What are the best data visualization tools?
Well, that’s a tricky question, because there are so many different
types of data visualization tools. These tools meet different
requirements and demand corresponding skills for users.
In this article, I reviewed what I consider to be the 6 mainstream
types and 14 top data visualization tools. The comparison covers
the use of these tools, their respective advantages, suitable
crowd, and price. Hope can help you find the best data
visualization tool for you.
The code tools are characterized by more freedom of data
parameters, increased data processing capacity, and more diverse
programming language nowadays, and they all have rich visualization
data visualizations with HTML, SVG, and CSS. D3 allows you to
handle the Document Object Model (DOM) based on your data.
D3 is the best chart gallery, which also can be applied with Python
considering the Python-nvd3 library last updated in 2016, which is
outdated compared to others. D3.js is highly flexible while hard
Plotly is an open-source, interactive, and browser-based python
graphing library. It enables creating complex interactive charts
that can be smoothly applied to dashboards or websites. It is built
on top of the d3.js visualization libraries. Therefore, Plotly is
an advanced chart gallery.
Its distinct advantage is creating multi-chart visualizations when
comparing datasets. Besides, you can set up Plotly to work in
online or offline mode or jupyter notebooks.
Suitable crowd：front-end developers who are good at Python
Cost: Free with commercial plans
ggplot2 is a data visualization package for the statistical
programming language R. The idea of ggplot2 is to separate the drawing from the
data. It is to make the drawing according to layers, which is
conducive to architectural thinking.
For professionals who need making plots involving mountains of
data, ggplot2 is the best choice. It is easy and quick to build
plots in layers to display complex stories. You can define various
underlying components and simple functions to achieve complex
Suitable crowd: professionals with R knowledge
2. Visual Reporting or BI
If you are going to use professional data visualization while
having no programming background, you should try the following data
visualization tools with drag-and-drop elements. In addition to
visualization, such tools generally focus on database connection,
data analysis, and data processing.
Beginners do not need to master too many such tools. Excel,
FineReport, and Tableau are the first choice. Excel is widely used;
FineReport is easy to create complex reports and dashboards,
Tableau is the best for data analysis via visualization.
As a part of Microsoft’s business office suite, you must be
familiar with Excel. Excel offers some standard charts, from unit
heat maps to scatter plots. Although it’s an entry-level tool, it’s
a great way to get started if you want to explore data.
Suitable crowd: Anyone
Price: Free with commercial plans
FineReport is a reporting software while being distinct at data
visualization, primarily visualizing your data via reports or
dashboards with impressive HTML5 charts including 3d and dynamic
What impressed me most is that it saved me much time to develop
reports. Before using FineReport, we made 10 excel tables for ten
stores, which were very troublesome. But with FineReport, we just
need to use the parameter query in one template, and then export
data in batches.
The other features worth mentioning is data integration and data
entry in terms of datasets for data visualization.
Suitable crowd: for report developers and BI engineers
Price: Free for personal use, Quota-based for companies
Tableau is a data analytics and visualization tool. Almost every
data analyst will mention Tableau. It has standard built-in
analysis charts and some data analysis models.
Unparalleled capacities of visualizing information are on top the
list of Tableau software benefits. Its graphs and color schemes are