It's commonly said that approximately 90% total global data came into existence in just the past two years. Almost all offline processes have been switched to automated digital processes, resulting in consistent, transactional data generation and streams of data logs. Every organization now relies on tons of data to be fully operational and to compete for economic stability. In order to extract meaningful insights from data and make it valuable, it's necessary to opt for cost-effective approaches that can be easily scaled.
In this article, you will get to know about:
- What "data visualization" means
- Important steps involved in the data visualization process
- How can it improve business outcomes
- Flexibility offered by open source data visualization tools
- Best open source data visualization tools
What is Data Visualization?
The representation of data or information in the form of some visuals (usually graphs or charts) is referred to as data visualization. It is considered to be an increasingly significant part of data analysis, as it communicates analytical details to the reader in an easy-to-understand manner.
Nowadays, decision making is dependent on the nature of data and how we use it. For that reason, machine learning advancements have introduced novel techniques to conduct predictive analyses using big data. Data scientists and data analysts can interpret the findings and predict meaningful trends or relationships from sparse data with the help of visualizations, in reduced time.
The significant steps involved in the visualization of data are mentioned below:
- Data extraction
- Data parsing
- Data filtration
- Data mining
- Data representation
- Data refinement
- Publishing data
Data visualizations are not only helpful for data scientists and analysts; they also help businesses in nearly every sector sustain their growth and produce better outcomes. Data changes over time with varied frequency, so visual representations help in identifying correlations and analyzing value as well as risk associated with business. Consistent visualization offers flexibility to share the meaningful takeaways with non-technical readers without involving any rocket science.
Best Open Source Data Visualization Tools
Open source tools provide access to competitive features and integrations free of charge without depending on software vendors. They also possess key technical capabilities such as scaling, adaptability, and consolidation.
Let's explore the best open source data visualization tools, along with their specific strengths and weaknesses.
- Provides a complete package of data visualization modules
- Creates interactive visualizations which are best-fit to be used for data science problems
- The interface of Candela is not professionally designed
- People who lack a thorough understanding of its features can encounter problems during implementation
Polymaps specializes in the visualization of maps. This tool generates interactive and dynamic maps, which are used for easily understandable analysis. It's a perfect tool for displaying information from a country level to smallest unit. State-level data, population growth, and route mapping are some prominent instances of this type of visualization.
- Users don’t have to pay a single penny to use its available functionalities
- All detailed information is concealed within a single and interactive view in a drill-down manner
- Supports multi-zoom datasets and provides SVG compatibility
- It doesn't support Android or mobile platforms. Its use is restricted to only Windows, Linux, and Mac systems
- Doesn't provide its offerings in any languages but English
D3.js is among the most effective visualization tools when we talk about functionality, features, and design creativity. D3 symbolizes data-driven documents. It is designed to exploit advanced search engines fully. Visualized data is created by implementing HTML, SVG, and CSS. D3.js employs a declarative approach and supports Document Object Models (DOM) manipulation.
- D3.js provides numerous customizable collections and restores previous versions of data manipulations with their accompanying visuals
- Allows significant amendments in to existing files in an efficient manner
- Provides the capability to add further advanced functionalities in visuals
- D3.js's efficiency is highly affected when users deal with vast sets of data. Therefore, it doesn't cater to large-scale businesses or enterprises
- Tutorials and a basic knowledge of coding are required to understand its utilization
- It only supports web-based devices
Charted is a data visualization tool built by Medium's Product Science team. All a user needs to do is provide a link to the data files and then Charted outputs the required charts in a presentable manner. This resulted visualizations can be shared with others for analysis. It refreshes the data after every thirty minutes to keep the visual chart up-to-the-minute.
- Arranges charts, descriptive titles, and background parameters efficiently and effectively
- It consistently re-fetches data periodically and keeps the stream updated
- Charted offers its users limited features in a free version
- Since it is a canvas-based framework, users can find difficulty when working with non-vector formats
- Transformation, storage, and formatting of data cannot be done when working within this framework
Data Wrapper is used by media professionals such as journalists to compile data in the required visual format. It allows layout customizations according to the user's presentation and design requirements. It exports data (charts) in the form of PDF and PNG. The most appealing feature is that it provides live visual updates.
- Provides an easy-to-use and friendly interface
- Automatically converts data into interactive visualizations without requiring any coding knowledge
- Saves all the data automatically, eradicating the possibility of any data loss
- Not responsive for iOS and Android as it's a desktop/web-based application
- Since it's an open-source tool, data security is a challenge for journalists to consider
- Customizations are not available in its free version
- It is an easy to use, flexible, and interactive tool
- Allows integration of multiple plugins
- Multi-zoom option is available
- Provides well-defined documentation
- Data preparation might need the employment of GIS software
RawGraph imports data large datasets such as CSV files or TSV files. Allowing the option to copy/paste, it serves as a bridge between files containing raw data and useful data visualization. It enables visual feedback in charts, graphs, and maps as well as custom layouts.
- Provides an easy-to-read and rearrangeable visual layout
- Guarantees data security
- Data can be scalable and data sharing options are available
- Its interface is quite simple and can integrate with vector graphics apps
- Doesn't support Android/iOS devices.
- The availability of log scales is not included
- It is narrow with respect to the number of visualization options
Plotly is an open-source tool used to create sophisticated data visualizations possessing the capability to work with programming languages like Python, R, and Matlab. Plotly is adopted by users in order to manipulate and modify graphical data online without the need for coding skills. It creates, edits, and shares visualizations in the form of charts and maps in collaboration with team members. This feature enhances efficiency and speeds up the process of data representation.
- Enables cloud deployment
- Provides online editing options for end-users
- Data can be easily shared because it is hosted on a server
- It requires more computational power as it enables highly interactive visuals
- Free version has limitations in terms of available features.
Paid versions of these open source visualization tools can offer more advanced features depending upon the nature of the plan (such as basic, standard, premium, enterprise, etc.), but the plan that is right for you will depend on your business needs.
There also exist a wide variety of licensed tools which are contributing to the business growth of organizations at scale while ensuring data security. Tableau, PowerBI, and QuickSight are considered to be the most popular data visualization tools among licensed tools due to their simplicity of use, ability to handle vast datasets, meaningful visualizations, and productive dashboards.
Human eyes are more responsive to colorful patterns as compared to textual descriptions, which is why data visualization tools have become an essential part of the big data world. Apart from the above-mentioned tools, there still exist plenty of effective data visualization tools to use and explore. By employing pragmatic data visualization tools, professionals can customize and design charts, graphs, and maps as per their requirements. Useful insights achieved from these visualizations can help decision makers grasp complex concepts or identify new trends in order to strengthen business strategies.
If you're looking to build a data visualization application for internal use or a consumer-facing app with automatically generated charts, Crowdbotics offers managed app development to deliver your build on time and within your budget. Get in touch with one of our experts today for a detailed quote and timeline.