Big Data-driven Value Creation for Organizations

Abstract

The past few years have been characterized by an enormous increase in data coming from different sources in real time and in many diverse forms. The term commonly used for such data is Big Data (BD). Much of this BD has a high business value and, if properly utilized, can become an important organizational asset helping the organization to achieve competitive advantage. However, many organizations make a limited use of BD because they lack necessary tools and/or do not understand the value of this data. The main contribution of this study is to investigate an issue of Big Data and elements shaping creation of BD-based business value. In particular, the outcome of this research is to build and verify a framework to provide business value based on BD. The proposed framework is distinguished by three components: (1) dynamic capabilities of organizations, (2) integrated process of BD resource exploration and exploitation, and (3) identification and measurement of business value creation based on BD.

Effects of Visualization Techniques on Understanding Inconsistencies in Automated Decision-Making

“…Nagel et al (2019) could show that such inconsistency metrics are associated with a better understanding of inconsistencies. In light of the unique big data challenges in WFMS, not only these (quantitative) insights themselves but also the speed of their use become important to foster the innovative and sustainable development of WFMS [19]. Thus, the actual visualization of metrics becomes a critical success factor in creating value from case data, as it can impact how information can be analyzed and interpreted.…”

“…However, monitoring correct and consistent decision-making in WFMS is currently evolving into a big data analytics problem for companies [6,25]. This becomes apparent in the scope of the 7V model [13,19], which indicates the main attributes of big data. For example, the volume of data created by WFMS is increasing rapidly.…”

“…In order to ensure consistent decision-making, companies must understand and utilize process case data to detect errors in automated decisions [6]. Such an understanding can be an important driver in creating value through innovation, e.g., by minimizing mistakes, improving WFMS, and streamlining business processes [6,12,19]. To support companies in this aim, the scientific field of inconsistency measurement has evolved and proposed so-called inconsistency measures, which are metrics that can help to identify those cases where inconsistencies have occurred in the decision-making [6,8].…”

“…Due to the unique big data challenges arising in the context of WFMS, not only these quantitative metrics themselves, but also an effective use becomes even more important [19,27]. In this context, the speed and ability to interpret data is closely related to a dimension of the 7V model, namely the visualization of metrics and data.…”

“…. Research AimFollowing authors such asSurbakti et al (2019) orOlszak & Zurada (2019), the effectiveness and speed of using insights into big data is a key factor in creating value for companies. As these abilities are closely linked to how information can be processed, this work aims at investigating how different visuali-Page 200…”