**1. Introduction**

This section gives a brief overview of this chapter. It starts by discussing what usability is and why advanced analysis is required to extract useful information from raw data. Later, the idea and need for visual analytics is discussed and its implementation pipeline is presented which shapes the basic working structure of Visual Analytics of Simulated Data (ViDAS). Furthermore, the context of ViDAS is presented, keeping the human-centered design in mind. While the expert evaluation and feedback is based on simulation data, ViDAS is equivalently capable of handling usability data.

#### **1.1 Motivation**

With the technological advancements, data is automatically recorded and stored using various sensors and monitoring systems. This large amount of complex data is known as big data which is unstructured and contains hidden values. Therefore, there is a need to analyze this data, discover new values, and gain an in-depth understanding to efficiently manage and organize it [1].

Currently, there are a number of tools available in the market that help its users in analyzing the data and finding trends in it. These tools are mainly divided into two main categories; DM (Data Mining) tools and BI (Business Intelligence) tools. The DM tools are mainly focused on applying advanced machine learning models to the data and do not incorporate the advanced visualization techniques to go with it. The BI tools are mainly focused on EDA (Exploratory Data Analysis) techniques and include various clustering techniques along with interactive visualizations that help in understanding the data. However, the BI tools do not include built-in machine learning models and instead, rely on either third-party extensions or ask the user for a premium subscription.

This chapter presents a solution in the form of a web-based tool called ViDAS that incorporates the best features from both DM and BI tools. ViDAS combines these features in such a way that makes it effortless for the user to apply machine learning techniques on a complex data set, and then visualize the transformed data interactively.

### **1.2 Background**

Intuitive computing technologies make their way into daily life and at the same time, the market is saturated with rival brands. This has made usability more popular in recent years, as businesses see the advantages of researching and designing their products using user-oriented approaches rather than traditional methods. Through knowing and studying the relationship between the product and the customer, the usability specialist may also have perspectives that are unfeasible by traditional market research. For example, after examining and evaluating customers, the usability specialist could recognize the requisite features or design shortcomings that were not expected.

Usability can be defined as the capacity of a software system to provide a condition for its users to perform tasks in a safe, effective, and efficient manner while enjoying user experience [2]. Usability requires techniques for assessing it, such as needs analysis [3] and the study of the values underlying perceived utility or beauty of the object. In the field of human-computer interaction and computer science, usability studies the sophistication and consistency with which interaction with a software system is built. Usability finds customer satisfaction and utility to be a quality component and strives to improve user experience through iterative design. Different researchers often focus on different parameters of usability, usability consultant Jakob Nielsen and computer science professor Ben Schneiderman have written (separately) about a framework of system acceptability, where usability is a part of "usefulness" and is composed of: Learnability, Efficiency, Memorability, Errors, and Satisfaction [4].

There are various methods that allow data collection of the above-mentioned parameters such as in-depth evaluations with a focus group, documenting the user experience, etc. Normally these parameters are analyzed using MS Excel and other traditional methods which require a lot of manual work. This is where the concept of visual analytics comes in as it provides an alternative approach that greatly supports the analysis process by the use of machine learning methods and detailed visualizations. Additionally, some of this usability data maybe complimented through simulations or machine learning methods that can be then analyzed to support the user-testing process.

In most cases, the dependencies and correlations of these parameters are not clearly identifiable, which forces the data analyst to make an educated guess. This guess is solely based on the expertise of the analyst which may result in extra time

#### *Implementing Visual Analytics Pipelines with Simulation Data DOI: http://dx.doi.org/10.5772/intechopen.96152*

and effort spent in testing the focused parameters. This approach is known as the "trial-and-error approach" [5] which focuses on finding a good solution and states that the data analyst must spend more time examining the parameters than building the model. In such a scenario, a better approach is to use visual analytics to understand the data better and find hidden relationships between the parameters. Visual analytics aims to help data analysts in identifying correlated parameters than relying on just the trial-and-error approach.
