Big Data Applications in Industry 4.0

P. Kaliraj, T. Devi, P. Kaliraj, T. Devi

• Publisher: Auerbach Publications
• Year: 2022
• ISBN:
  9781000537673
• Edition: 1
• Topic: Business
• Subtopic: Industrial Management
• Index: Business

Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development.

Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems.

Features

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• An introduction to data science and the types of data analytics methods accessible today

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• An overview of data integration concepts, methodologies, and solutions

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• A general framework of forecasting principles and applications, as well as basic forecasting models including naïve, moving average, and exponential smoothing models

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• A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies

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• The application of Industry 4.0 and Big Data in the field of education

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• The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance

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• Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making

Chapter 1 Data Science and Its Applications

Paul Abraham¹ and Lakshminarayanan S.²

¹Doddabanaswadi, Bangalore, India

²Department of Computer Applications Bharathiar University, Coimbatore, India

DOI: 10.1201/9781003175889-1

Contents

1. 1.1 Introduction to Data Science
2. 1.1.1 Data Science: A Definition
3. 1.1.2 Data in the Business
4. 1.1.3 Types of Data Analytics
5. 1.1.4 Use Cases in the Business
6. 1.1.5 Data Analytics Process, Implementation and Measurement
7. 1.2 Data Science and Its Application in the Healthcare Industry
8. 1.2.1 Data Types Generated in the Healthcare Sector
9. 1.2.2 Analytics Use Cases in Healthcare
10. 1.2.3 Future and Challenges
11. 1.3 Data Science and Its Application in the Retail and Retail E-Commerce
12. 1.3.1 Data Types Generated in the Retail and Retail E-Commerce Sector
13. 1.3.2 Analytics Use Cases in Retail and Retail E-Commerce
14. 1.3.3 Future and Challenges
15. 1.4 Data Science and Its Application in the Banking, Financial Services and Insurance (BFSI) Sector
16. 1.4.1 Data Types Generated in the BFSI Sector
17. 1.4.2 Analytics Use Cases in BFSI
18. 1.4.3 Future and Challenges
19. 1.5 Statistical Methods and Analytics Techniques Used across Businesses
20. 1.6 Statistical Methods and Analytics Techniques Used in Sales and Marketing
21. 1.6.1 Data Types Generated in Sales and Marketing Function
22. 1.6.2 Statistical Methods and Analytical Techniques
23. 1.6.3 Future and Challenges
24. 1.7 Statistical Methods and Analytics Techniques Used in Supply Chain Management
25. 1.7.1 Data Types Used in the SCM
26. 1.7.2 Analytics Use Cases in SCM
27. 1.7.3 Future and Challenges
28. 1.8 Statistical Methods and Analytics Techniques Used in Human Resource Management
29. 1.8.1 Data Types Generated in Human Resource Management
30. 1.8.2 Analytics Use Cases in Human Resource Management
31. 1.8.3 Future and Challenges
32. References

This chapter starts with a brief introduction to data science and aims to cover three industry segments and three business functions, where and how data science is applied.

Objectives

The objective of this chapter is to introduce data science and discuss its applications in the business today. Data science is about solving business problems, and businesses must recognize this fact. It examines which questions need answers and where to find the related data to support business decisions. This chapter defines and introduces the field of data science, possible types of data available in the business today, the many types of data analytics methods available today and covers use cases through its application. Though data science is used in all walks of life, this chapter restricts only its text to the scope of business or commercial activity. Going a little deeper, this chapter aims to cover three industry segments and three business functions where data science is applied.

1.1 Introduction to Data Science

Businesses see an uprising in transactions, leading to creating a huge repository of data comprising these transactions. This creates a need for information, insight, and intelligence about the business. Managers in the businesses moved from making decisions out of experience or institution to fact-based, data-driven decisions. This was effectively done by understanding the business objectives and their operative nuances and building intelligence around them.

The last decade has seen a huge transformation in the businesses moving toward a digital era by automating their process flows. In this trend, most businesses have also been collecting and storing their data in digital formats, and now the time has come to analyze and bring some value from the collected data. The collected data now demands to be cleaned by removing noises or unwanted information before being processed (Foster Provost & Tm Fawcett, 2018) to bring out meaningful insights for the business. Significant advancements related to storage spaces, thereby reducing the hardware costs, faster processing, and software products capable of performing complex calculations have become a boon to the business wanting to have a data-driven culture for decision making.

1.1.1 Data Science: A Definition

The loose definition of data science is to analyze data of a business to be able to produce actionable insights and recommendations for the business (Affine Analytics, 2018). The simplicity or the complexity of the analysis also impacts the quality and accuracy of results. As businesses and the data they collect became sophisticated, the need for technological skills, math/stats skills, and the necessary business acumen to define and deliver a relevant business solution became more relevant.

Data science is the process of examining data sets to conclude the information they contain, increasingly with the aid of specialized systems and software, using techniques, scientific models, theories, and hypotheses. These three pillars have very much been the mainstay of data science ever since it started getting embraced by businesses over the past two decades and should continue to be even in the future (Figure 1.1): Computer Science & IT, Business Acumen and Methods, Models, & Process.

Figure 1.1 The Data Science model.

Data Science expressed like this in the above picture is an idea accepted in academia and industry. It’s an intersection of programming, analytical, and business skills that allows extracting meaningful insights from data to benefit business growth. However, this is used in social research, scientific & space programs, government planning, and so on, but this chapter will focus on its application in the Business Industry.

	DATA SCIENCE MODEL DEFINITION
➊	Business Acumen in its purest form means running a Business Enterprise. Any business existing to sell its product or services for a profit incurring some cost and generally having the functions like HR, Supply Chain, Finance, Sales & marketing to support it
➋	Methods, Models, Process are defined as industry and academia proved practices that are the backbone to Data Science, including Mathematical models, theorems, Statistical methods, techniques, and process methodologies likes CRISP-DM, Six-Sigma, Lean, and so on
➌	Computer Science & IT practice is the full range of hardware, the software involved in providing computing for processing data, storage for storing and sharing data and networking for collecting and movement.
➍	When Business Acumen or Knowledge and Models methods process come together, it’s classically called “traditional research.” It involves using data collected in the business to make dashboards and reports to understand the business, plan for its future and make corrections if needed.
➎	Businesses take help from the Computer Science IT practice to help run business by building applications, web services, websites or plan...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgments
• Editors
• Contributors
• 1 Data Science and Its Applications
• 2 Industry 4.0: Data and Data Integration
• 3 Forecasting Principles and Models: An Overview
• 4 Breaking Technology Barriers in Diabetes and Industry 4.0
• 5 Role of Big Data Analytics in Industrial Revolution 4.0
• 6 Big Data Infrastructure and Analytics for Education 4.0
• 7 Text Analytics in Big Data Environments
• 8 Business Data Analytics: Applications and Research Trends
• 9 Role of Big Data Analytics in the Financial Service Sector
• 10 Role of Big Data Analytics in the Education Domain
• 11 Social Media Analytics
• 12 Robust Statistics: Methods and Applications
• 13 Big Data in Tribal Healthcare and Biomedical Research
• 14 PySpark towards Data Analytics
• 15 How to Implement Data Lake for Large Enterprises
• 16 A Novel Application of Data Mining Techniques for Satellite Performance Analysis
• 17 Big Data Analytics: A Text Mining Perspective and Applications in Biomedicine and Healthcare