelligence

When compared to original options, cloud connection can give the cost-effective “ heavy lifting” of processor-intensive analytics, allowing users to see bigger volumes of data from numerous sources to help speed up decision-making.

3. Military

It's a matter of life and death for the armed forces; having clarity of actionable data is critical, and taking the appropriate action requires having clarity of data to pull out applicable insights. The enemy is present in the field today, as well as posing a danger through digital warfare and cybersecurity. It's critical to collect data from a variety of sources, both organized and unshaped. The volume of data is enormous, and data visualization technologies are essential for the rapid delivery of accurate information in the most condensed form feasible.

4. Finance Diligence

For exploring/explaining data of the linked customer, understanding consumer behavior, having a clear inflow of information, the effectiveness of decision making, and so on, data visualization tools are getting a demand for financial sectors.

5. Data science

Data scientists generally produce visualizations for their particular use or to communicate information to a small group of people. Visualization libraries for the specified programming languages and tools are used to produce the visual representations.

4 Ways Data Visualization Improves Decision-Making :

1. Faster Response Times

Big data is an extremely precious resource for businesses. Marketing managers, sales managers, directors, and service reps need vital data at their fingertips to be capable of doing their day jobs. It's incredibly useful for sales managers to be capable to list crucial statistics from past sales campaigns to a prospective customer in real-time on a sales call rather than having to say they will go down and search for the information and get back to them.

2. Simplicity

Advances in technology mean the types of information companies are collecting from their customer and audience are multiplying. From traditional sources such as customer mailing addresses and phone numbers to more advanced demographics such as customer behavior and buying patterns from social media, mobile apps, websites, and CRM relations, this titanic of information is being generated every single day in the digital world.

3. Easier Pattern Visualization

Data visualization is a very easy way to see new paths and identify new patterns and trends. Spreadsheets are the bane of numerous marketing managers' lives when trying to find patterns in data while reviewing hundreds of lines in spreadsheets.

4. Platoon Involvement

Data visualizations intimately display important data in real-time, so every department has access to the information it needs to more unite. These infographics display a combination of company criteria, which is great for keeping everyone in the company on the same page.