Business Analytics for Effective Decision Making / edited by K. V. Sumi and R. Vasanthagopal.

Format:

Book

Contributor:

Sumi, K. V., editor.

Vasanthagopal, R., editor.

Language:

English

Subjects (All):

Industrial management--Statistical methods.

Industrial management.

Physical Description:

1 online resource (152 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2024.

Summary:

Business Analytics for Effective Decision Making is a comprehensive reference that explores the role of business analytics in driving informed decision-making. The book begins with an introduction to business analytics, highlighting its significance in today's dynamic business landscape. The subsequent chapters review various tools and software available for data analytics, addressing both the opportunities and challenges for professionals in different sectors. Readers will find practical insights and real-world case studies across diverse industries, including banking, retail, marketing, and supply chain management. Each chapter provides actionable insights and concludes with implications for implementing data-driven strategies. Key Features: Practical Examples: Real-world case studies and examples make complex concepts easy to understand.Ethical Considerations: Guidance on responsible data usage and addressing ethical implications.Comprehensive Coverage: From data collection to analysis and interpretation, the book covers all aspects of business analytics.Diverse Perspectives: Contributions from industry experts offer diverse insights into data analytics applications in business research, marketing, supply chain and the retail industry.Actionable Insights: Each chapter concludes with practical implications for implementing data-driven strategies.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

List of Contributors

Introduction to Business Analytics for Effective Decision Making

Sumi K.V.1,* and R. Vasanthagopal1,*

DATA ANALYTICS

Privacy

Fairness

Transparency

Structured Data

Unstructured Data

Big Data

TOOLS FOR DATA ANALYTICS

Statistical Software

Machine Learning Software

Business Intelligence Software

The Different Challenges and Limitations of Data Analytics

Data Quality

Data Bias

Interpreting Results

Cost

BUSINESS ANALYTICS

Improved Decision-making

Increased Efficiency

Enhanced Customer Insights

Reduced Risk

Improved Compliance

KEY STEPS INVOLVED IN BUSINESS ANALYTICS FOR EFFECTIVE DECISION MAKING

Collect the Data

Clean and Prepare the Data

Analyze the Data

Communicate the Results

THIS BOOK PRESENTS A COLLECTION OF PAPERS THAT ILLUSTRATE THE USE OF DATA ANALYTICS IN DIFFERENT FIELDS. THE PAPERS COVER A VARIETY OF TOPICS, INCLUDING

Data Mining in Banks

Value at Risk and Conditional Value at Risk

Relevance of Big Data Analytics in Banking Sector

Performance Appraisal and Organizational Outcome

HR Analytics and its Implications in Organizations

Stress Management Among Women Police Officers

Marketing Analytics in Business

Impact of Data Analytics in Retail Industry

Emerging Landscape in Business Analytics Technologies

A Study on Supply Chain Management Practices of Seafood Industries in Kerala

Gamut of Data Mining Incidental to Fraud Detection in the Era of Digital Banking

PROS AND CONS OF THE METHODS USED IN THE PAPERS

ARIMA Model on GST - A Predictive Analysis

Value at Risk and Conditional Value at Risk in The Risk Management of Indian Stock Portfolios.

Relevance of Big Data Analytics in the Banking Sector

Performance Appraisal and Organizational Outcome via the Mediating Effect of Relationship with Peer Group and Subordinates - A Tool for HR Analytics

Stress Management Among the Women Police Officers with Special Reference to Kannur District

Gamut of Data Mining Incidental to Fraud Detection in the era of Digital Banking

Overall, The Methods Used in These Papers have Both Pros and Cons

THE PRACTICAL/THEORETICAL IMPLICATIONS OF THE CHAPTERS

Value at Risk and Conditional Value at Risk in The Risk Management of Indian Stock Portfolios

Stress Management Among Women Police Officers with Special Reference to Kannur District

Data Mining in Banks: A Bibliometric Analysis

Kavya Shabu1 and R. Vasanthagopal2,*

INTRODUCTION

Research Questions

RQ1

RQ2

RQ3

RQ4

RQ5

RQ6

RQ7

RQ8

Research Methodology

RESULTS AND DISCUSSION

Overview

Annual Scientific Production &amp

Average Citation Per Year

RQ1.

Three-Field Plot Analysis

Source Clustering through Bradford's Law

Source Impact

Thematic Map and Thematic Evolution

Clustering Network

CONCLUSION

REFERENCES

Value at Risk and Conditional Value at Risk in the Risk Management of Indian Stock Portfolios

Relevance of Big Data Analytics in the Banking Sector

Sumi K.V.1,*

Classification of Analytics

Big Data in Banking

Impact of Big Data

Advantages of Big Data Analytics for the Banking Sector

Assessment of Attitude and Reaction

Effective Customer Feedback Analysis

Purchase Patterns of Customers

Data Management and Fraud Risk Assessment

Big Data Analytics Challenges

Security Issues

Regulatory Specifications

Intense Regulatory Requirements

Maintaining Data Quality

Data Analytics to Manage Risks in Banks

Performance Appraisal and Organizational Outcome via the Mediating Effect of Relationship with Peer Group and Subordinates-A Tool for HR Analytic

S. Jayadev and R. Sumitha1,*

Peer Appraisal

Forced Choice Method

Rating Scale

Forced Distribution Method

Behaviorally Anchored Rating Scale

Critical Incident

Human Resource Accounting

Psychological Approach

MBO Approach

360-degree Appraisal

Assessment Centre Approach

Paired Comparison Method

Stress Management Among Women Police Officers With Special Reference to Kannur District

Vigi V. Nair1,* and Madhusoodanan Kartha N.V.2

RESEARCH PROBLEM

SIGNIFICANCE OF THE STUDY

SCOPE OF THE STUDY

LITERATURE REVIEW

OBJECTIVES OF THE STUDY

HYPOTHESIS OF THE STUDY

RESEARCH METHODOLOGY

Research Design

Sampling Design

Data Collection Methods

Statistical Tools used for the Study.

LIMITATIONS OF THE STUDY

ANALYSIS &amp

INTERPRETATION

Analysis I

H0

Interpretation

Analysis II

Analysis III

Factors Influencing Stress.

Standardized Canonical Discriminant Function Coefficients

EFFECTS OF STRESS

FINDINGS AND SUGGESTIONS

SUGGESTIONS

Marketing Analytics in Business: Emerging Opportunities and Challenges

Aswani Thampi P.R.1,* and Ambeesh Mon. S.1

OBJECTIVES

REVIEW OF LITERATURE

ANALYSIS AND RESULTS

Opportunities

Understanding and Identifying Target Consumers

Trend in Markets

Personalized Messages

Analyzing the Competition

Marketing-related Decision Making

Analyse Social Media Engagement

Measuring the Marketing Performance

Marketing and Optimization for Search Engines

Challenges

Data Boom and Data Usage

Overreliance on Data

Fast Changing Trends

Trustworthiness of Data

Skill Shortage

Identify the Best Tool

Typical Examples of Applications of Marketing Analytics in a Business

Use of Marketing Analytics to Improve the Website

Use of Marketing Analytics to make Content Recommendations

Use of Marketing Analytics for Gaining Customer Insights

Danileo Jose1,*

RETAIL MARKETING

Types of Retail Marketing

Store Based Retail Marketing

Non-Store Based Retail Marketing

Digital Marketing

Data Analytics

Data Analytics in Business

Retail Data Analytics

How do Retailers Collect Data?

How Data Analytics is Transforming Retail Industry

Forecasting Demand in Retail

Personalizing Customer Experience

Predicting Spending

Customer Journey Analytics.

Use of Data Analytics in Multiple Retail Chain

Benefits of Data Analytics in Retail Marketing

Challenges for Data Analytics in the Retail Industry

D. Mavoothu1,*

HISTORY OF ANALYTICS

ANALYTICS AND DECISION-MAKING IN BUSINESS

Descriptive Analytics

Business Analytics in the Past and Present: An Overview

The Changing Landscape of Business Analytics Technologies

Embedded Analytics

Hybrid Data Architecture

Containerization

Data Fabric

IoT

Blockchain

5G

Connected Cloud

Challenges of Changing Landscape of Analytics Technologies

Data Management

Data Integration

Quickness

Customisation

Right Data

Actionable Insights

Unused Data

Suggestions

Investment

Data Leverage

Best Practices

Combine Strategy and Technology

Enhance Financial Returns

Data-Savvy Teams

Data Governance and Compliance

Data Security

S. Geetha1,* and Sanal S.1,*

SCOPE AND SIGNIFICANCE OF THE STUDY

STATEMENT OF THE PROBLEM

HYPOTHESES

H1

SUPPLY CHAIN MANAGEMENT PRACTICES

Data Analysis X2 Test

Null Hypothesis

Interference

Findings

Recommendation/Suggestion

Subject Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

981-5238-36-1

OCLC:

1457638174