Integrating Big Data Analytics into Advertising Curriculum

2016 ◽  
pp. 131-152 ◽  
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The World ◽  
Challenges And Opportunities ◽  
The Status ◽  
Consumer Data ◽  
Tools And Techniques ◽  
Data Analytic ◽  
Collection Methods

With the assistance of new computing technologies and consumer data collection methods, advertising professionals are capable of generating better targeted advertising campaigns. Big Data analytics are particularly worth noticing and have presented ample opportunities for advertising researchers and practitioners around the world. Although Big Data analytic courses have been offered at major universities, existing advertising curricula have yet to address the opportunities and challenges offered by Big Data. This chapter collects curricular data from major universities around the world to examine what Big Data has posed challenges and opportunities to existing advertising curricula in an international context. Curricula of 186 universities around the world are reviewed to describe the status of integrating these developments into better preparing advertising students for these changes. Findings show that only selected advertising programs in the U.S. have begun to explore the potential of the data analytics tools and techniques. Practical and educational implications are discussed.

Integrating Big Data Analytics into Advertising Curriculum

2017 ◽  
pp. 228-250
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The World ◽  
Challenges And Opportunities ◽  
The Status ◽  
Consumer Data ◽  
Tools And Techniques ◽  
Data Analytic ◽  
Collection Methods

With the assistance of new computing technologies and consumer data collection methods, advertising professionals are capable of generating better targeted advertising campaigns. Big Data analytics are particularly worth noticing and have presented ample opportunities for advertising researchers and practitioners around the world. Although Big Data analytic courses have been offered at major universities, existing advertising curricula have yet to address the opportunities and challenges offered by Big Data. This chapter collects curricular data from major universities around the world to examine what Big Data has posed challenges and opportunities to existing advertising curricula in an international context. Curricula of 186 universities around the world are reviewed to describe the status of integrating these developments into better preparing advertising students for these changes. Findings show that only selected advertising programs in the U.S. have begun to explore the potential of the data analytics tools and techniques. Practical and educational implications are discussed.

Big Data Analytics in Undergraduate Advertising Curricula

2022 ◽  
pp. 1958-1973
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Past Research ◽  
Data Driven ◽  
The World ◽  
Tools And Techniques ◽  
Personalized Advertising

The rapid ascent of data-driven advertising practices has allowed advertising professionals to develop highly-targeted and personalized advertising campaigns. The success of data-driven advertising relies on if future professionals are proficient with basics of Big Data analytics. However, past research of undergraduate advertising curricula around the world has shown that higher education institutions tend to fall behind in offering the most up-to-dated training for advertising students. Findings have shown that undergraduate advertising programs have slowly taken advantage of the potential of the data analytics tools and techniques. This trend is observed among higher education institutions around the world. Practical, research, and pedagogical implications are discussed.

Big Data Analytics in Undergraduate Advertising Curricula

2018 ◽  
Vol 8 (1) ◽  
pp. 34-47
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Past Research ◽  
Data Driven ◽  
The World ◽  
Tools And Techniques ◽  
Personalized Advertising

The rapid ascent of data-driven advertising practices has allowed advertising professionals to develop highly-targeted and personalized advertising campaigns. The success of data-driven advertising relies on if future professionals are proficient with basics of Big Data analytics. However, past research of undergraduate advertising curricula around the world has shown that higher education institutions tend to fall behind in offering the most up-to-dated training for advertising students. Findings have shown that undergraduate advertising programs have slowly taken advantage of the potential of the data analytics tools and techniques. This trend is observed among higher education institutions around the world. Practical, research, and pedagogical implications are discussed.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

DATA VISUALIZATION OF COVID-19 DATA USING TABLEAU

2021 ◽  
Vol 1 (10) ◽  
pp. 308-315
Author(s):  
Nirmit Singhal ◽  
Amita Goel, ◽  
Nidhi Sengar ◽  
Vasudha Bahl
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Information Sources ◽  
Big Data Analytics ◽  
Data Sources ◽  
Visualization Technique ◽  
Global Pandemic ◽  
The World ◽  
Drop Interface

The world generated 52 times the amount of data in 2010 and 76 times the number of information sources in 2022. The ability to use this data creates enormous opportunities, and in order to make these opportunities a reality, people must use data to solve problems. Unfortunately, in the midst of a global pandemic, when people all over the world seek reliable, trustworthy information about COVID-19 (Coronavirus). Tableau plays a key role in this scenario because it is an extremely powerful tool for quickly visualizing large amounts of data. It has a simple drag-and-drop interface. Beautiful infographics are simple to create and take little time. Tableau works with a wide variety of data sources. COVID-19 (Coronavirus)analytics with Tableau will allow you to create dashboards that will assist you. Tableau is a tool that deals with big data analytics and generates output in a visualization technique, making it more understandable and presentable. Data blending, real-time reporting, and data collaboration are one of its features. Ultimately, this paper provides a clear picture of the growing COVID19 (Coronavirus) data and the tools that can assist more effectively, accurately, and efficiently. Keywords: Data Visualization, Tableau, Data Analysis, Covid-19 analysis, Covid-19 data

The Compute Infrastructures for Big Data Analytics

2014 ◽  
pp. 74-109 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Steady Increase ◽  
Data Generation ◽  
Abnormal Growth ◽  
The World ◽  
Tremendous Amount ◽  
And Storage

The implications of the digitization process among a bevy of trends are definitely many and memorable. One is the abnormal growth in data generation, gathering, and storage due to a steady increase in the number of data sources, structures, scopes, sizes, and speeds. In this chapter, the author shows some of the impactful developments brewing in the IT space, how the tremendous amount of data getting produced and processed all over the world impacts the IT and business domains, how next-generation IT infrastructures are accordingly getting refactored, remedied, and readied for the impending big data-induced challenges, how likely the move of the big data analytics discipline towards fulfilling the digital universe requirements of extracting and extrapolating actionable insights for the knowledge-parched is, and finally, the establishment and sustenance of the dreamt smarter planet.

Big Data Analytics

2019 ◽  
pp. 67-81
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Big Data ◽  
Open Problem ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Capture ◽  
Data Sets ◽  
Full Potential ◽  
Processing Application ◽  
Nosql Databases ◽  
The World

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

Introduction to Use and Analysis of Big Data in E-Learning

2019 ◽  
pp. 75-87
Author(s):  
Renuka Mahajan
Keyword(s):  
Big Data ◽  
Big Data Analysis ◽  
Vital Role ◽  
Interaction Data ◽  
The World ◽  
Learner Interaction ◽  
E Learning ◽  
One Year ◽  
Tools And Techniques ◽  
Data Analytic

In today's world everything is connected and is either consuming data or generating data. The world is changing so fast that even one-year-old data may not be useful, and hence, big data analysis plays a very vital role for higher management of any organizations for decision making. Data warehousing helps in gathering and storing verifiable information into a single entity. Data can be of different types like speech, text, etc. It can be structured or unstructured. Each data point is characterized in terms of volume or variety. This chapter gives an overview of how to utilize the learner interaction data from a particular website and how patterns can be captured by analyzing learner interaction data with big data analytic tools. Big data has risen in the field of education and has many challenges like storage, combining, analysis, and scalability of big data. It covers tools and techniques that can be used. The results from this study will have implications for new learners to the e-learning website, website designers, and academicians.

Big Data Applications in Healthcare

Big Data ◽  
2016 ◽  
pp. 1247-1259 ◽  
Author(s):  
Jayanthi Ranjan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Big Data Applications ◽  
Business Functions ◽  
Automated Algorithms ◽  
Almost All ◽  
Tools And Techniques ◽  
Research Centres

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.

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