Martin Kowalczyk

Big Data and Information Processing in Organizational Decision Processes

Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing mechanisms and their complementary relationship to data-centric mechanisms. Third, it demonstrates how information processing theory can be applied to assess the dynamics of mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.

Aligning Organizations Through Measurement

39 V. Basili, University of Maryland, College Park, MD, USA; A. Trendowicz, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany; M. Kowalczyk, Technical University of Darmstadt, Darmstadt, Germany; J. Heidrich, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany; C. Seaman, University of Maryland, Baltimore, MD, USA; J. Münch, University of Helsinki, Helsinki, Finland; D. Rombach, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany

An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study

Abstract Providing data-centric decision support for organizational decision processes is a crucial but challenging task. Business intelligence and analytics (BI&A) equips analytics experts with the technological capabilities to support decision processes with reliable information and analytic insights, thus potentially raising the quality of managerial decision making. However, the very nature of organizational decision processes imposes conflicting task requirements regarding adaptability and rigor. This research proposes ambidexterity as a theoretical lens to investigate data-centric decision support. Based on an in-depth multiple case study of BI&A-supported decision processes, we identify and discuss tensions that arise from the conflicting task requirements and that pose a challenge for effective BI&A support. We also provide insights into tactics for managing these tensions and thus achieving ambidexterity. Additionally, we shed light on the relationship between ambidexterity and decision quality. Integrating the empirical findings from this research, we propose a theory of ambidexterity in decision support, which explains how such ambidexterity can be facilitated and how it affects decision outcomes. Finally, we discuss the studys implications for theory and practice.

GQM+Strategies in a Nutshell

This chapter introduces the GQM+Strategies approach for aligning organizational goals and strategies through measurement. We first explain the basic idea of combining alignment and measurement within GQM+Strategies, which provides an integrated method for explicitly defining organizational goals and controls for the execution of those plans. Next, we describe in detail the core components of GQM+Strategies. This includes a specification of the GQM+Strategies model as well as the description of the GQM+Strategies process for defining, controlling, and continuously improving organizational goals and strategies.

Phase 2: Define Goals, Strategies, and Measurement

In this phase, we derive the GQM+Strategies Grid. In particular, we specify and align organizational goals and strategies within the GQM+Strategies scope, and we quantify goals using GQM graphs. Table 5.1 summarizes the objectives, inputs, basic activities, and outcomes of this phase. In the following sections, we will describe the individual activities of this phase in more detail.

Business Intelligence & Analytics and Decision Quality – Insights on Analytics Specialization and Information Processing Modes

Leveraging the benefits of business intelligence and analytics (BI&A) and improving decision quality does not only depend on establishing BI&A technology, but also on the organization and characteristics of decision processes. This research investigates new perspectives on these decision processes and establishes a link between characteristics of BI&A support and decision makers’ modes of information processing behavior, and how these ultimately contribute to the quality of decision outcomes. We build on the heuristic–systematic model (HSM) of information processing, as a central explanatory mechanism for linking BI&A support and decision quality. This allows us examining the effects of decision makers’ systematic and heuristic modes of information processing behavior in decision making processes. We further elucidate the role of analytics experts in influencing decision makers’ utilization of analytic advice. The analysis of data from 136 BI&A-supported decisions reveals how high levels of analytics elaboration can have a negative effect on decision makers’ information processing behavior. We further show how decision makers’ systematic processing contributes to decision quality and how heuristic processing restrains it. In this context we also find that trustworthiness in the analytics expert plays an important role for the adoption of analytic advice.

Perspectives on Collaboration Procedures and Politics During the Support of Decision Processes with Business Intelligence & Analytics

Raising the level of decision quality in managerial decision processes by utilizing business intelligence and analytics (BIA Davenport, 2010; Polites, 2006; Watson et al., 2002). BIA Chen et al., 2012; Dinter, 2013; Koutsoukis and Mitra, 2003; Watson, 2010). These technologies equip BIA Viaene, 2013).

Industrial Challenges and Applications

This chapter gives some insights into typical industrial challenges addressed by GQM+Strategies and highlights some industrial real-life applications of the approach. First, we will focus on typical usage scenarios and real-life challenges addressed by the different domains where the approach has actually been applied. After that, we will take a closer look at the specific challenges and industrial needs of IT and software development companies as this was defined as the initial focus and starting point of the GQM+Strategies approach. Finally, we will present three industrial cases in which the approach was applied:

Summary and Future Perspectives

Measurement provides many benefits to organizations of all types. However, measurement confined to the project level is limited in its ability to provide benefits throughout the organization. Measurement has always been used to help organizations assess and monitor various aspects of their operations and aid executives in strategic decision-making.

Phase 3: Plan Grid Implementation

In this phase, we operationalize the GQM+Strategies grid by preparing plans for implementing and deploying strategies (Strategy Plans for short) and for measuring the impact of the strategies on the attainment of organizational goals (Measurement Plans for short). Strategy plans refer to the setup of a couple of strategic projects in the organization that are responsible for implementing the defined strategies from the grid. Measurement plans refer to the setup or modification of measurement and control mechanisms. Thus, planning includes defining or adjusting procedures with respect to which activities are to be performed, by whom, when, how often, and how they will be performed, including the required infrastructure. Table 6.1 summarizes the objectives, inputs, basic activities, and outcomes of the “plan grid implementation” phase.