Dataviz: Which data visualization tools and techniques are best for me?

Published on 04 August 2021

Reading time 9 minutes

What's data visualization? What tools does it offer, and in what situations can they help us communicate? We tell you everything below!

Data visualization tools

Understanding and appreciating data visualization and business intelligence within companies' decision-making processes all began in the eighties, and we've witnessed the field's growth ever since. Only in the 2000's did computers prove themselves powerful enough to process far larger volumes of data.

Talk about volumes! These days, numbers are churned out for nearly everything, from keeping track of management and billing systems to inventory. But as the volume of data to be processed increases so rapidly, compiling and analyzing so much information at a glance has become no longer possible.

This is why we need to extract all this information to monitor the activities of companies (by calculating the turnover of a company for example, or the number of employees...). Data is very present in the fields of digital marketing, human resources, and digital activities in general.

With data visualization, we try to understand this set of indicators and synthesize it to be able to see more clearly and establish strategic reports, in particular to simplify decision-making within companies.

Want to know how to use data in strategic decision making within your company? Discover now our dedicated article by Dayo Aderibigbe, Product Operations Manager at Google.

Data visualization and Business Intelligence: definitions, similarities, and differences

What is data visualization?

The main objective of data visualization is to provide simplified access to large volumes of information. Thanks to the information collected, analyzed, and visualized, trends, comparisons, and recommendations will emerge.

Simply put, data visualization is the visual way of knowing what the information that is available to us is made of.

As we all know, the human brain is not designed to analyze thousands of lines of data. Without data visualization—or dataviz for the initiated—analyzing all these lines of numbers and words would take us far too long. Thus, we'll try to make all this information and its indicators accessible to as many people as possible by making them easier to understand and read.

Here's an example of a bar graph created with the data visualization and storytelling app Flourish):

Made with Flourish

This visualization represents the evolution of the world population, with a projection until 2100. Source: United Nations.

What's business intelligence?

Nowadays, intelligence around numbers is becoming essential—especially in the business world. When we talk about business intelligence, we're referring to the production of representations (reports and/or dashboards) to facilitate decision making and be able to track trends.

The global business intelligence market was already worth more than $17 billion in 2016 according to Gartner, and it's expected to generate $26.5 billion by 2021!

Thus, even if dataviz and BI (business intelligence) have a common main issue—allowing everyone to exploit their information through different types of graphs, pie charts, curves—there are still notable differences between big-data visualization tools and libraries.

Data visualization libraries: particularities

Data visualization libraries were created to develop and obtain results in the form of visualization to be able to customize your representations from A to Z.

We often tend to identify them as tools that are technically more advanced than BI tools. (More on that later.) These libraries require users to have skills not only in data, but also in development: you need to know how to analyze and interpret information as well as how to code!

Yes, you heard right: "data visualization library" rhymes with programming language.

There are three reference languages: Python, JavaScript and R!

However, not all dataviz libraries have the same objective. To help you see more clearly, we can divide them into two types: interactive dataviz libraries and exploratory dataviz libraries.

Interactive dataviz libraries

Interactive dataviz libraries allow you to create dynamic visualizations to achieve high-quality, presentable final results. For example, we'll try to have a clear presentation in a company—for customers as well as end users.

What are the most used interactive dataviz libraries?

1. Plotly is a data visualization library (in Python and JavaScript) that allows creation of sophisticated visualizations through code. This library is made accessible thanks to its instructive guide.

The development of Dash—a framework in Python—has made it possible to simplify the integration of Plotly visualizations into a web application. Dash was co-created by Chris Palmer, who also happens to be one of the co-founders of Plotly.

2. Bokeh  is a Python-based data visualization library that in addition uses JavaScript and HTML to format its graphs. This flexible and interactive visualization includes dynamic plots that change depending on how the user interacts with them, allowing exploration of information from a variety of angles.

3., based on D3.js, is one of the most used libraries in React, allowing easy creation of highly customizable dataviz applications.

To help you visualize what can be done by coupling code and data visualization, we invite you to check out the Starbucks Interactive Dashboard, created by Antony Henrion, our instructor in Data analysis at Wild Code School Paris, using Plotly!

As mentioned above, this interactive dashboard* was developed using the Plotly dataviz library coupled with the Dash framework.

In short, if you have technical insight AND want to have fully customizable results and control over every detail, opt for data visualization libraries!

Dashboard: a control panel that gathers several data sets from various sources and establishes a clear and understandable synthesis for the audience. It also allows you to follow in real time the evolution of the different indicators it presents. For example, a dashboard can include a combination of graphs, figures, and other information of all kinds.

Exploratory dataviz libraries

There are also dataviz libraries to perform static visualizations used in the exploratory phase of a data set.

Thus, we only try to visualize descriptive statistics and some indicators through box plots or bar charts that allow us to know more about the data by helping us to make choices.

BI tools: particularities

BI tools are to data what no-code is to web development. They are already developed applications that allow you to drag and drop data sets to visualize the data directly.

These tools are said to be more accessible to people who have little or no experience with data. They're often intended for companies that are looking for quick and easy to use results.

Which BI tools are most commonly used?

1. Tableau is the reference BI tool on the market! It's widely used in companies—thanks above all to its ability to analyze and visualize large amounts of data.

How does this work with Tableau? Using one or more data sources, you build the visualization on an already developed platform. You simply incorporate your data source and drag and drop the data as you wish depending on the visualization you want to obtain, whether it's map, chart, or any other format. To learn more, visit the Tableau gallery.

Here's an example of a project accomplished through Tableau:

Dashboard 1

2. Qlikview  was the main competitor of Tableau before it lost steam. It has a large number of capabilities (data visualization, business intelligence, analysis, and reporting) that make its configuration very customizable. However, the learning curve is not negligible: mastering Qlikview requires a bit of patience!

3. Power BI, now Tableau's main competitor, is a suite of analytical apps developed by Microsoft that allows you to combine data from multiple resources to build detailed reports. The sharing of this information is completely secure. For more information, visit the Power BI gallery.

4. Google Data Studio  is a tool from the Google Analytics suite that transforms your data from your Google accounts (Ads, Analytics, Drive, Sheets...) and other sources (Facebook, YouTube, Twitter, Search console, MySQL, and more) into reports that are both customizable and aesthetically pleasing—and thus easy to read and share.

To summarize, you can achieve similar results in your organization by working with dataviz libraries and BI apps, but both solutions rely on different techniques.

Which data visualization tool should you use depending on your situation?

Beginners who only want to experiment

Turn to Google Data Studio instead. Even if this app is still recent and does not allow you to make representations as advanced as Tableau, it's a totally free, customizable, and collaborative app that updates data in real time. Perfect for taking your first steps in data visualization!

Entrepreneurs with limited resources but with basic data knowledge

The Power BI Desktop application is free of charge, and allows you to share reports with your team, stakeholders or customers. If you have minimal resources, you can even opt for Power BI Pro (included in Microsoft 365 E5) for $9.99/month.

There is also a free version of Tableau: Tableau Public, which allows you to create data storytelling without having to write a single line of code.

Entrepreneurs with resources or companies with resources

Tableau Desktop provides advanced analysis through advanced calculations, interactive and attractive visualization, and the creation of qualitative, lively, and animated working documents. In order to use the full version, a $70/month license is required.

As for Qlik, although this app has free versions, it offers a catalog of options and variable prices depending on the option you choose such as visualization or integrations. More information here!

What job opportunities does the Dataviz market offer?

Candidates that have mastered BI tools can become a BI consultant. This field includes:

  • analysis of functional and technical needs of projects
  • data modeling
  • creation of reports

Candidates with expertise in a particular tool (e.g. Tableau), can become a BI expert. This includes:

  • project definition and management
  • needs analysis
  • modeling data architecture
  • guiding clients through projects from start to finish

Lastly, candidates who've mastered both BI tools and dataviz libraries can become a data analyst or business analyst. Missions can vary depending on the company and the job description.

Dataviz projects of our students: concretely, what can you do after five months of training at Wild Code School?

Project conducted by Michael Kohler on European passenger flight patterns throughout Europe, March 2019-March 2021:

Flux aérien des passagés européens en Europesur les 2 dernières années (2019-2021)

Project carried out by Antoine Carré on flight patterns throughout Europe, January 2018-April 2021:

Vol aérien en Europe de 2018 à 2021

To go further...

Interested in data but don't know where to start? Start with our data analyst prep course, dive into our articles dedicated to Big Data or read the Data analyst syllabus to find all the info about training program, the application process, the upcoming sessions and so on...

If you liked this introduction, we offer an appointment to one of our data trainings: