Data, information and analytics as services

Delen, Dursun; Demirkan, Haluk

doi:10.1016/j.dss.2012.05.044

Cited by 319 publications

(219 citation statements)

References 3 publications

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“…There is growing interest in the use of data and advanced analytics for various types of business problems (Delen et al 2012;Turban et al 2011). Business Analytics (BA) is an umbrella industry term referring to the application of a broad range of analytical techniques and methods and data-driven analytic methodologies to different business domains.…”

Section: Introductionmentioning

confidence: 99%

“…In the IS literature, for example, ITs are treated as primary resources (e.g., Luo et al 2012). In the BA contexts, data and analytics are discussed as two most important elements (Davenport et al 2011;Delen et al 2012;O'Dwyer et al 2011;Sharma et al 2010). Thus, advanced analytics and accurate manufacturing data are considered two "primary" resources for the use of BA for manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective

Chae

Yang

Olson³

et al. 2014

Decision Support Systems

161

100

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This study is interested in the impact of two specific business analytic (BA) resourcesaccurate manufacturing data and advanced analytics-on a firms' operational performance. The use of advanced analytics, such as mathematical optimization techniques, and the importance of manufacturing data accuracy have long been recognized as potential organizational resources or assets for improving the quality of manufacturing planning and control and of a firms' overall operational performance. This research adopted a contingent resource based theory (RBT), suggesting that the moderating and mediating role of fact-based SCM initiatives as complementary resources. This research proposition was tested using Global Manufacturing Research Group (GMRG) survey data and was analyzed using partial least squares/structured equation modeling. The research findings shed light on the critical role of fact-based SCM initiatives as complementary resources, which moderate the impact of data accuracy on manufacturing planning quality and mediate the impact of advanced analytics on operational performance. The implication is that the impact of business analytics for manufacturing is contingent on contexts, specifically, the use of fact-based SCM initiatives such as TQM, JIT, and statistical process control. Moreover, in order for manufacturers to take advantage of the use of data and analytics for better operational performance, complementary resources such as factbased SCM initiatives must be combined with BA initiatives focusing on data quality and advanced analytics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective

Chae

Yang

Olson³

et al. 2014

Decision Support Systems

161

100

View full text Add to dashboard Cite

show abstract

“…It is further not uncommon to distinguish "basic analytics" and "advanced analytics" [19], [16]. Following this view on analytics, we refer to it as processing of large amounts of data through the use of methods from statistics and operations research in order to derive descriptive, predictive, or prescriptive decision support [20].…”

mentioning

confidence: 99%

Capturing Value from Data: Revenue Models for Data-Driven Services

Schüritz¹,

Seebacher²,

Dorner³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…This scheme was defined by Delen et. al [17] as a high-level taxonomy for business analytics. Table 7 provides a summary of each type of analytics.…”

Section: Type Of Analyticsmentioning

confidence: 99%

Big data in manufacturing: a systematic mapping study

O’Donovan

Leahy

Bruton

et al. 2015

Journal of Big Data

148

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The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These smart facilities are focused on creating manufacturing intelligence from real-time data to support accurate and timely decision-making that can have a positive impact across the entire organisation. To realise these efficiencies emerging technologies such as Internet of Things (IoT) and Cyber Physical Systems (CPS) will be embedded in physical processes to measure and monitor real-time data from across the factory, which will ultimately give rise to unprecedented levels of data production. Therefore, manufacturing facilities must be able to manage the demands of exponential increase in data production, as well as possessing the analytical techniques needed to extract meaning from these large datasets. More specifically, organisations must be able to work with big data technologies to meet the demands of smart manufacturing. However, as big data is a relatively new phenomenon and potential applications to manufacturing activities are wide-reaching and diverse, there has been an obvious lack of secondary research undertaken in the area. Without secondary research, it is difficult for researchers to identify gaps in the field, as well as aligning their work with other researchers to develop strong research themes. In this study, we use the formal research methodology of systematic mapping to provide a breadth-first review of big data technologies in manufacturing.

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Data, information and analytics as services

Cited by 319 publications

References 3 publications

The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective

The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective

Capturing Value from Data: Revenue Models for Data-Driven Services

Big data in manufacturing: a systematic mapping study

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