Comparison of Data Visualization Tools and Programming for Data Analytics

Introduction

Data visualization means the presentation of data in a visual form such as graphs and pictorial format. It assists an individual in decision making by seeing the analytics visually so that they can grasp new patterns and difficult concepts quickly. Through interactive visualization of the data, you can take the idea of displaying a step further by enhancement of the technology to drill down into charts and graphs for more accurate data processing and changing how a person sees and processes data. The concept of data visualization has been in practice for several centuries, and this draws back from the use of maps and graphs in the early 17th century to the use of a pie chart in the 18th century. The use of technology has greatly lit the fire in regard to data visualization. Computer and other electronic devices have made it possible to process and present large amounts of information at a very high speed. In the modern world, data visualization has become a highly evolving part of science and art, which is prone to change the corporate landscape over the years.

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Literature Review

Information visualization is described as the accepted term used in the field of graphical communication where data visualization is generated. Underpinning the various data presentations, designs, and data analysis in the research are well-established principles of statistical graphics and human perception. Christensen (2017) wrote an extensive research paper on the field where visual elements of communication such as shape, color, and size are used to measure the effectiveness of data communication and quantitative information analysis. Besides, Christensen, J. (2017) continues to look at the language of visualization technology, and information visualization generally looks at the subject from the standpoint of perceptual science. The research done on this particular subject is groundbreaking because of its approach to visual literacy and information presentation. The data analysis field is extensive and continuously evolving with the advancement of modern technology.

Differences Between Data Visualization and Data Analytics

Data visualization is described as the presentation of data or information in a visual context in that the trends and patterns inherent in the data visualization process can be explicit. These trends and patterns, however, may not be precise in text-based data. Most of the modern tools that have been used data visualization process can filter the data so that the resulting data can be manipulated as per user perspective. On the other hand, the traditional forms of visualization, such as tables, column charts, and graphs, have been ousted in the modern world by the use of 3D visualization patterns. Data analytics has gone further to discover and identify the patterns and trends that exist in the field of data analysis. Even though data visualization enables an individual to observe and make sense of the data, it does not provide the required and complete picture of the intended information. The effectiveness of data visualization greatly relies on the data or information that is used in relaying the visualization data in the first place. As a result, the feeding of the visualization engine with incorrect data and information will result in erroneous, half-bake, or obsolete visualization of the data.

Data Visualization and Programming

Kumar& Kirthika (2017) argues that with the increasing demand for new ways of visualizing data, several standards, and guidelines to support the creation of visualized data have been put forth. There is an advanced body of that purely works and focuses on data visualization and the existing challenges that this field faces that is not common in the interactive system evaluation. This can be improved by how the experts in the field of information systems organize programming knowledge. Research establishes that knowledge of programming is organized in a broader conceptual structure instead of syntax, and this can improve the data visualization process. Golfarelli & Rizzi (2019) propose that they can organize their work and knowledge in what the scholar terms as plan. Plans are defined as the procedure of achieving a given, such as the computation of a sum. These programs are presented as codes with gaps for specific detailed information.

Data visualization and programming are, therefore, related because most of the visualizations programs and patterns are programmed to attain the desired output that will be presentable. Most of the programming knowledge has advantages but has left a lot of gaps in regards to their applications. Therefore data visualization, in addition to the programming languages and codes used, are more feasible to data analytics as compared to pure programming.

Importance of Data Visualization Compared to Programming in Data Analytics

According to Kumar& Kirthika (2017), because of how human being processes information and data using either graphs or charts to visualize a considerable amount of data, it is easier to present information as compared to poring over reports and spreadsheets. Data visualization as a method of representing data is an easy way to convey the various concept universally. This can be experimented with using different scenarios and making adjustments where required.

Data visualization, as a tool of data analytics, can also:

• Identify relevant areas that need proper improvement and attention
• Assist you to clarify which factors can influence the customer's satisfaction and a result it a major too in advertisements
• It helps an individual to understand which type of product is based on being placed in what position to attract the attention of the public.
• It can predict the sales volume concerning the quality of the visualization process.

Methodology

The most outstanding demerits in the evaluation of the data visualization processes rely on the fact that it is complicated to replicate the condition under which this system is used. In order to mitigate and avoid most of the common pitfalls related to data visualization, the research has used various methods in data analysis and collection, which is supported by the previous study and several case studies. Because there is no globally applied system for evaluating data visualization processes, proper care has been taken to focus on the scenarios that are relevant to the study. The main two scenarios considered in the evaluation of the visualization process are the evaluation of the user experience and the assessment of the user performance.

Research Design

In the evaluation of data visualization integrity, three ways of visualizing football data interactively are compared in three different forms of interactive prototypes. The prototypes generally represent the fantasy of football because the study only focuses on the visual representation of the player data. The figure below depicts the prototypes to be evaluated

Identification of the Variables

To ensure that there is accuracy in the data presentation analysis, each of the variables to be analyzed was identified in the figure above. Data visualization that is represented by the three prototypes is described as the independent variables that have been used throughout the evaluation of the data visualization process. The dependent variable, which was the user experience and performance, was measured against the independent variables. The figure below shows the analysis of the dependent, dependent and controlled variables use in the analysis

The Evaluation Method Used in This Data Visualization Process

To ensure that there were fairness and valid judgments on the three prototypes, all the information was evaluated against a specific matrix utilizing a variety of evaluation methods. The use of the mixed techniques helps in capturing both the subjective and objective information so that they are used to measure the impact of each of the visualization technique and their effects on the user experience and performance. The table below illustrates this

Quantitative Method

The quantitative data collection method generally relies on the structured data collection method and random sampling that has a diverse experience with the predetermined categories of responses. The quantitative methods that were used in the analysis of data visualization taking the three prototypes as the case study are

Time tasks

Time task is the scripted tasks or questions that are asking the participants to present a prototype in a given manner and a controlled way. Each of the functions provided reflects the real scenario, such as making a comparison and a selection based on the fantasy player prices and points. The tasks were organized in such a way that there was no biasness to either participant or the prototypes.

Error rate

As a requirement of the controlled experiment, the number of tasks that were failed or abandoned was also measured using this quantitative data analysis method. Error is always regarded as a very significant issue while measuring the performance of any system.

Qualitative Methods Used in the Research Study

Qualitative data collection methods are used in impacting the evaluation process by giving useful information to understand the procedures concerning the changes in peoples' perceptions Kumar& Kirthika (2017). The qualitative methods in this research are

Observation

The behaviors of the participant performance concerning the data visualization technique of the prototypes were observed in each of the tasks. This was very brief, unobtrusive, and subjective in which all the participants were being asked to ensure that they complete a particular task other than discussing the prevailing actions at a given time.

Voice mapping

This qualitative technique is used in the study to measure the performance of the participant's attitude towards the system. It was used in measuring the comparison between the system or measuring the overall effect of the design improvement as the system elapse. The participants were required through this method to make a line showing the perceived ease of use and participant satisfaction with the order.

Conclusion

In a situation where an individual or an organization wants to establish an enterprise, a definite strike automatically comes between data visualization and programming. It is clear that, however, much data visualization essential than programming; it cannot solely solve all the problems. Therefore all two must be cooperated to achieve the desired result. The selection of the data visualization tools varies from one organization to the other, and the type of data that the organization handles and the degree of the data affects this.

The result of the evaluation of the prototypes proves that data visualization can significantly improve the experience and performance of fantasy football, and this applies to almost all the activities carried out by the various organizations. Both data visualization and programming in data analytics are essential to harvest potential within the organization's variety of data.

References

Analysis of High Dimension Clustered Data using Visualization Technique. (2017). International Journal Of Science And Research (IJSR), 6(1), 1048-1052. doi: 10.21275/art20164181

Christensen, J. (2017). Effective Data Visualization: The Right Chart for the Right Data, and Data Visualization: A Handbook For Data-Driven Design. Technology|Architecture + Design, 1(2), 242-243. doi: 10.1080/24751448.2017.1354629

Christensen, J. (2017). Effective Data Visualization: The Right Chart for the Right Data, and Data Visualization: A Handbook For Data-Driven Design. Technology|Architecture + Design, 1(2), 242-243. doi: 10.1080/24751448.2017.1354629

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