Design Tech

Data Visualization – A complete Overview

Data Visualization Overview
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Why Data Visualization Is a Central Element of Effective Analytics

Pitching your idea with data visuals is the method any business seeking to sell their stories and product to multiple crowds should utilize. It is an integral part of any business transaction and would help the business owner convey their message to their audience. This method of portraying data is what makes it possible to cause any positive change in business.

What is Data Visualization?

Data visualization is the system of passing critical messages to an audience found within the business development unit of an organization. It is the act of sharing information through visual contexts such as maps and graphs. 

This system makes it easy for the human brain to break down small and big data for human consumption. Visualization makes it easy to detect patterns and understand trends and detect anomalies in your group of data.  

Good data visualization would give meaning to that complicated sets of data and make it clear and more understandable. Though good data visuals are more than charts and graphs; it should create a narrative that portrays the organization’s story and bring the right conclusions to the audience mind. 

Data visualization is essential to companies and businesses because they realize useful insights from the data interpretation and act on them, which is trying to persuade decision-makers. With data visualization, companies can find new ideas, solve difficult tasks, and develop new process efficiencies all through insights. 

To correctly visualize data, one needs to read data and analyze it and also know how to present such data compellingly. 

Data Visualization in Business

For businesses, to get started with data visualization, one need to understand who are their audience. There are different classes of people within a business organization, and each prefers different methods of visualizing data.

Now, as the first rule for data visualization, don’t visualize unnecessary information to your audience. I can tell you for free that no audience wants to spend their time visualizing what doesn’t matter, including the presenter.

In a real-life scenario, a presenter should easily convey their ideas in five to ten short slides, which would be easily understood by their audience. Still, when practicing and preparing for the presentation, there’s nothing stopping anyone from using dense slides. It’s also allowed to share more detailed graphics of your presentation beforehand to get your audience to speed and give them a more in-depth understanding of the information shared.   

At this point, you can now use the meeting to hammer on your key points and give your audience proper information about your ideas.  

Types of Data Visualization

 Data visualization has evolved to the point where everyone is an analyst and is expected to create presentations with visuals that stresses the angle of their ideas. Reports have shown that several companies now add visualization training to their budgets. 

As more companies now require data visualization as part of their requirements for recruitment, potential employees would need to know what visual data option is best to pass their message. To understand what visible option is best, you need to know different chart options. 

There are different ways to visualize your ideas and trends, depending on what solution you want to provide.


Here is the list of 10 most common Data Visualization formats;

  • Bar charts: used for comparing single measure across categories
  • Histograms and Box plots: for illustration and comparing data clusters
  • Bullet charts: for displaying performance data
  • Waterfall charts: used to analyze data, and for understanding and explaining the gradual transition in the quantitative value of a unit which is subjected to increment or decrement.
  • Time series charts: for visualizing the change of data points over time
  • Scatterplot charts: used to show the relationship between two variables
  • Bubble Chart: a bubble chart can show distribution or relationships
  • Mekko Charts: with this, you can compare values, measure the composition of each value, and analyze data distribution all at the same time.
  • Reference lines, Bands, and Distributions: used to measure progress against goals
  • Visualizing data with maps: for answering location-specific questions or aiding geographical exploration

Open Source Data Visualization Tools

Data reading isn’t easy for the average person to understand at first look. Though there are still people who would look through a spreadsheet and find the data they need. For those who can’t find the information they need from a mass of figures, that’s where data visualization comes to play. 

For the presenter, rendering the data in a way that is informative and presenting it so it can stand out from the mass of data on the visuals are the challenges you’ll face; that’s why technology companies have designed built-in tools that will help users present and visualize their data better.

Here are the best tools for Data Visualization:

  • Candela
  • Charted
  • Datawrapper
  • Chart.js
  • D3.js
  • Dygraphs
  • RawGraphs
  • Palladio
  • Timeline
  • Leaflet
  • Candela
Data Visualization tool
Source: sampig.github

This data visualization package can be accessed from the Resonant platform. It stands out from other tools because it has a full suite of data visualization components. Also, it has a training document that serves as a quick read for novices.

  • Charted
Source: Braveterry

If you are looking for a simple data visualization tool, Charted is the right choice. All it needs to work is a link to a Google Sheets location or CSV file, then click on GO. It created a visual display using a line chart or bar chart.

  • Datawrapper
Datawrapper Tool for visualizaing data
Source: Thenextweb

Datawrapper was designed in 2011 and is used majorly by journalists; however, it is comprehensive enough and can be used by any researcher or data scientist. It has both paid and free versions.

  • Chart JS
Chart.js a data visualization tool

Chart JS is an open-source data visualization tool that allows data science experts to visualize data using JavaScript. It is an open-source clean charting library.

  • D3.js
Source: Dribbble

D3.js operates on a JavaScript library and is used to manipulate documents based on data. The library enables data visualizations using SVG, HTML, and CSS.

  • Dygraphs
Dygraphs: Data Visualization Tool
Source: Pinterest

Leaftlet only deals with maps. Although it has no charting capabilities, it is the leading open-source JavaScript library for mobile-friendly maps. It has different kinds of mapping layers and interaction features like mouse-over functionality and zoom controls.

  • RawGraphs
Source: Density design

RawGraphs is a bit similar to Datawrapper and Charted. Its tagline is the missing link between data visualizations and spreadsheets. RawGraphs requires its users to paste/cut data, provide a link, or upload to create varieties of charts.

  • Palladio
Palladio Tool: Visualizing data
Source: Visualizing data

Palladio is a free tool for the visualization of complex historical information. It includes features such as map view, graph view, list view, and view of the gallery. Data can be displayed in CSV, TAB, or TSV formats using the tool. You can imagine the relationship between your data dimensions with the chart view. The information is shown as lines-connected nodes.

  • Timeline
Timeline.js: Data Visualization tool
Source: Designyourway

A timeline is a free tool for creating reporting timelines. You can attach your Google Drive account using the templates provided in the tool to create a timeline from the Google Spreadsheet. You can create customized installations using JSON.

  • Leaflet
Leaflet: Data Visualization Tool
Source: Rampages

Leaftlet only deals with maps. Although it has no charting capabilities, it is the leading open-source JavaScript library for mobile-friendly maps. It has different kinds of mapping layers and interaction features like mouse-over functionality and zoom controls.

The Three Elements of Visualizing Data Successfully

Data visuals would not be termed successful, except it checks many factors which will be discussed shortly. Though these factors are numerous, here are three factors that designers often overlook.

  • It understands the audience 

A lot of data designers do not consider the views of the audience before coming up with visuals. Before you begin any design, you should think about the goal of the design, which is spreading a large amount of information in a pattern that is easily understood. So, when creating a plan that you intend to be successful, you have to understand who the design targets, how they will read and interpret the information, what are their expectations, what information do they really need, and what is the visualization’s functional role, and how can viewers take action from it.

  • It sets up a clear framework

The designer, when creating a design, has to ensure that every member of the audience viewing their design share a common understanding of what it represents. To achieve this, they set up a clear framework involving the semantics and syntax from which the data will be pulled. The semantics is the meaning of the words and graphics used, while the syntax is the structure of the communication.

  • It tells a story

The designer should use their design to tell a story to their audience. It has been confirmed that only a few forms of communication are persuasive as a compelling story and data visualization falls under that row. Data visualization portrays itself as a correct medium for storytelling and should be used for such. Whoever reads a visual set up in for of a story will immediately understand the knowledge from the data.   

How to Prepare Data for Analytics

The biggest problem with creating a design is that of missing information or biased information. It is the reason why visualizations should be built on a solid ground of correct information. The designer may have used data gathered by other analysts, so you must compare data and make sure they are genuine. Ask questions about the data origin, how it was collected, and other crucial variables before using the data to build the visualization. 

If you cannot verify the Genuity of the data, you do not have to wait for perfect data before making your graphs. Make use of what you’ve found; be vocal about the amount of confidence you have in the data to your audience. 

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