Analytics Enabled Decision Making

In the current business environment, which is greatly dynamic and competitive, business organizations are continually striving for expanding their competence and financial performance through improving almost every facet of their business––product/service quality, customer satisfaction, customer retention, productivity, line filling strategies, and others. In this sense, success and failure of organizations depend on the extent of precision of their decisions. Organizations are engaged with data to extract insights, identify trends and make decisions at different levels; and also, many of them learn how to utilize the power of data. Analytics can enable them to derive conclusions, make predictions, and ascertain actionable insights in a contextual and time-bound manner. It helps to examine data from multiple perspectives and gives visualizations by using different frameworks and platforms such as IBM Watson, Tableau, and R. The chapter presents the role of analytics in decision-making processes and assess the effectiveness of decisions upon their implementation, so the corrective measures can also be inserted. As decision making is a continuous business process, analytics accelerates it and gives organizations a pace to keep updated with changing business scenarios. Thus, this chapter presented a decision-making framework exhibiting how decision-making functions as an ongoing process. Different contexts and cases have been used to establish the relevance of each step of the framework.

This chapter introduces the role of algorithms as decision-makers in business. Algorithms will help several activities in the processes and production of the firms. Algorithms decrease the amount of human activities, decisions and delays with increasing the effectiveness of supply chains. At the same time, human decision-making power decreases and the content of work changes. Due to several platforms and the digitization of business, often the most important co-worker and boss is the platform, which gives advice where and what to do next. The role of the algorithm as a decision-maker is possible to understand in two ways: either they support decision making (decision support algorithms, DSA) or are directly decision-makers (decision-making algorithm, DMA). This dichotomy is important, but less used in the literature. This chapter not only emphasizes dichotomy but also provides an initiative to evaluate the share of DMA and DSA in algorithm-based decision making. This chapter is based on a literature review, which is completed with a short case description of algorithm-based business model of Wolt enterprise, a technology company known for its delivery platform for food and merchandise. Most of the analyzed literature is focused on ideas or experiments of algorithms that is upstream parts of algorithm-based products, not on the ready marketable algorithms.

Decision-making is apparently a core of business operations, and fundamentally responsible for the sustainability and success of organizations. Technological interventions have changed the way of business in the current highly competitive and uncertain business environment, where the accuracy of decisions is of great concern. Effective decision-making is dependent on high-quality data and requires swift access to data. Data warehouse helps to store huge amounts of data in a manner that improves business performance by providing rapid access to data with a high level of accuracy and relevance. Business intelligence (BI), with data warehousing, offers processes that provide great ease to make real-time decisions. BI without data warehousing couldn’t function. This chapter provides a fundamental understanding of data warehousing and BI to compel progressive transformation with Big Data analytics. The primary focus of Big Data analytics is on analysing and generating actionable insights for business organizations. The chapter explains how Big Data analytics influences BI processes.

This chapter fundamentally aims at the development of generalized framework encapsulating a wide range of dynamic utility functional and resultant latent choice models. The objectives are served by the application of well cherished exponential family of distributions capable of entertaining numerous probabilistic articulations through a single comprehensive and elegant expression. Moreover, the utility of the proposed scheme is further substantiated by delineating the working pedagogy in accordance with the rapidly embraced Bayesian paradigm. The legitimacy of the devised mechanism in the pursuit of optimal decision-making is advocated with respect to diverse experimental states. We entertained varying extent of worth parameters describing the preference ordering, different sample sizes and distinguished stochastic formations to inject the prior information or historic data in the demonstration of choice behaviors.

Drafted baseball players typically begin their professional baseball career with Minor League teams and are not guaranteed opportunities in the Major League. Accurate estimation of players’ likelihood to advance to the Major League debut can reduce the cost and increase value for both players and franchises. We mined both baseball performance stats and non-baseball data of players drafted from 2001 to 2010. We applied machine learning techniques to analyze and rank stats and data variables. We compared four sets of variable selections to train and validate our models, which predict the likelihood of a drafted player reaching the Majors. We fitted extreme gradient boosting, random forest, decision tree, and support vector machine to determine the high impact variables in the prediction. We successfully translated our model results into guidance for drafted players in the Minor League on what they should improve to increase their chances to play in the Major League.

Predictive analytics could also be defined as the application of statistical techniques and mathematical modeling to anticipate the future performance and expected return on investments. Predictive analytics examines the most recent and the historical data to see if the same pattern is likely to reoccur or not. This gives an opportunity to businessmen and financial investors to make an appropriate decision about their investments and expected returns. Ever since the development of ANN technique, researchers have tried to create a number of predictive models using ANN. The chapter is focused on defining predictive analytics and the tools used in predictive analytics, with a special orientation on Artificial Neural Networks. The objective of the chapter is to establish ANN as an effective technique for making appropriate predictions and thereby contributing toward the decision-making in various spheres using the outcomes from various researches. The chapter also aims to explain the step-by-step process of ANN in outcome prediction with the help of example.

The need to make informed decisions has become a matter of survival for every organisation in the current unprecedented and dynamic business environment. The exponential growth in technology along with the internet has brought in enormous volume and variety of data access to all organisations irrespective of their industry. Organisations should consider their resources and industry environment to build their analytics capabilities, as it involves a tremendous investment. In this context, it is essential to understand the role of financial analytics in decision-making for better firm performance. Hence, this chapter proposes to throw light on the evolution of big data analytics, theoretical linkages, dimensions and benefits of using financial analytics, technological support for financial analytics along with use cases, and Strengths, Weaknesses, Opportunities, and Challenges (SWOC) analysis.

This chapter provides an overview of several human behavior/psychological analytics that can be used to help assess current statuses and performances and predict future performance. Moreover, the chapter presents case illustrations for the use of analytics in attaining meaningful data that improve corporate performance. The objective is to help understand ways of reducing uncertainty through various analytics and to enhance data-driven, performance-managed organizations. The overview of multiple analytics with corresponding case illustrations attempts to fill a gap in the literature and help readers understand that using analytics in the business environment can engender productive analysis and change. This is important because organizations are being judged on metrics that are based on their internal and external impacts.

The field of analytics has seen tremendous growth in the past decade. Beginning with marketing and supply chain analytics, the application of analytics in human resources has shown lots of benefits for organizations. Research and application of HR analytics have substantiated its significant impact on organizational performance (Chierici et al., Management Decision 57:1902–1922, 2019). Analytics has the potential to generate insights for driving individual and group performance. The chapter discovers the importance of people analytics for performance management and the metrics used by organizations for measuring employee engagement and performance management. The chapter also covers how predictive analytics can be applied to determine the factors that are responsible for individual or team performance. The insights generated can be beneficial to the organizations in numerous ways if effectively implemented.

Platform economy involves technology to connect the dispersed network of participants. The Platform Business Model denotes a triangular participation between; the platform itself, the supplier and the consumer. The global market is witnessing a rise of digital platforms with an increase in the power of algorithms and cloud-based computing, connecting millions of participants in the network. The technological advancement makes the digital platforms a formidable force that ushers in change and brings out economic revolution across the globe. Many entrepreneurs have been created by these platforms, the workforces have the freedom to choose their work time and job, leading to an economically vibrant society. Platform businesses exist across various verticals, even in manufacturing setup. A variety of goods can be produced in a flexible assembly line. Hence the concept of outsourcing may require new definition from low-cost labour-based countries to high technology low-cost countries. There may be a transformation of economies shifting towards service, as major manufacturers may reorient themselves into service operators. Overall, the platform business model would make the entire operation more transparent with real time data transfer between the participants, leading to efficiency across the entire chain of business activities.

The use of big data in decision-making has been burgeoning due to its significant potential to predict the possible shifts, tendencies and trends in social and economic behaviours, politics, health issues and consumer preferences. In recent years, particularly web analytics which employs an enormous amount of data in different web domains increased its popularity. Drawing on Google and social media data, this study investigates the trends and changes in Turkish women’s current and emerging consumer trends in fashion brands. Additionally, the differences in consumption patterns between two women consumer segments which are conservatives and liberals are identified. Search engine results pages and netnographic analyses findings reveal that a new lifestyle of conservative women emerged from the combination of religious codes and Western types of consumption, which may provide marketers with significant clues about future consumption trends, preferences and changes. The limitations and further research suggestions are highlighted at the end of the paper.

Coronavirus is airborne and can spread easily. Air pollution may have an impact on breathing and also keep the virus airborne. The levels of air pollution were impacted by the lockdown measures, restricting the vehicular and industrial pollutants. Therefore, there is a need to understand the relation between air pollution levels and the Coronavirus infection rate. The study aims to find the effect of various pollutants across major cities of India on the R-value. The pollution data was collected from the Government’s official portal. The major pollutants on which the data was collected are “PM2.5, PM10, NO, NO₂, NOx, SO₂, CO, and Ozone”. The data on air pollution levels were also collected for the selected cities from April 2020 to April 2021. The spread is measured as the reproduction number at time ‘t’ (Rt), which is an estimate of infectious disease transmissibility throughout an outbreak, or it is the rating of Coronavirus or any disease’s ability to spread. The data is analysed using MS Excel and R Programming. Descriptive statistics and regularisation are performed on the data. The study results reveal that some pollutants positively and negatively affect the infection rate. However, the effect is very low, and it concluded that the pollution might not directly affect infection rates.