Trends and Applications of Data Visualization and Analysis: Capstone Academic Research Project for Business Intelligence and Data Visualization

Graduate Course

Jonathan Frahm, Thomas Edison State College

Maurice Dawson, University of Missouri-St. Louis

December 2014

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Contents

Abstract

Introduction

Open Source Software

Wheat Production

Conclusion

Author Bios

References

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Abstract

Business Intelligence (BI) is an ever expanding field in which a large amount of data is manipulated to extract hidden knowledge. As BI has developed, the complexity of business models and the ever increasing global economy connections have made the process more difficult. Statistical data visualization is designed to communicate complex relationships and trends more clearly than pure number formats. This paper reviews the general understanding of BI as a field and demonstrates select functions and applications of data visualization and data analysis. Global wheat production statistics are utilized to demonstrate the capabilities of modern business intelligence platforms.

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The explosion of analysis and visualization options has created a customer-driven focus for business intelligence providers. This focus has led to some general trends in business intelligence that affect how analysis and visualizations are conducted. Perhaps the most significant trend is the increasing ease of integration of a new business intelligence platform with existing data warehouses [15].

Introduction

Changes to business intelligence also branch into some intangible benefits for most companies. One of these is improved communication among the company. Data analytics models have increased both the availability and speed at which reports on critical metrics are delivered [17]. Organizational data is analyzed in a fraction of previous reporting times. The result is real-time analysis that fits in normal workflows and allows much greater understanding and control of business strategy. Reporting speed and availability allows the company to remain very fluid and responsive to rapid market or performance changes. The success of data analysis applications in this respect has created an expectation of the same in all data analytics platforms, effectively establishing a market standard [6].

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Open Source Software

Another main method of business intelligence is open source software. Open source software refers to a collection of freely accessible programming languages and programs that allow companies to create their own data analysis and visualization platforms [9-13]. Perhaps the premier programming language used in open source software is R [14]. R statistical language was developed to allow the virtually limitless creation and adaptation of statistical functions and visualizations in a thread format. Various programs have been developed to more easily use R, and make the comprehension and application of the language much simpler for those without a programming background. For business applications, however, a team of programmers is desired to create the level of performance needed for professional applications. One unique characteristic of R statistical programming language is the complete customization that can be attained. The raw capability of R makes it a viable option for any business to consider for data analysis [14].

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Wheat Production

Wheat production is a vital part of many food products and is the starting raw material that keeps thousands of companies in operation. The global production of wheat exceeds 250 million tons annually [18]. This market focuses heavily on trends and prices greatly fluctuate with production, quality, supply, and international demand. Modern data analysis provides valuable tools to evaluate past and present wheat production data. These tools fall into two main categories: data reports, and data visualizations [1].

In Figure 1, the production numbers are plotted for 2014. Each plat has a designated color based on the percentage of the total world wheat production amount. Blue plots indicate a low level of percentage, while red indicates a high percentage of the total when compared to the other plotted points. Not only does this give a visualization of the countries production amount, it provides a distinction or highlight of the major players in global wheat production. This detail facilitates rapid comprehension of important data that could have been overlooked in other charts.

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Wheat Production cont.

Figure 2 is only displaying one year [8] production numbers for each country. A further development of this geomap is a sequential, time series progression through each year of the data. The result is a fluid adjustment each year of the growth of a country’s wheat production in relation to the total [14]. Since the size of the circles indicating production is compared to the total in each progressing geomap, overall growth in total production is not being tracked. This particular geomap is indicating a percentage of the world total production. Figure 2: Geomap of global wheat production 2014

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Conclusion

Data analysis and visualization are critical parts of a business intelligence platform [1]. Understanding the capabilities and purposes for various tools and visualizations will enable modern businesses to discover valuable knowledge about the business, market, or environment. Emerging trends in data analytics enable better communication and long term success of an organization. The global wheat production examples demonstrate the value of the appropriate method of knowledge communication and discovery. Intuitive managers understand the benefits of business intelligence and will utilize the tools available to optimize their business’s performance.

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References

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Author Bios

Jonathan Frahm is completed a Master of Business Administration (MBA) and Bachelors of Science in Business at Thomas Edison State College (TESC) in Trenton, New Jersey. His main areas of research interest are R Programming, predictive analytics, visualization, and Business Intelligence (BI).

Frahm is current a training specialist in which he provides software application training for physicians, mid-level professionals, billers, and other associated healthcare administration staff. Previously he was a product management responsible for data mining and analysis.

Maurice Dawson (M'13'-14) received a Doctor of Computer Science from Colorado Technical University. Additional degrees completed from this institution are a Master of Business Administration and Master of Science in Management in Information Systems Security. A Bachelor of Science in Applied Technology was obtained from Athens State University in Athens, Alabama. Dawson serves as an Assistant Professor of Information Systems at the University of Missouri-St. Louis, Assistant Professor (Honorary) of Industrial and Systems Engineering at The University of Tennessee Space Institute, and Fulbright Scholar at South Ural State University.

Dr. Dawson is recognized as an Information Assurance System Architect and Engineer by the U.S. Department of Defense. Research focus area is cyber security, systems security engineering, open source software, mobile security, and engineering management. Dr. Dawson is a member of the ACM, IAENG, International Information Systems Security Certification Consortium (ISC)2, and Information Systems Audit and Control Association (ISACA).