23 Best Data Visualization Tools You Can't Miss!

What Is Data Visualization?

Data visualization is the process of graphical representation of data in the form of geographic maps, charts, sparklines, infographics, heat maps, or statistical graphs. 

Data presented through visual elements is easy to understand and analyze, effectively extracting actionable insights from the data. Relevant stakeholders can then use the findings to make more efficient real-time decisions.

Data visualization tools, incorporating support for streaming data, AI integration, embeddability, collaboration, interactive exploration, and self-service capabilities, facilitate the visual representation of data. 

Here are 15 top-notch data visualization tools that are gaining market recognition for their impressive performance and usability.

What Are Data Visualization Tools?

Data is becoming increasingly important every day. For any organisation, you can understand how important data is while making crucial decisions. For the same reason, data visualisation is grabbing people's attention. Modern data visualisation tools and advanced software are on the market. A data visualisation tool is a software that is used to visualise data. The features of each tool vary, but at their most basic, they allow you to input a dataset and graphically alter it. Most, but not all, come with pre-built templates for creating simple visualisations.

What Do the Best Data Visualization Tools Have in Common?

All of the technologies available on the market for data visualisation have something or another feature in common. The first advantage is their simplicity of usage. There are two types of software that you will most likely encounter: those that are easy to use and those that are really difficult to visualise data. Some include good documentation and tutorials and are constructed in user-friendly ways. Others, regardless of their other qualities, are missing in certain areas, excluding them from any list of "best" tools. The one thing you should ensure is that the software can handle large amounts of data and many kinds of data in a single display.

The better software can also generate a variety of charts, graphs, and maps kinds. Obviously, there will be others in the market who present the facts in a somewhat different manner. Some data visualisation tools specialise in a single style of chart or map and excel at it. Those tools are also among the "best" tools available. Finally, there are financial concerns. While a larger price tag does not inherently disqualify a tool, it must be justified in terms of greater support, features, and overall value.

1. Tableau

Tableau is a highly popular tool for visualizing data for two main reasons: it's easy to use and very powerful. You can connect it to lots of data sources and create all sorts of charts and maps. Salesforce owns Tableau, and it's widely used by many people and big companies.

Tableau has different versions like desktop, server, and web-based options, plus some customer relationship management (CRM) software.

Providing integration for advanced databases, including Teradata, SAP, My SQL, Amazon AWS, and Hadoop, Tableau efficiently creates visualizations and graphics from large, constantly-evolving datasets used for artificial intelligence, machine learning, and Big Data applications.

The Pros of Tableau:

• Excellent visualization capabilities
• Easy to use
• Top class performance
• Supports connectivity with diverse data sources
• Mobile Responsive
• Has an informative community

The Cons of Tableau:

• The pricing is a bit on the higher side
• Auto-refresh and report scheduling options are not available

2. Dundas BI

Dundas BI offers highly-customizable data visualizations with interactive scorecards, maps, gauges, and charts, optimizing the creation of ad-hoc, multi-page reports. By providing users full control over visual elements, Dundas BI simplifies the complex operation of cleansing, inspecting, transforming, and modeling big datasets. 

The Pros of Dundas BI:

• Exceptional flexibility
• A large variety of data sources and charts 
• Wide range of in-built features for extracting, displaying, and modifying data

The Cons of Dundas BI:

• No option for predictive analytics
• 3D charts not supported

3. Power BI

Power BI, Microsoft's easy-to-use data visualization tool, is available for both on-premise installation and deployment on the cloud infrastructure. Power BI is one of the most complete data visualization tools that supports a myriad of backend databases, including Teradata, Salesforce, PostgreSQL, Oracle, Google Analytics, Github, Adobe Analytics, Azure, SQL Server, and Excel. The enterprise-level tool creates stunning visualizations and delivers real-time insights for fast decision-making.

The Pros of Power BI:

• No requirement for specialized tech support
• Easily integrates with existing applications  
• Personalized, rich dashboard
• High-grade security 
• No speed or memory constraints
• Compatible with Microsoft products

The Cons of Power BI:

• Cannot work with varied, multiple datasets

4. Zoho Reports

Zoho Reports, also known as Zoho Analytics, is a comprehensive data visualization tool that integrates Business Intelligence and online reporting services, which allow quick creation and sharing of extensive reports in minutes. The high-grade visualization tool also supports the import of Big Data from major databases and applications. 

The Pros of Zoho Reports:

• Effortless report creation and modification
• Includes useful functionalities such as email scheduling and report sharing
• Plenty of room for data
• Prompt customer support.

The Cons of Zoho Reports:

• User training needs to be improved
• The dashboard becomes confusing when there are large volumes of data

5. Google Charts

Google Charts is one of the major players in the data visualization market space, coded with SVG and HTML5, is famed for its capability to produce graphical and pictorial data visualizations. Google Charts offers zoom functionality, and it provides users with unmatched cross-platform compatibility with iOS, Android, and even the earlier versions of the Internet Explorer browser.

The Pros of Google Charts:

• User-friendly platform
• Easy to integrate data
• Visually attractive data graphs
• Compatibility with Google products.

The Cons of Google Charts:

• The export feature needs fine-tuning 
• Inadequate demos on tools
• Lacks customization abilities
• Network connectivity required for visualization

6. Visual.ly

Visual.ly is one of the data visualization tools on the market, renowned for its impressive distribution network that illustrates project outcomes. Employing a dedicated creative team for data visualization services, Visual.ly streamlines the process of data import and outsource, even to third parties.

The Pros of Visual.ly:

• Top-class output quality 
• Easy to produce superb graphics
• Several link opportunities

The Cons of Visual.ly:

• Few embedding options 
• Showcases one point, not multiple points
• Limited scope

7. RAW

RAW, better-known as RawGraphs, works with delimited data such as TSV file or CSV file. It serves as a link between data visualization and spreadsheets. Featuring a range of non-conventional and conventional layouts, RawGraphs provides robust data security even though it is a web-based application.

The Pros of RAW:

• Simple interface
• Super-fast visual feedback
• Offers a high-level platform for arranging, keeping, and reading user data
• Easy-to-use mapping feature
• Superb readability for visual graphics
• Excellent scalability option

The Cons of RAW:

• Non-availability of log scales
• Not user intuitive

8. IBM Watson

Named after IBM founder Thomas J. Watson, this high-caliber data visualization tool uses analytical components and artificial intelligence to detect insights and patterns from both unstructured and structured data. Leveraging NLP (Natural Language Processing), IBM Watson's intelligent, self-service visualization tool guides users through the entire insight discovery operation.

The Pros of IBM Watson:

• NLP capabilities
• Offers accessibility from multiple devices
• Predictive analytics
• Self-service dashboards

The Cons of IBM Watson:

• Customer support needs improvement
• High-cost maintenance

9. Sisense

Regarded as one of the most agile data visualization tools, Sisense gives users access to instant data analytics anywhere, at any time. The best-in-class visualization tool can identify key data patterns and summarize statistics to help decision-makers make data-driven decisions.

The Pros of Sisense:

• Ideal for mission-critical projects involving massive datasets
• Reliable interface
• High-class customer support
• Quick upgrades
• Flexibility of seamless customization

The Cons of Sisense:

• Developing and maintaining analytic cubes can be challenging
• Does not support time formats
• Limited visualization versions

10. Plotly

An open-source data visualization tool, Plotly offers full integration with analytics-centric programming languages like Matlab, Python, and R, which enables complex visualizations. Widely used for collaborative work, disseminating, modifying, creating, and sharing interactive, graphical data, Plotly supports both on-premise installation and cloud deployment. 

The Pros of Plotly:

• Allows online editing of charts 
• High-quality image export
• Highly interactive interface
• Server hosting facilitates easy sharing 

The Cons of Plotly:

• Speed is a concern at times
• Free version has multiple limitations
• Various screen-flashings create confusion and distraction 

11. Data Wrapper

Data Wrapper is one of the very few data visualization tools on the market that is available for free. It is popular among media enterprises because of its inherent ability to quickly create charts and present graphical statistics on Big Data. Featuring a simple and intuitive interface, Data Wrapper allows users to create maps and charts that they can easily embed into reports.

The Pros of Data Wrapper:

• Does not require installation for chart creation
• Ideal for beginners
• Free to use

The Cons of Data Wrapper:

• Building complex charts like Sankey is a problem
• Security is an issue as it is an open-source tool

12. Highcharts

Deployed by seventy-two of the world's top hundred companies, the Highcharts tool is perfect for visualization of streaming big data analytics. Running on Javascript API and offering integration with jQuery, Highcharts provides support for cross-browser functionalities that facilitates easy access to interactive visualizations. 

The Pros of Highcharts:

• State-of-the-art customization options
• Visually appealing graphics
• Multiple chart layouts
• Simple and flexible

The Cons of Highcharts:

• Not ideal for small organizations

13. Fusioncharts

Fusioncharts is one of the most popular and widely-adopted data visualization tools. The Javascript-based, top-of-the-line visualization tool offers ninety different chart building packages that integrate with major frameworks and platforms, offering users significant flexibility.

The Pros of Fusioncharts:

• Customized for specific implementations
• Outstanding helpdesk support
• Active community

The Cons of Fusioncharts:

• An expensive data visualization solution
• Complex set-up
• Old-fashioned interface

14. JupyteR

A web-based application, JupyteR, is one of the top-rated data visualization tools that enable users to create and share documents containing visualizations, equations, narrative text, and live code. JupyteR is ideal for data cleansing and transformation, statistical modeling, numerical simulation, interactive computing, and machine learning. 

The Pros of JupyteR:

• Rapid prototyping
• Visually appealing results
• Facilitates easy sharing of data insights

The Cons of JupyteR:

• Tough to collaborate
• At times code reviewing becomes complicated

15. QlikView

A major player in the data visualization market, Qlikview provides solutions to over 40,000 clients in 100 countries. Qlikview's data visualization tool, besides enabling accelerated, customized visualizations, also incorporates a range of solid features, including analytics, enterprise reporting, and Business Intelligence capabilities. 

The Pros of QlikView:

• User-friendly interface 
• Appealing, colorful visualizations
• Trouble-free maintenance
• A cost-effective solution

The Cons of QlikView:

• RAM limitations
• Poor customer support
• Does not include the 'drag and drop' feature

16. Infogram

Infogram is one of the most popular software programmes on the internet today. It is a web-based tool for creating infographics and visualising data. It is primarily intended to assist all users in quickly and simply creating interesting and interactive reports, infographics, and dashboards with data-driven information and captivating images. This particular solution provides customers with over 550 maps and 35 charts, 20 ready-made design templates, numerous pictures and icons, a drag-and-drop editor, and other features. Even someone who is new to the sector may quickly learn how to utilise this programme.

It has a simple editor that allows users to modify the colours and styles of their visualisations, add corporate logos, and adjust the display choices. In addition, the users will be granted the right to use over a million icons, GIFs, and photos in their visualisations. Users may add connections to generate traffic to their website using interactive charts, which allow audiences to examine data using Infogram tabs. Reports that are interactive and shareable may also be developed and incorporated, with metrics to measure audience interaction.

17. ChartBlocks 

ChartBlocks selects the appropriate data segment to create a chart and manages the whole import process. It may import information from virtually any source. It enhances many sharing options that set the chart on the website and instantly share it. It contains hundreds of customization and design choices that influence various aspects of the chart. The Wizard feature selects and selects the appropriate data for the chart using the basic chart design wizard. ChartBlocks' data import capabilities enable data to be swiftly imported from any source. It aids in the import of proper data from the target source and the creation of the chart. And all of this happens in a matter of minutes. To create a chart, no code is necessary.

It allows for the creation of a chart in minutes, as well as the use of a chart designer and the selection of hundreds of chart kinds, which may be adjusted as needed. It can also gather data from nearly any source and use it to make visualisations. The data import wizard walks you through each step of the procedure. It easily embeds charts into any website of your choice and distributes them.

The same sharing functionality is available in the built-in social media sharing tools. It is known to interface directly with Facebook and Twitter. It also has a function that allows the charts to be exported as editable vectors and graphics.

18. D3.js

With Data-Driven Document, you can use any browser to bind data to a DOM in a document, allowing you to manipulate documents from anywhere. Transforming data involves selecting selections of nodes and manipulating them individually. You can easily change and alter node attributes, register event listeners, change nodes, alter HTML or text content, and access the document's underlying DOM by working with functions of data (styles, attributes, and other properties). You can associate operations (updates, additions, and deletions) with nodes to improve performance. You can build new functions using the function factory, as well as using the graphical primitives included. Geographic coordinates can be retrieved using a function as opposed to a constant. Properties can be reused by having data bound to the documents.

It uses HTML, SVG and CSS to create graphics from data, for example generating a table in HTML from data. Using animated transitions and high performance, you can easily visualize data in bar charts and graphics, support large amounts of data, and enjoy dynamic interaction and animation in a 3D environment with large amounts of data.

19. Chart.js

Chart.js is a popular JavaScript charting toolkit that is open source. It is a Data Visualization Software that will help you visualise data. Because it is open-source, it is maintained by the entire community. It has support for eight various types of charts, including pies, lines, and bars. The good news is that all of these are really responsive. All you have to do is put up your chart, and the library will make sure it is readable. It has 53.7k GitHub stars and a strong ecosystem providing wrappers for Vue, React, Ember, and more frameworks. The library draws the charts on the browser canvas. It is an independent project with numerous community contributors. Chart.js provides eight different types of chart bar charts, but also bubble charts,  scatter charts, line charts, and polar charts.

20. Grafana

Grafana open source is a free and open source visualisation and analytics tool. It enables you to query, display, alert on, and examine metrics, logs, and traces stored everywhere. It includes tools for transforming time-series database (TSDB) data into informative graphs and visualisations.

It also has a Graohana cloud component. It is an OpenSaaS logging and metrics platform that is highly available, quick, and fully controlled. The program provides all of the features you love about Grafana, but Grafana Labs hosts and manages the program for you.

Grafana Enterprise is Grafana's commercial edition, which offers capabilities not present in the open source version. Grafana Corporate includes enterprise data sources, sophisticated authentication choices, expanded permission restrictions, 24x7x365 support, and core team training.

21. Chartist.js 

Chartist.js is an online application that allows you to build highly customizable responsive charts that highlight important data and construct a library or libraries. Chartist.js encapsulates the given data in a library for usage in a user-friendly framework. Chartist.js is now used to create libraries in a variety of projects, including Chartist JSF (Java Server Faces Component),  node chartist (node package for server-side charts, ng-chartist.js (Angular Directive), Table press Chartist (WordPress/ tablepress extension), Ember - cli - chartist (Ember Addon), react chartist (react component), etc.

Chartist.js is user-friendly since it is compatible with a variety of browsers, making it simple to work with any of them. The browsers enable the use of several remarkable capabilities, such as general browser support, sophisticated CSS animations, SVG animations, multi-line labels, with SMIL, and responsive option override.

These are critical properties that every browser wants to have in order to provide reliable information. These capabilities allow Chartist.js to create charts that have an animation component, making them presentable and simple to read.

22. Sigma.js

Sigma is a JavaScript library for drawing graphs. It enables developers to incorporate network exploration into rich online applications and makes it simple to publish networks on websites. 

• The Sigma.js layout is fantastic.
• It enables individuals to follow up with interest as soon as possible. 
• Sigma.js's performance is currently satisfactory. 
• Sigma.js support is fantastic and quite helpful.
• Good software must be tried.

23. Polymaps

Polymaps, a collaboration between Stamen and SimpleGeo, is a free JavaScript library for image and vector tiled maps that use SVG. The library enables the creation of interactive and dynamic maps in web browsers, as well as the rapid display of datasets and support for a broad range of visual presentations for vector data (tiled). Cartography from CloudMade, OpenStreetMap, Bing, and other image-based web map suppliers is supported by Polymaps. It can load data at all scales and works well for displaying information from the country level down to the local level. It displays information using Scalable Vector Graphics (SVG), which allows users to simply create data design using CSS rules. This also saves users from having to learn new scripts because they may do the majority of jobs using scripts to which they are already accustomed.

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FAQs

1. What is data visualization?

Data visualization is the process of interpreting data in the form of eographic maps, charts, sparklines, infographics, heat maps, or statistical graphs. This helps make data easier to consume and understand.

2. What are the best data visualization tools?

Some really good data visualization tools are Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks etc.

3. What are data visualization tools?

Data visualization tools are programs that help turn data into visual representations. 

4. What are data visualization techniques?

Data visualization techniques include knowing your audience, understanding your goals, choosing the right chart for your audience and dataset, using the correct layout, including comparisons, telling a tale using the data, and using the right data visualization tool.

5. Why do we use data visualization?

Data in its raw form is very messy to understand and make sense of, this is why data needs to be sorted, organized, and visually presented in a way that it makes sense. This is where data visualization comes handy.

6. How important is data visualization?

Data visualization is important as data has become an important part of every industry. Hence, learning from data is crucial for the running of business and data visualization helps us understand the data better.

7. What are the types of data visualization?

The most common data visualization types are scatter plots, bar charts, heat maps, line graphs, pie charts, area charts, choropleth maps and histograms.

8. Is Microsoft Excel a data visualization tool?

Microsoft Excel is not a type of visualization tool, but is a powerful tool to help analyze data sets. 

9. What should I look for in a data visualization tool?

The type of tool you use is dependent on your need. You will have to understand first what would you like the data to show, and then select a tool that works in tandem to help you with the result. You should also consider ease of use, flexibility of the tool, broad data platforms, cost, etc. when you consider selecting a tool.