What is Data Visualization? Example, Tools & Software, Best Practices

What is Data Visualization?

Data visualization is a way of making information easier to understand by using bar charts, graphs, animations, maps, videos, and other types of images.

It is often used to make business information more digestible in industries such as healthcare, retail, finance, insurance, telecom sales, human resources, and recruiting. This visual style can also be interactive, allowing users to explore more of the data without help.

To use a simple analogy, telling the story of data through visual art is like telling stories to children through cartoons. It is easier for people to see and understand the information and make informed decisions when it is presented this way.

How Data Visualization Transformed Over the Years

Data visualization has been around for centuries, but it really came into its own during the mid-20th century, at the dawn of the Information Age. Below is a brief summary of the evolution of using visual representations to convey data.

Early Humans: Our evolutionary ancestors wrote information down in the form of pictures. With cave walls as their canvas, they marked the positions of nighttime constellations or the animals encountered on their regular hunts.

17th Century: Explorers needed accurate information for their excursions, and they gathered this data in the form of maps. Land masses, rivers, and other navigational aids represented visually, were key to successful voyages.

18th Century: More information began to be displayed in easy-to-understand formats, with the invention of line, area, bar, and pie charts (all by the Scottish engineer — William Playfair).

19th Century: This era has often been referred to as the Golden Age of data visualization. Charts and graphs were made with painstaking detail and color, tracking everything from immigration to war to national debt. Infographics began to be published. Among the most notable was Charles Darwin’s “Tree of Life,” which showed how species were connected.

20th Century: With the advent of computers and the world entering the Digital Age, data visualization became mainstream. Easy storage and instantaneous access to data made visualizing information easier to accomplish and publish.

Why is Data Visualization Important?

• Human eyes are drawn to colors, shapes, and patterns, and our capacity for pattern recognition helps our brains separate and process information. This ability to interpret visual stimuli helps make us self-reliant.
• Data visualization makes it easy to convey information in a universally understood manner, regardless of the reader’s discipline and expertise.
• It is increasingly valuable for professionals to use data to make decisions. Visualization makes it easier to understand the who, what, where, when, and how of a topic.

Data Visualization Benefits:

Any field – science, math, finance, performing arts, education, sports – benefits from leveraging visual storytelling.

• Businesses can make better choices without the need for text-heavy spreadsheets and reports.
• Decision sciences can use data visualization to teach patterns to machine learning (ML) algorithms, eliminating the need for manual intervention once the algorithm has learned to spot factors that influence customer behavior.
• Data visualization can predict sales volumes based on the observations found through analysis.
• In healthcare, data visualization can convert complex patient, practitioner, and other data into a user-friendly visual format.

Data Visualization Examples (Types of Data Visualization)

The biggest challenge for users is deciding which visual best suits the business purpose.

Some of the most widely used visualization techniques include:

• Charts
  • Pie charts divide circles into sections to compare the size of each component to another.
  • Line charts use lines that demonstrate changes by plotting a series of data points over time.
  • Area charts are similar to line charts. They connect data points with line segments by stacking variables and applying colors.
  • Column charts represent data in vertical stacks on an X-Y axis.
  • Bar charts are similar to column charts. Bar charts represent data in horizontal stacks.
  • Fever charts are graphical representations showing the change of a variable over time. They are usually used for data that changes continuously, like stock markets.
  • Time series charts are graphs or plots that illustrate data points at successive intervals, corresponding to a quantity and the time measured.
• Heat maps display behavioral data by location. They can be on a map or a webpage.
• Scatter plots reveal a relationship between two variables on an X-Y axis with dots representing data points.
• Treemaps are displays of hierarchical data in a nested shape, like a rectangle. They are used for comparing proportions between multiple categories.
• Histograms distribute numeric data into ranges or groups to identify outliers.

Where to Use Data Visualization: Storytelling and Dashboards

Storytelling can excite and engage the audience and has been a universal part of human nature since ancient times. Data visualization helps tell the information’s story to business users through visuals called dashboards, which are the best way to display the graphical representation of data.

When designing a dashboard, there are certain yardsticks that define how well it should perform based on the intended audience:

• Executive dashboards are amazingly fast, although they have fewer charts and almost no parameters or filters. They are immediately digestible without much thought.
• Exploratory dashboards are fast and have many parameters and filters. They enable users to slice-and-dice to find patterns but can have a bit of a learning curve.
• Operational dashboards are fast and have write capabilities that can solve issues. They are geared towards diagnosing and solving specific problems.

Data Visualization Tools and Software

There are several data visualization tools available in today’s market. The most widely used are business intelligence (BI) reporting tools.

With the help of these visualization tools, business users can navigate through dashboards to track company performance across key performance indicators (KPIs).

Data Visualization Software Tools and Their Features

• Microsoft Power BI is an interactive data visualization platform designed primarily for BI. It includes Power BI Desktop and Power BI Service.¹
• Tableau is an analytics platform driven by data to forecast business and strategies.²
• MicroStrategy is an embedded analytics platform that transforms data into BI. It includes artificial intelligence/machine learning (AI/ML) applications.³
• SQL Server Reporting Services (SSRS) is a Microsoft-provided on-premises reporting tool for creating, deploying, and managing paginated mobile reports.⁴
• SAS Visual Analytics is an integrated environment for data exploration and analytics.⁵
• Qlik Sense⁶ and QlikView⁷ are business analytics platforms to uncover insights through data integration and analytics.

Free, Open-source Data Visualization Tools

• D3.js is a JavaScript library that produces powerful and interactive data visualizations in web browsers using hypertext markup language (HTML), cascading style sheets (CSS), and scalable vector graphics (SVG).⁸
• ECharts is a JavaScript- and ZRender-based lightweight library for powerful charting and visualization that adds intuitive, interactive, and highly-customizable charts to products, research papers, presentations, and more.⁹
• Vega defines itself as “visualization grammar,” providing support to customize visualizations across large datasets. It is accessible from the web.
• deck.gl is Uber’s open-source visualization framework suite, used for exploratory data analysis on big data. It helps build high-performance, GPU-powered visualizations on the web.

Big Data and Data Visualization

Big Data, as the name says, primarily refers to data that is massive in volume and complex in detail. This data can come from a variety of sources such as documents, videos, images, streaming sites, social networks, or even structured data such as databases or transaction processing systems. There are several approaches to keep in mind when dealing with Big Data.

• In the modern world, data is everywhere, and it is overwhelming; as the saying goes, “data is the new oil.”
• With the enormous need to pull insights out of information, the concept of Big Data has become popular in the market. This has given more importance to visualization than before.
• The previously discussed heat maps and fever charts are unique techniques in the realm of Big Data, and they require powerful machines for processing.
• Organizations wanting to make the most out of Big Data visualization tools should hire an expert to identify the data sets and visual styles needed to optimize data usage.

Data Visualization with Python

Creating data visualization with the programming language Python helps make dashboards interactive and customizable. Several Python-based offerings, like those listed below, provide excellent support for visuals with different features.

• pandas is a free software library that is easy to use. It can create plots with its Series and DataFrames features.
• Matplotlib is a flexible, low-level library, but it requires more lines of code to be written.
• Plotly can create interactive plots and publish quality graphs.
• Seaborn is a high-level interface based on Matplotlib with less code.

When to Use Data Visualization: Best Practices

As with any field, data visualization can be misinterpreted if ineffective information is used. Some best practices will avoid this and ensure your data visualization is clear. These practices also reduce the load on data scientists.

The acronym D.A.V.S. can help users remember these best practices: business Data, Audience, Visual, and Simplicity.

• Data – Data visualization aims to meet user requirements by displaying benchmarks, objectives, and KPIs, then discovering which insights to show. For instance, a product manufacturing business looks for sales growth, while in sports, a midfielder's touches or a batter's runs are analyzed.
• Audience – Identify your target audience. What is the user trying to accomplish? Represent the user’s needs visually.
• Visual – Business users should choose the right visual representation when developing dashboards using self-service tools like Power BI. For example, pie charts can represent categories, but heat maps can show where a team spends most of its time on the field. It is also important to find aesthetically pleasing sizes, shapes, and colors for the visuals.
• Simple – Keep visual representations simple. It is important not to put all the information in a single graphic. Instead, be sure the user’s attention is focused by leveraging additional information when necessary and eliminating repeated information.

Data Visualization in Healthcare

Data visualization is essential in transforming and displaying medical information. The analyses done on healthcare datasets are often very large and complex. Data visualization simplifies the presentation of that critical material, making it easier to understand and determine the next best action.

The healthcare industry produces massive amounts of data from many sources. Data visualization tools have become imperative for helping medical professionals interpret data analytics easily, resulting in faster and better-informed decisions. Data analysis, data preparation, data modeling, and data visualization are part of a healthcare analytics field that is becoming more advanced through AI/ML technologies.

As with any other system, healthcare systems face errors and fraud problems. With data visualization, there is more transparency between patients, claims, practitioners, providers, and payers, creating a more intelligent system.

Data Visualization Jobs

A data visualization engineer is essentially a storyteller who focuses mainly on collecting and communicating complex information in a way non-experts can understand. These are some of the primary duties of the data visualization engineer:

• Fetch the data
• Profile and clean the data
• Identify historical data
• Validate data resources and datasets
• Forecast trends with the data
• Test the data

Careers in Data Visualization

These are some of the in-demand and high-paying jobs involving data visualization:

• Data Specialist
• Data Analyst
• Business Systems Analyst
• BI Analyst
• BI Developer
• Analytics Manager
• Data Scientist
• Data Engineer

Conclusion and Takeaways

Our world is full of so much data that human minds can’t process it all. But today’s array of data visualization tools in every price range allows data professionals to be storytellers who help businesses make better decisions and doctors make sense of complicated patient histories.

To start your journey toward harnessing the power of data visualization, hire a data science team with proven experience. Let them guide you to the right tools. And most importantly, know what you’re looking for, but be ready for the unexpected insights you find along the way!

To learn more about the products and services provided by Axtria and how we enable business users with data visualization, explore the following:

• Business intelligence and data visualization
  
• Analytics industrialization through Axtria InsightsMAx^TM
  
• Digital transformation for a top 10 biopharma with a next-generation commercial insights platform

This article is contributed by Sidharth Sirish, Technical Architect at Axtria.