Data Visualization Techniques: Traditional Data to Big Data

Gandhi, Parul; Pruthi, Jyoti

doi:10.1007/978-981-15-2282-6_4

Cited by 8 publications

(4 citation statements)

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“…In today's world, data visualization is adopted when analyzing large and complex data, and this is done for effective projection and communication of predictive patterns and trends, enabling stakeholders to make actionable plans [37]. Explanation or communication through modern-day visualization instruments is more effective than traditional instrumental methods because it goes beyond the conventional demonstration of graphs, pie charts, tables, and bar graphs [38].…”

Section: Data Visualization and Communication Skillsmentioning

confidence: 99%

Critical Analysis of Innovative Leadership through Effective Data Analytics: Exploring Trends in Business Analysis, Finance, Marketing, and Information Technology

Adigwe,

Abalaka,

Olaniyi

et al. 2023

AJEBA

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Globally, leaders are lighthouses that help direct followers on the right decision to make, whether in careers, entrepreneurship, personal development, education, community and social services, relationships, health, finances, and enacting purpose. In today's data-driven environment characterized by information overload, leaders and organizations seek to harness the full potential of data to improve every facet of endeavor, including leadership; thus, leaders in management and other spheres of human endeavors must rethink leadership methods in line with data analytics. This research investigates the synergistic relationship between innovative leadership and effective data analytics in organizations across different industries. The study assesses the impact of data analysis, visualization, and governance on various aspects of innovative leadership. Structured questionnaires designed using Likert scale questions and statements were used as research instruments to collect data from 490 leaders across five industries, including Accounting and Finance, Marketing, Computer and IT, Business Development, and Education. Statistical analysis, including correlation and linear regression, examined the relationships between data analytics and innovative leadership variables. The research findings support the hypotheses, demonstrating that data Analysis Influences Innovative Behavior in leadership; data Analysis is significantly and positively related to Vision and Strategic Planning; data Visualization significantly impacts Communication Skills in leadership positively; Data Visualization significantly influences Vision and Strategic Planning in leadership positively; while a positive relationship exists between Effective Data Governance and Innovative Behavior. Data analytics is critical in achieving innovative leadership and improving organizational performance. Data analysis, visualization, and governance were essential in driving innovation and fostering clear communication. The paper recommends the promotion of a culture of collaboration and open communication, investment in employee training for data analytics skills, and encouraging innovative thinking through data-driven problem-solving strategies. The research offers a foundation for further investigations into industry-specific nuances and external factors influencing the collaboration between data analytics and innovative leadership.

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Section: Data Visualization and Communication Skillsmentioning

confidence: 99%

Critical Analysis of Innovative Leadership through Effective Data Analytics: Exploring Trends in Business Analysis, Finance, Marketing, and Information Technology

Adigwe,

Abalaka,

Olaniyi

et al. 2023

AJEBA

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“…Veri görselleştirme inovasyon ve keşfe yol açan ve bilimsel süreci kolaylaştırarak yeni araştırma yolları sağlayan dinamik bir araç olarak değerlendirilmektedir (Gandhi, 2020).…”

Section: Görselleştirmeunclassified

Sürdürülebilir Kalkınma Hedefleri Bağlamında Turizm Uygulamalarının Değerlendirilmesi

Bellikli,

Yalın

2023

Turizm Alanında Çok Yönlü Araştırmalar II

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Birleşmiş Milletler Genel Kurulu tarafından belirlenen 2030 Gündemi kapsamında yer alan sürdürülebilir kalkınma hedeflerine ulaşmada, belirlenen sürenin yaklaşması ve konunun önemi nedeniyle uluslararası kuruluşların ve ülkelerin gösterdiği hassasiyet artarak devam etmektedir. Turizm ise dünya çapında sadece ekonomik değil, sosyal ve çevresel olarak da önemli etkiye sahip bir sektördür. Bu bağlamda sürdürülebilir kalkınma hedeflerine ulaşmada turizm büyük bir potansiyele sahiptir. Sadece uluslararası örgütlerin konu hakkındaki faaliyetleri yeterli olmayıp; devletlerin, kamu kurum ve kuruluşlarının, sivil toplum kuruluşlarının, turizm birliklerinin ve turizm işletmelerinin katılımı önem arz etmektedir.

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“…Data visualization deals with developing, designing, and applying graphical representations of data to make it easier to make sense of the data. Data visualization is also known as scientific or information visualization [11]. Using images, graphs, charts, and maps to understand data and information has been around for centuries.…”

Section: Data Visualizationmentioning

confidence: 99%

“…Use descriptive data visualization when a lot of data is coming back, but you know what it is. Both categories are helpful for visually representing data [11].…”

Section: Data Visualizationmentioning

confidence: 99%

Revealing Insights Through Exploratory Data Analysis on Earthquake Dataset

Arsyad,

Yunita,

Krismartopo

et al. 2023

JSIS

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Exploratory Data Analysis (EDA) is a critical approach in developing machine learning models because the goal is to summarize the main characteristics of the data, often with visual methods, before modeling. It is frequently used as a prerequisite for more advanced data analytics techniques. Earthquakes are one of the natural disasters that commonly happen worldwide and lead to many victims. Research on machine learning for predicting earthquakes has been conducted a lot in recent years. This is a preliminary study for understanding an earthquake dataset to reveal several insights. This study aims to perform EDA using a dataset available on Kaggle, the Earthquake dataset from 1965 until 2016. Using several libraries in Python for data visualization and correlation analysis, this study results that depth does not correlate with magnitude, and the most frequent earthquake happened in 2011. Recommendations for further research are to cluster the dataset using clustering algorithms, such as K-means and hierarchical clustering, and then classify using several classifier algorithms.

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Data Visualization Techniques: Traditional Data to Big Data

Cited by 8 publications

References 0 publications

Critical Analysis of Innovative Leadership through Effective Data Analytics: Exploring Trends in Business Analysis, Finance, Marketing, and Information Technology

Critical Analysis of Innovative Leadership through Effective Data Analytics: Exploring Trends in Business Analysis, Finance, Marketing, and Information Technology

Sürdürülebilir Kalkınma Hedefleri Bağlamında Turizm Uygulamalarının Değerlendirilmesi

Revealing Insights Through Exploratory Data Analysis on Earthquake Dataset

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