Research Data Visualization Background By Sharda Et

Research Data Visualizationbackground As Noted By Sharda Et Al 2020

Research: Data Visualization Background: As noted by Sharda et al (2020), Data Visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included chats and graphs as well as other types of visual elements used to create scorecards and dashboards. Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc. ISBN-13: Assignment: What is data visualization? Why is it needed? Finally, write briefly on the historical roots of data visualization. Your research paper should be at least 3 pages (800 words), double-spaced, have at least 4 APA references.

Paper For Above instruction

Introduction

Data visualization is a fundamental aspect of data analysis that involves representing data graphically to facilitate understanding, insight, and decision-making. As described by Sharda et al. (2020), it intersects with various fields such as information graphics, scientific visualization, and statistical graphics. The primary purpose of data visualization is to transform complex datasets into visual formats that are easily interpretable, enabling stakeholders to recognize patterns, outliers, and trends efficiently. The necessity for data visualization stems from the increasing volume and complexity of data generated in today’s digital age, which makes raw data difficult to interpret without visual aids. Thus, data visualization serves as a bridge between raw data and actionable insights, making it indispensable in modern decision support systems.

The Need for Data Visualization

The primary reason for the growing importance of data visualization is the exponential increase in data sources and volume. Businesses, researchers, and policymakers now deal with massive datasets that require effective tools for interpretation. Without visualization, analyzing these datasets would be impractical, as traditional numerical analysis alone may not reveal significant patterns or relationships. Visualization enables analysts to quickly grasp complex information, facilitating rapid decision-making. For example, dashboards and analytics reports used in business intelligence (BI) are effective because they condense large data collections into clear visual summaries. Additionally, data visualization enhances communication; visual data is more accessible for diverse audiences, making insights more impactful. Moreover, visualization aids in identifying anomalies, trends, and correlations that might remain hidden in raw numerical data, ultimately supporting better strategic decisions (Few, 2006).

Historical Roots of Data Visualization

The roots of data visualization date back centuries, with notable milestones contributing to its evolution. The earliest forms involved simple charts and diagrams used by mathematicians and scientists to illustrate relationships and data distributions. One of the pioneering figures in the history of data visualization was William Playfair, who in the late 18th century invented several types of financial and statistical graphics, including line graphs, bar charts, and pie charts (Tufte, 2001). These innovations transformed how data could be visually communicated, setting the foundation for modern data visualization techniques.

In the 19th century, scientists like Florence Nightingale utilized data visualization to influence public health policies, notably through her polar area diagram that displayed mortality rates. The 20th century saw the advancement of computer technology, permitting more dynamic and complex visualizations. The development of computer graphics facilitated the creation of interactive dashboards and real-time data visualizations, crucial for fields like meteorology, economics, and healthcare (Kirk, 2016).

Today, data visualization continues to evolve with innovations such as infographics, geographic information systems (GIS), and immersive data representations like virtual reality. Its history illustrates a trajectory from simple visual forms to sophisticated, interactive tools that support complex decision-making processes across various domains (Robinson, 2013).

Conclusion

In summary, data visualization is a vital component of modern data analysis processes, enabling clear communication and insightful decision-making. Its necessity is driven by the ever-growing complexity and volume of data, which traditional analytical methods alone cannot effectively handle. The historical roots of data visualization reflect a gradual progression from basic graphical representations to complex, interactive platforms that enhance our ability to interpret vast datasets. Understanding this evolution highlights the importance of continued innovation in visualization techniques to meet the challenges of the data-driven world.

References

Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.

Kirk, A. (2016). Data visualisation: A handbook for data driven design. SAGE Publications.

Robinson, A. C. (2013). The visual organization of data: An early history. IEEE Computer Graphics and Applications, 33(6), 14-21.

Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.

Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science, & artificial intelligence: Systems for decision support (11th ed.). Pearson Education.