Business Analytics for Effective Decision Making E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
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.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 206
Veröffentlichungsjahr: 2024
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PREFACE
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:
ARMA Model on GST – A Predictive Analysis: This chapter explains the use of an ARMA model to predict the future revenue of the GST in India.Data Mining in Banks: This chapter provides a bibliometric analysis of the literature on data mining in banks.Value At Risk and Conditional Value at Risk in The Risk Management of Indian Stock Portfolios: This chapter compares the performance of value at risk (VaR) and conditional value at risk (CVaR) in the risk management of Indian stock portfolios.Relevance of Big Data Analytics in the Banking Sector: This chapter discusses the 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: This chapter examines the relationship between performance appraisal and organizational outcome.HR Analytics and its Implications in Organizations: This chapter discusses the implications of HR analytics for organizations.Stress Management among the Women Police Officers with Special Reference to Kannur District: This chapter examines the stress levels of women police officers in Kannur district, India.Marketing Analytics in Business: Emerging Opportunities and Challenges: This chapter discusses the emerging opportunities and challenges in marketing analytics.Impact of Data Analytics on Retail Industry: This chapter discusses the impact of data analytics on the retail industry.Emerging Landscape in Business Analytics Technologies: This chapter discusses the emerging landscape in business analytics technologies.A Study on Supply Chain Management Practices of Seafood Industries in Kerala: This chapter examines the supply chain management practices of seafood industries in Kerala, India.Gamut of Data Mining Incidental to Fraud Detection in the Era of Digital Banking: This chapter discusses the gamut of data mining techniques that can be used for fraud detection in digital banking.The papers in this book are all written by experts in their field, providing a wealth of information about the use of data analytics in different industries. The book is a valuable resource for anyone who is interested in learning more about data analytics and how it can be used to improve decision making.
List of Contributors
Introduction to Business Analytics for Effective Decision Making
Sumi K.V.1,*,R. Vasanthagopal1,*DATA ANALYTICS
Data analytics is the process of collecting, cleaning, analyzing, and interpreting data to gain insights that can be used to make better decisions. It is a broad field that encompasses a variety of techniques, from simple statistical analysis to complex machine learning algorithms. The goal of data analytics is to extract knowledge from data so that it can be used to make better decisions. Data ethics has an important role in data analytics. Data analytics can be a powerful tool, but it can also be used for some illegal activities. Therefore, it is important to be aware of the ethical implications of data analytics and to use it in a responsible way. Some of the key ethical considerations for data analytics include:
Privacy
Data analytics often involves collecting and using personal data. It is important to respect people's privacy and to only use their data in ways that they would be comfortable with.
Fairness
Data analytics can be used to make decisions that affect people's lives. It is important to use data analytics in a fair and equitable way, and to avoid discrimination.
Transparency
People should have the right to know how their data is being used and to have a say in how it is used. It is important to be transparent about the use of data analytics and to give people the opportunity to opt out data collection and analysis. The Different Types of Data can be used for Data Analytics.
Data analytics can be used with a variety of data types, including:
Structured Data
Structured data is data that is organized in a consistent format, such as a table or spreadsheet. This type of data is often used for data analytics because it is easy to process and analyze.
Unstructured Data
Unstructured data is data that is not organized in a consistent format, such as text, audio, and video. This type of data can be more difficult to process and analyze, but it can also be more valuable because it can provide insights that structured data cannot.
Big Data
Big data is data that is so large and complex that it cannot be processed using traditional data processing techniques. Big data analytics is a field of study that focuses on developing new techniques for processing and analyzing big data.
TOOLS FOR DATA ANALYTICS
There are different tools and software that can be used for data analytics. There are a variety of tools and software that can be used for data analytics, including:
Statistical Software
Statistical software can be used to perform basic statistical analysis, such as descriptive statistics and hypothesis testing.
Machine Learning Software
Machine learning software can be used to develop models that can make predictions or classify data.
Business Intelligence Software
Business intelligence software can be used to visualize data and to create dashboards that track key performance indicators.
The Different Challenges and Limitations of Data Analytics
Data analytics is a powerful tool, but it is not without its challenges and limitations. Some of the challenges and limitations of data analytics include:
Data Quality
Data quality is essential for data analytics. If the data is not accurate or complete, the results of the analysis will be unreliable.
Data Bias
Data bias can occur when the data is not representative of the population that it is supposed to represent. This can lead to inaccurate or misleading results.
Interpreting Results
The results of data analytics can be complex and difficult to interpret. It is important to have the expertise to interpret the results correctly and to communicate them effectively to others.
Cost
Data analytics can be expensive, especially if it involves the use of big data or machine learning
BUSINESS ANALYTICS
Business analytics is the process of collecting, analyzing, and interpreting data to make better business decisions. It involves using data from a variety of sources, including internal data, external data, and social data, to identify trends, patterns, and relationships. Business analytics can be used to improve all aspects of a business, from marketing and sales to operations and finance.
Here are some of the benefits of using business analytics for effective decision making:
Improved Decision-making
Business analytics can help businesses make better decisions by providing them with insights into their data. This can help businesses to identify opportunities, mitigate risks, and improve their bottom line.
Increased Efficiency
Business analytics can help businesses to improve their efficiency by identifying areas where they can optimize their processes. This can lead to cost savings and improved customer service.
Enhanced Customer Insights
Business analytics can help businesses to gain a better understanding of their customers. This can help businesses to target their marketing and sales efforts more effectively and improve the customer experience.
Reduced Risk
Business analytics can help businesses to identify and mitigate risks. This can help businesses to avoid costly mistakes and protect their bottom line.
Improved Compliance
Business analytics can help businesses to comply with regulations. This can help businesses to avoid fines and penalties.
Key steps involved in business analytics for effective decision making
Define the problem: The first step is to define the problem that you are trying to solve. What are you hoping to achieve by using business analytics?
Collect the Data
Once you have defined the problem, you need to collect the data that you need to solve it. This data can come from a variety of sources, including internal data, external data, and social data.
Clean and Prepare the Data
Once you have collected the data, you need to clean and prepare it for analysis. This involves removing any errors or inconsistencies in the data.
Analyze the Data
The next step is to analyze the data using statistical and machine learning techniques. This will help you to identify trends, patterns, and relationships in the data.
Communicate the Results
Once you have analyzed the data, you need to communicate the results to the decision-makers. This involves presenting the results in a way that is easy to understand and actionable.
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
This paper uses data mining techniques to identify patterns in customer behavior. These patterns can then be used to calculate the value at risk, which is a measure of the potential loss that a bank could face.
Value at Risk and Conditional Value at Risk
This paper discusses two different methods for calculating the risk of a financial portfolio. Value at risk (VaR) is a measure of the maximum loss that a portfolio is expected to experience over a given time period with a given confidence level. Conditional value at risk (CVaR) is a measure of the expected loss that a portfolio will experience after it has already experienced a loss of a certain magnitude.
Relevance of Big Data Analytics in Banking Sector
This paper discusses the importance of big data analytics in the banking sector. Big data analytics can be used to improve a bank's risk management, customer service, and fraud detection capabilities.
Performance Appraisal and Organizational Outcome
This paper uses data analytics to examine the relationship between performance appraisal and organizational outcome. The paper finds that performance appraisal can have a positive impact on organizational outcome, but only if it is properly designed and implemented.
HR Analytics and its Implications in Organizations
This paper discusses the use of HR analytics in organizations. HR analytics can be used to improve a company's workforce planning, talent management, and compensation decisions.
Stress Management Among Women Police Officers
This paper uses data analytics to examine the factors that contribute to stress among women police officers. The paper finds that women police officers are more likely to experience stress than men police officers, and that this stress can have a negative impact on their physical and mental health.
Marketing Analytics in Business
This paper discusses the use of marketing analytics in business. Marketing analytics can be used to improve a company's marketing campaigns, product development, and customer segmentation.
Impact of Data Analytics in Retail Industry
This paper discusses the impact of data analytics on the retail industry. Data analytics can be used to improve a retailer's inventory management, pricing, and customer experience.
Emerging Landscape in Business Analytics Technologies
This paper discusses the emerging trends in business analytics technologies. These trends include the use of artificial intelligence, machine learning, and cloud computing.
A Study on Supply Chain Management Practices of Seafood Industries in Kerala
This paper studies the supply chain management practices of seafood industries in Kerala. The paper finds that seafood industries in Kerala can improve their supply chain efficiency by using data analytics.
