Analytics Enabled Decision Making “Tracing the Journey from Data to Decisions”

Vinod Sharma (sharmavins@gmail.com), Jeanne Poulose (jeanne.poulose@christuniversity.in) and Chandan Maheshkar
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Vinod Sharma: Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University)
Jeanne Poulose: CHRIST (Deemed to Be University)
Chandan Maheshkar: East Nimar Society for Education

A chapter in Analytics Enabled Decision Making, 2023, pp 1-22 from Springer

Abstract: Abstract 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.

Keywords: Analytics; Decision making; Data science; Data-based decision making; Model building; Business analytics (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-19-9658-0_1

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