What is Visual Analytics: Key Concepts

by | Dec 31, 2018 | Data Analytics

8 Min Read. |

Visual Analytics is one of the hottest trends that will rock 2019. Business owners who were so far baffled over the complexities of endless spreadsheet files filled with data appreciate what Visual Analytics brings to the table.

Now, data can be ingested, analyzed, and translated into a visual representation to make data interpretation easier and more accessible to key players in an organization. Here we have discussed Visual Analytics key concepts.

One of the most pertinent questions asked by data analysts today is: what is Visual Analytics? Well, to define Visual Analytic in simpler words, we may that is the science of analytical reasoning supported by interactive visual interfaces.

Data flow and volume both have increased manifold in the current age of information exchange. The last few decades have witnessed the growth of a large number of automatic data analysis methods.

However, it becomes imperative to include human intelligence at an early stage in the data analysis process, because of the complexity of the processes involved.

Visual Analytic methods help business decision makers combine their human capabilities with the storage and processing capacities of the improved methods to gain insight into complex problems.

It is now possible to directly interact with the data analysis capabilities of today’s computer, allowing them to make well-informed decisions in complex situations.

Visual Analytics

Visual Analytics

What is Visual Analytics: Definition & Purpose?

Visual Analytics, according to Thomas, J., Cook, K in his essay titled Illuminating the Path: Research and Development Agenda for Visual Analytics (2005) “is the science of analytical reasoning supported by interactive visual interfaces.”

To put it in simpler terms, Visual analytic may be explained as a kind of inquiry in which data that provides insight into solving a problem is displayed in an interactive, graphical manner.

Visual Analytics can be perceived as an integrated approach that combines visualization, human factors, and data analysis.

Visual Analytics in the context of visualization relates to the areas of Information Visualization and Computer Graphics, and with respect to data analysis, it benefits largely from methodologies of information retrieval, data management & knowledge representation as well as data mining.

A Visual Analytic system often uses a specific software dashboard to present analytics results visually. For example, the dashboard screens might have different types of engines involving visual graphs, pie charts or infographics tools, where, after computational algorithms work, the results are displayed on the screen.

The Visual Analytics interface makes it easy for a human user to understand the results, and also make changes simultaneously that further directs the computer’s algorithmic process.

Part of the innovative idea of visual analytics is this interplay between humans and computers, wherewith the combination of raw algorithmic strength and adept visualization, each one strengthens the work of the other in refining data sets for various goals and objectives.

Visual Analytics

Visual Analytics: Definition & Purpose

What is Visual Analytics: Visual Analytics Tools & Methods

For creating data visualizations, you will have two options: hiring a data visualization specialist/agency or using data visualization software. Both methods are appropriate for different types of visualization projects. If you have a project that is best served by data visualization software, then you need to choose the best data visualization tools for your project.

Here we take a peek into some of the best Visual Analytic tools and methods.

(i) MicroStrategy Analytics Desktop

MicroStrategy Analytics Desktop, one of the most popular Visual Analytics tools, allows you to connect any database you like, import Excel spreadsheets/CSV files and even import data from an online source. Moreover, you can blend multiple data sources and types to gain the exact insights you need.

The basic version of MicroStrategy Analytics Desktop is free and allows for insightful, visualizations to be created. You may use more advanced features, like Salesforce Reporting, Enterprise Information Integration or Mobile access for more complicated project requirements.

(ii) Domo

Domo offers an online business intelligence tool that has a sleek UI and is specifically designed to allow users to build sophisticated dashboards with no IT involvement. The Domo platform works best for users looking to visualize data in existing cloud-based apps like Salesforce.com.

In addition to cloud-based apps, Domo can take input from databases, spreadsheets, and even social media platforms. The output is just as versatile – Domo created dashboards are designed to be viewable on tablets and mobile devices in addition to a traditional PC interface.

(iii) Tableau

Tableau, another popular Visual Analytics tool, offers a suite of tools that include an online, desktop and server version. All of these versions provide an easy-to-use drag and drop interface that can help you quickly turn your data into business insights. The online and server versions allow your entire team to build and work with the visualization tool.

Tableau can also connect to local or remote data of many different formats. Additionally, the Tableau engine can connect to live data for up-to-date visualizations (at the sacrifice of some speed) or warehoused data for many smoother-moving visualizations.

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(iv) QlikView

The QlikView business discovery platform is one of the few Visual Analytic tools offered by Qlik. QlikView helps you quickly analyze your data in multiple dimensions. Also, QlikView can work off of data in memory instead of off your disk, allowing for real-time operational BI environments (like monitoring financial transactions).

QlikView is also capable of working with a wide variety of data sources, including SAP, Oracle, Salesforce.com and other legacy data files like Excel spreadsheets. What’s more, QlikView can combine these disparate data sources into a single visualization or dashboard.

Want to know about the key challenges for Visual Analytics?

Visual Analytics Applications

While Visual Analytic though extensively used it is largely restricted to research and academic environments. A quick look into any of the visual analytics research papers or visual analytics ppts strengthens this proposition.

My observation says a major gap exists between state-of-the-art visual analytics research and its application in operational environments.

Lack of experience in the operational space is the primary reason that has prevented the widespread adoption of tools and techniques whose effectiveness has already been experimentally proven.

Without productive visualization techniques in operational environments, analysts are unable to gather information from humongous data sets coming from multiple sources.

Visual Analytics

Visual Analytics Applications

The deficiency is glaring in fields like cybersecurity where it cannot be known a priori which data are relevant. Cyber Analysts often require exploratory visualizations that empower them to drill down and investigate unknown threats without sacrificing their situational awareness of the environment at large.

While current visualizations offer intuitive representations, they do not take advantage of humans’ innate exploratory capabilities.

Improving Visual Analytics in an operational environment thus rests on first achieving an understanding of the human cognitive processes that are in play when visual representations are encountered.

Accurately aligning the strengths of humans and machines involves reaching across multiple social and scientific disciplines to achieve a better understanding of how concepts are stored and processed by the human brain.

As a general rule, designers should use improved visualization techniques, such as clutter reduction and highlighting, by drawing on current research in human perception.

Another major area where Visual Analytics has made a mark is Traffic Modelling and Simulation. we combine interactive visual techniques with computational methods from machine learning and statistics, to support analysis and modelling of large amounts of spatiotemporal data having the form of spatially referenced time series (TS) of numeric values.

One application area where such data is encountered in huge amounts is the analysis, evaluation, and prediction of regional (e.g., city-wide) traffic volumes based on positional records (e.g., GPS tracks).

Visual Analytics toolkit is developed for data-driven modelling and simulation of constrained traffic. This framework includes interactive visual interfaces to a library of modelling methods, supports the selection of a suitable method, adjustment of model parameters, and evaluation of constructed traffic volume models.

Subsequently, these models can be used to simulate traffic flows and predict traffic situations, thus providing planners with a valuable, visual-interactive tool for their needs.

Visual Analytics

Visual Interactive Tools

Career in Visual Analytics Research

Does Visual Analytic interest you? Then you must go for a career in Visual Analytics research.

You may enroll for an advanced degree or master’s degree in Visual Analytics that teaches you about the phenomena of visual analytics, different fundamental concepts, techniques, and methods of visual analytics, and help you understand linkages between business intelligence and visual analytics and the potential benefits for organizations.

The exponential rise of data has led to an unprecedented demand for Big Data scientists and Big Data analysts. Enterprises must hire data science professionals with a strong knowledge of deep learning and Big Data applications.

However, there is a sharp shortage of data scientists in comparison to the massive amount of data being produced. This makes hiring difficult and more expensive than usual.

Visual Analytics

Career in Visual Analytics Research

You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Applications. Students with a master’s degree in Economics or Social Science can also be a data scientist. Take up a Data Science or Data Analytics course, to learn Data Science skills and prepare yourself for the Visual Analytics job roles that interest you.

The theories, concepts, and methods you learn should be applied in practice and exercise sessions. Choose a course that allows students to work in the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises.


Digital Vidya offers one of the best-known Data Analytics courses for a promising career in Data Science. Industry-relevant syllabuses, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.

In addition, students also get lifetime access to online course matter, 24×7 faculty support, expert advice from industry stalwarts, and assured placement support that prepares them better for the vastly expanding Big Data market.

Register for FREE Orientation Class on Data Science & Analytics for Career Growth

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