The Support of Decision Processes with Business Intelligence and Analytics

Insights on the Roles of Ambidexterity, Information Processing and Advice

verfasst von: Martin Kowalczyk

Verlag: Springer Fachmedien Wiesbaden

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

In his research, Martin Kowalczyk empirically investigates the challenges of designing and establishing successful decision support with Business Intelligence and Analytics (BI&A). The results from his work elucidate organizational and individual perspectives of BI&A support in decision processes. The organizational perspective considers the processual aspects of decision making and addresses process phases, roles and their interactions. The individual perspective reflects upon decision making of human individuals including their cognition and behaviors involved in decision making. The support of managerial decision making with BI&A gains increasing priority for many businesses in their desire to achieve better decision outcomes and improved organizational performance.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

The general idea of improving managerial decision making through support with high quality information or facts is shared by the decision support specialty of information systems research (Arnott and Pervan, 2005, 2008, 2014), management research (e.g., Pfeffer and Sutton, 2006; Simon, 1960), and practitioners alike (e.g., Davenport et al., 2010; LaValle et al., 2011). The reason for this huge interest lies in research findings and practitioner reports that suggest that data-driven decision making results in better decisions and, as a consequence, in better organizational performance (Brynjolfsson et al., 2011; Davenport et al., 2010; LaValle et al., 2011).

Martin Kowalczyk

Chapter 2. Study A: A Structured Literature Review on Business Intelligence and Analytics from a Decision Process Perspective

Abstract

In recent years the idea of business intelligence and analytics (BI&A) has gained an increasing amount of interest among researchers and practitioners due to a number of published success cases. These success cases report tremendous improvements in organizational performance, based on improved decision making and new business insights (Chen et al., 2012; Davenport, 2006). BI&A systems provide support for collecting and transforming data and put particular emphasis on data analysis with the purpose of improving decision making (Chen et al., 2012; Davenport, 2006; Shanks et al., 2010).

Martin Kowalczyk

Chapter 3. Study B: Big Data and Information Processing in Organizational Decision Processes

Abstract

In recent years, data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have attracted major attention in both the academic and the business communities (Buhl et al., 2013; Chen et al., 2012; LaValle et al., 2011). The interest is driven by expectations of tremendous improvements in organizational performance based on new business insights and improved decision making. In this context, big data and BI&A can be regarded as two sides of the same coin.

Martin Kowalczyk

Chapter 4. Study C: Perspectives on Collaboration Procedures and Politics during the Support of Decision Processes with Business Intelligence and Analytics

Abstract

Raising the level of decision quality in managerial decision processes by utilizing business intelligence and analytics (BI&A) is a crucial task, but the realization of the expected benefits is often challenging (Clark et al., 2007; Davenport, 2010; Polites, 2006; Watson et al., 2002). BI&A comprises a set of data collection, integration, and analytics technologies, which aim at improving data processing and analysis procedures along the information value chain (Chaudhuri et al., 2011; Chen et al., 2012; Dinter, 2013; Koutsoukis and Mitra, 2003; Watson, 2010). These technologies equip BI&A-experts (i.e., analysts or data scientists) with the technological capabilities for supplying decision makers with high quality information (Davenport and Harris, 2007; Viaene, 2013).

Martin Kowalczyk

Chapter 5. Study D: An Ambidextrous Perspective on Business Intelligence and Analytics Support in Decision Processes

Abstract

Data-centric decision support is vital for managerial decision making in organizational decision processes. Business intelligence and analytics (BI&A) equips analytics experts (i.e., analysts or data scientists) with the technological capabilities to support decision processes with reliable information and analytic insights (Chaudhuri et al., 2011; Chen et al., 2012; Davenport and Harris, 2007; Davenport and Patil, 2012). The added value of BI&A is based on increasing the utilization of “data-driven” decision making and thus improving decision quality and organizational performance (Brynjolfsson et al., 2011; McAfee and Brynjolfsson, 2012; Pfeffer and Sutton, 2006).

Martin Kowalczyk

Chapter 6. Study E: Business Intelligence and Analytics – Decision Quality and Insights on Analytics Specialization and Information Processing Modes

Abstract

Business intelligence and analytics (BI&A) provides the technological capabilities for data collection, integration, and analysis with the purpose of supplying decision processes with high quality information and new analytic business insights (Chaudhuri et al., 2011; Chen et al., 2012; Davenport and Harris, 2007; Dinter, 2013; Watson, 2010). While the supply of high quality information and the generation of analytic insights have the potential for improving managerial decision making, they must be used effectively in decision processes in order to live up to this potential (Pfeffer and Sutton, 2006; Popovič et al., 2014; Shollo and Galliers, 2013).

Martin Kowalczyk

Chapter 7. Conclusion and Summary of Contributions

Abstract

This thesis aimed to contribute to our comprehension of what constitutes successful BI&A-supported decision processes and how to establish effective BI&A support that involves collaboration between specialized roles (i.e., analytics experts and decision makers). Progress in understanding both of these aspects of BI&A support creates a foundation for designing and establishing BI&A-supported decision processes that result in improved decision making and higher quality decision outcomes.

Martin Kowalczyk

Backmatter

Titel: The Support of Decision Processes with Business Intelligence and Analytics
verfasst von: Martin Kowalczyk
Verlag: Springer Fachmedien Wiesbaden
Electronic ISBN: 978-3-658-19230-3
Print ISBN: 978-3-658-19229-7
DOI: https://doi.org/10.1007/978-3-658-19230-3