MITS6002 Business Analytics

Introduction

Data visualization  involves the process of representation of data with general patterns. These patterns are used for decision making based on the understanding of the output. Through this viewing the insights related to business can be viewed together with the affected areas [5]. Data visualization is important in understanding the correlation between different pattern and how they result into better outcomes. The process that involves building a related story by interpreting a given data for better analysis using the tools for the inter dependency between various relationships. [4] To achieve this different business tools in the industry can be used.

Currently business analytics are great importance in business context. Business analytics is a very powerful tool in today’s market due to the rising of complexity of the global trade network and trends of multiple and big data. Business analytic tool has become significant in operational efficiency, boosting better strategic changes, stimulating costs and improving organizational financial performance [7]. Due to flooding in of data and the increasing demand the importance of business analytics has improved. Therefore, multi-dimensional data visualization analysis tools are commonly used in businesses. This essay will look at methods of multi-dimensional data in visualization.

Data Visualization

Data visualization is a process of transforming collected raw data into the visual contents using diversification forms of maps, bars, plots and graphs. It is one of the most applied tools of data analysis and it is capable of turning multiple large data sets into attractive visual form which may include indicators, static images, colorful dashboards and data depicting video which improves the overall effectiveness of data analysis. In this case raw data that has been collected, analyzed and summarized and presented become more understandable, interactive and communicative with the business organization [6]. To facilitate the data analysis process of dealing with huge sets of raw data there is need to create visualization tools and software’s due to increased demand for data visualization. Data visualization is used to perfectly illustrate simplicity. Hence data visualization is the way of representing multiple huge sets of data in a simplified manner which in turn enables the targeted audience has a clear understanding of information or patterns of the data being presented [6]. Data visualizations assists in reducing the difficulty associated with communicating the data and renders concise and brief forms of presenting data in an attractive manner which removes the problems of handling large amount of information.

Data visualization differs from over-brief data summary as it can sort different data sets collected and display them with 100% originality and efficiency. This preserves the accuracy and the amount of raw data [10]. Therefore, due to its functions and characteristics data visualization is used in professional context especially international companies. Among the industries include automobile, banking, social media companies, telecommunication and software companies employ data visualization to present for more effective information sharing, decision making process, risk predicting and demand forecasting.

Techniques of multi-dimensional data visualization

Due to tides of big data overwhelming amounts of data is affecting the smooth running and operations of many companies. Therefore, there are intense needs to use techniques and popular applications and software [6]. The different kinds of data forms bring out the nature of data visualization. There are four types of data visualization forms which consist of a more connected data visualization system.

Techniques

1. Temporal data visualization – This technique can effectively handle the data by use of scattered plots, timelines and polar area diagrams if the data visualized meet the condition of linear and multi-dimensional.
2. Hierarchical data visualization – Use of tree diagrams and ring charts to fully demonstrate the organization of data could categorized into different branches and levels by application of hierarchy method.
3. Network data visualization – network data visualization can be applied on data that show internal relations with each other. It could employ matrix charts, histograms and alluvial diagrams and node-link diagrams.
4. Multi-dimensional data visualization – this kind of technique is applied when data has multiple dimensions and with more than two variable. It includes employing histograms, Venn diagrams and pie charts can effectively handle relevant data sets.
   
   

Standard Visualization

It is a class of visualization technique that is very effective in presenting two or three dimensional data. A number of techniques belong to this class which may include Pareto graphs, area, bar, x-y (-z) plots column charts, stacked bar among others [3]. [5] This study will focus on those techniques that are suitable for presenting multidimensional dat.

Multiple line graphs

This technique is used in multidimensional data. The values on the horizontal axis are not repeated for instance time and ordering of the table. The variable of interest is shown on the vertical axis. . This technique can also be used to show more than two dimensions or variables [1].

Permutation matrix

Permutation matrix displays a special type of bar graph. Data values can be presented in the heights of the bars as one bar graph represents one dimensional data. For each data dimension in permutation matric there is built a bar graph. All values with the same information are contained on the horizontal axis of all bar graphs. The color black in all permutation matrix represents values below average while the color black represents the values above average

Survey plot

This method is a variation of permutation matrix. The horizontal bars represent the values of each data. The data values are proportional to the width of the bars [8]. There are no spaces separating the bars since they are centered. Colors can be used to differentiate different classes.

Geometrical displays transformation

The aim of these techniques is to find interesting dimensions of multidimensional data sets. The categories from these techniques may include clustering techniques, scatterplots matrices and projection techniques.

Scatter plot matrix

This technique is used to plot two dimensional data where horizontal axis displays values of one variable while the vertical axis displays the values of another variable. The technique is effective when looking for all pairs of variables in a dataset.

Parallel coordinates

This technique was introduced by Inselberg and it represents multidimensional data using lines. Parallel axis represents the data dimensions. The minimum and maximum values are each dimension is scaled to the lower and upper parts of the vertical axis.

Icon based displays

This technique is applied to map the attribute of multidimensional data values into features such as color and shape of an icon. [9] States different types of icons that can be used to visualize high dimensional data items such as star glyphs, color icons, Chernof faces, Tile bars and needle icons. These displays are effective when the data items are relatively dense.

Star Glyphs

In this technique a data item is represented by an icon known as the glyph having n lines emerging from a point that is uniformly separated angles. [10] The n lines are approximately equal to the number of data dimensions. The value of each data dimension is proportional to the length of the line. To reduce misinterpretation when glyphs overlap, the end point for a given glyph is connected to form a polygon.

Stacked displays

This technique is required when data is needed to be partitioned in a hierarchical fashion. The information contained in the data dimensions dictates the kind of partitioning. The techniques are effective for representing hierarchical data.

Tree maps

These are hierarchical visualization of multidimensional data. The data information is mapped to the size, color, label and position of the nested rectangles.

Conclusion

In this essay, seven multidimensional data visualization techniques for representing financial performance data were reviewed. Different multidimensional visualization techniques were analyzed and how they can be used to display financial performance of companies. Capabilities of different visualization techniques were also analyzed in uncovering certain patterns in financial data.

Potential benefits of using certain visualization techniques were also highlighted for solving a busines problem such as uncovering all interesting patterns of data. One of the benefits was that a user is able to see different facets of the data and the problem under investigation by using different visualization. A second benefit is given by the descriptive power of some techniques over the others.

This study can be extended to study other multidimensional data visualization techniques. The study did not consider the interaction of techniques that is required for exploration of data efficiently. Therefore, the implementation of interaction techniques such as brushing and linking techniques would enable the users to see their actions on more than one view. This will be the recommended research for designers of visualization systems.

References

[1]   M. L. U. R. Shahid, V. Molchanov, J. Mir, F. Shaukat, and L. Linsen, “Interactive visual analytics tool for multidimensional quantitative and categorical data analysis,” Information Visualization, p. 147387162090803, May 2020, doi: 10.1177/1473871620908034.

[2]  I. Uzzaman and T. M. Adnan, “Visual Project Management Tools and Practices: Supplier Company Case Study,” Journal of Business Analytics and Data Visualization, vol. 1, no. 1, pp. 35–50, Aug. 2020, doi: 10.46610/jbadv.2020.v01i01.005.

[3]  D. Ingale, “A Critical Analysis of Corporate Social Responsibilities in Underdeveloped Countries,” Journal of Business Analytics and Data Visualization, vol. 2, no. 1, pp. 28–33, Apr. 2021, doi: 10.46610/jbadv.2021.v02i01.003.

[4]  J. H. Elder, “A new training program in data analytics & visualization,” Big Data and Information Analytics, vol. 1, no. 1, Sep. 2015, doi: 10.3934/bdia.2016.1.1i.

[5]  Y. R, “Evaluation of OCD-Spectrum Disorders Using Data Analytics and Visualization Techniques,” International Journal of Psychosocial Rehabilitation, vol. 24, no. 4, pp. 4027–4038, Feb. 2020, doi: 10.37200/ijpr/v24i4/pr201515.

[6]  J. Géryk, “Visual analytics of educational time-dependent data using interactive dynamic visualization,” Expert Systems, vol. 34, no. 1, p. e12175, Aug. 2016, doi: 10.1111/exsy.12175.

[7]  A. Garg and D. P. Goyal, “Sustained business competitive advantage with data analytics,” International Journal of Business and Data Analytics, vol. 1, no. 1, p. 4, 2019, doi: 10.1504/ijbda.2019.098829.

[8]  S. Kameswaran and V. S. F. Enigo, “ScrAnViz: A Tool for Analytics and Visualization of Unstructured Data,” International Journal of Business Intelligence and Data Mining, vol. 14, no. 1, p. 1, 2019, doi: 10.1504/ijbidm.2019.10012009.

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