Business Analytics for Effective Decision Making

The COVID-19 pandemic has inflicted a multi-sectoral impact on omic activities around the globe and is considered as the worst economic fallout since the Great Depression. Demand and supply disruptions have acutely affected trade and commerce. A downfall of global trade has been pointed out by WTO. Indirect taxes constitute an important source of development funds for developing economies. The implementation of GST has brought landmark gains in consolidating indirect taxes and thereby reducing the cascading effect of taxes. GST revenues have fluctuated during the pandemic period. Considering the significance of the revenue to the economy, an attempt has been made to predict GST revenue using ARIMA analysis. The prediction results indicate an optimistic growth in GST revenues which along with the efforts of the Government towards liberalization of GST norms and revision of tax slabs is expected to provide the economy with the essential boost to revive and revitalize the Indian economy. 

Overview: Banking is the core influencing unit of the economy and over the years, the banking system has evolved from digitalization to the management information system, enabling complex blockchain technology and fintech to take the lead. Data mining is a process within the management information system that enables the banks in decision-making. Data warehousing and data mining are now common processes in every industry to efficiently implement customer relationship management.

Purpose: The purpose of the study is to analyse the trend, author origin, keywords(DE) and relevant sources to this field. This is done through the identification of best sources of journals based on Bradford’s Law and source citation index, relevant keywords according to thematic mapping, thematic evolution and also analyzing clusters through network and overlay visualization.

Approach of the Study: The study is conducted by accumulating data from the database SCOPUS and 366 records were found for further analysis after the application of required filters. The extracted file was further run in Biblioshiny and Vosviewer for analysis.

Paper Type: Analytical Paper.

Successful investment involves maximizing rewards while minimizing risk. Investors and traders consider risk while making investment decisions, which is often the deciding element in accepting or rejecting an asset or security. The study focuses on risk management in Indian stock portfolios and VaR and CVaR models for risk valuation. The study compares VaR and CvaR valuations on different stock portfolios. This work provides more details on stock portfolio risk blended with different industries. The VaR model framework helps determine the entity's loss potential and the likelihood of the defined loss. The financial sector is the leading sector in stock portfolio returns, and Value at Risk and Conditional Value at Risk values for the financial sector stock portfolio indicate a high level of risk. 

As the need for real-time data availability and reporting capabilities grew over the following several decades, increasingly sophisticated database standards and applications were created. These developments have recently started to accelerate due to the rising use of advanced analytics and data visualisation in recent years. The process of extracting hidden insights from vast amounts of organised and unstructured data known as data science now makes use of highly developed technology such as data mining, machine learning, and advanced analytics. Big data is no exception to the banking industry's history of being an early user of new technologies. Big Data is a term used to describe an expanding body of data that is both structured and unstructured and is present in a variety of formats. Volume, velocity, variety, value, and truthfulness are this technology's key characteristics.

The human resources industry has undergone significant transformation as a result of the advent of HR analytics. Managers are now able to make accurate databased decisions leading to better performance. Organizations utilize HR analytics to examine employee turnover, utilize talents effectively, gather information for decisionmaking, etc. HR analytics can also boost performance, productivity, and profitability when they are linked with corporate strategy. Performance Appraisal is a continuous system of evaluating employees based on their set goals. HR analytics is very useful during the performance evaluation cycle as it assists employers in identifying performance gaps and closing them using the data available. Equally important in the appraisal process are the peer group and subordinates, whose inputs in the form of data help assess and improve performance. These variables may be tracked and shared with management and staff to help everyone perform better and be more productive. In this context, the study aims to explore the mediating effect of peer groups and subordinates in the performance appraisal process and how it contributes to the organizational outcome of selected IT Companies in Kerala.

Law enforcement is a profession with a few unusual traits that can cause stress, and policing is one of the maximum demanding and stressful jobs amongst them. The police employees are the few experts wherein people are expected to stand risks and, if necessary, to treat their lives in addition facing high stress in lots of different aspects. Police personnel go through tremendous occupational physical and mental stress. Women law enforcement officials are steadily growing in numbers within the country and they're believed to revel in greater stress than their male counterparts and additionally operating in regulation enforcement. Studies have shown that working hours, education, age and the relationship among co-workers and supervisors have been the aspects that led them to experience stress. Stress affects the overall performance of men or women in this sector. The study deals with the analysis of stress management among women police officers with special reference to Kannur District.

In today’s modern marketing scenario, a greater understanding of the consumer’s mind and user behavior is vital in positioning a product or service. Personalized ads that speak to the buyer’s specific needs and interests are inevitable for brands to catch individual attention. With the advent of more advanced analytical tools and approaches in recent years, companies and marketers no longer need to guess buyer behavior patterns or their product preferences. Marketers can also better understand current marketing trends, determine which programs worked and why, evaluate and monitor trends over time, assess the market, predict future results, etc. Marketing analytics helps the business to make decisions on everything, and gives the business leaders significant new decision-making power. Insights gleaned from marketing analytics can enable organizations to improve customer experiences, increase the return on investment of marketing efforts, and craft future marketing strategies. Even though, marketing analytics is a crucial element in business and provides several opportunities, most of the marketers face multiple marketing analytics challenges that restrict them from using the available data to the best potential. The key here is to identify and understand these challenges. This research paper aims to identify the various opportunities and challenges associated with marketing analytics in business. Thereby assessing its importance in today’s competitive business landscape.

In this era, retail market has grown to an extreme level where satisfying a customer’s need is of utmost importance for the survival of a retailer in the market as in the business. Hence, managing and processing the data to meet the customer’s expectation is very much crucial in order to run their business, promote growth as well profit-making. As the retail sector is in the fast run and for serving faster to the customers and clients, every business sector makes use of the big data, more specifically data analytics for almost every part of the retail activities such as tracking the trending products, monitoring the inventory level forecasting future sales, forecasting future demand and many such activities. Also it helps the retailers in working on how to promote themselves or their product to their targeted customers, what are the purchasing patterns of the buyers, what to sell next to their targeted customers by evaluating the customer behaviour as well their interests.

This review paper tries to capture the emerging landscape in business analytics technologies with the main focus being to identify the potential challenges due to this emergence. With the projected global data creation of 170 ZB by 2025, with enterprises comprising around 60% of that 170 ZB total, companies need weighty analytics technologies to maneuver them. Cloud, data warehouses, big data, and new software/hardware developments have intensified the unfolding of analytics. Analytics as such has not changed much, but analytical technology has undergone a lot of changes. New and emerging analytics technologies include hybrid data architecture, containerization, data fabric, IoT, blockchain, connected cloud, etc. Business analytics innovative technologies bring new opportunities and challenges, but not without net gain.

Kerala contributes close to one-fifth of the total marine fish landed in India. The marine fish landing in Kerala during the year 2020-21 was 3.91 lakh tonnes (4.75 lakh tonnes in 2019-20) and the export of marine products from Kerala during the year 2020-21 was 1,57,698 MT (1,63,563 MT in 2019-20). Organizations are faced with an array of challenges as they strive to compete in today’s dynamic global markets. To remain competitive, organizations must recognize the importance of supply chain practices that improve not only their own performance but also coordinate with their channel partners to improve their joint performance. Effective and efficient supply chain management practices now have become a very valuable and important way to remain competitive in the market and to improve organizational performance. A successful supply chain will effectively coordinate its processes, focus on delivering quality products, eliminate unnecessary costs in key functional areas, proper waste management, and create a performance measurement system that provides data on whether the supply chain is performing up to expectations. The significance of this study is first to study the extent to which the seafood industries in Kerala implement supply chain management practices. Second, to identify various factors that contribute to supply chain management practices, competitive advantage, and organizational performance.

Rivals in the modern fintech space are fighting legacy banks from all sides and gradually dismantling the protective walls that have been up over the years. The banks and broader financial sector must deal with this fledgling innovation in digital banking as well as difficulties connected to payments, cash management, lending, and investment management. Credit cards, peer-to-peer lending networks, real-time payment systems, digital wallets, challenger banks, etc. are examples of such innovations in digital banking. At present new entrants to the banking ecosystem have a greater degree of independence. Due to seamless integration, mobile connectivity, data availability, trust-based transactions, cloud-physical infrastructure, scaling up the business, etc. have improved and the cost of acquiring and servicing clients has reduced. People around the world are frequently travelling, and making purchases than at any other time in the past. However, the shift of banking to digital channels has resulted in a revolution in financial fraud. In the current era, digital banking fraud is a big worldwide industry, where highly competent criminal gangs use ever-moreadvanced and ever-sophisticated technology. They regularly collaborate with dishonest bank personnel to steal substantial sums of money. Data mining, artificial intelligence, and machine learning are being used to protect clients and the digitalized banking system against scammers and financial fraud. This study explicitly shows the scope of emerging data mining techniques for fraud detection and prevention in the modern era of digitalized banking.