Service Analytics: Leveraging Data Across Enterprise Boundaries for Competitive Advantage

Fromm, Hansjörg; Habryn, Francois; Satzger, Gerhard

doi:10.1007/978-3-642-29181-4_13

Cited by 20 publications

(21 citation statements)

References 11 publications

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“…Although the use of data and analytics is viewed to offer new ways of growth and competitive advantage [18], there is only little progress in integrating analytics within service systems [16]. Referring to Davenport [21], the use of analytics has developed from a traditional -mostly internal -decision support perspective to a stage where analytics helps creating additional value for customers by enriching products and services through the exploitation of data.…”

Section: Data-driven Servicesmentioning

confidence: 99%

“…We focus on start-up companies using pure data-driven services to reflect that these services are at an early stage of development and application [16] and that start-ups tend to be the first ones to leverage the opportunities of novel technologies [43].…”

Section: Source Of Datamentioning

confidence: 99%

“…Although analytics has gained attention within recent years, the field of analytics still lacks of a uniform definition [16]. In fact, while there is no agreed-upon definition of analytics, some may use "analytics" and "data mining" interchangeably [17] or use "analytics" as a synonym for "business intelligence" [18].…”

mentioning

confidence: 99%

“…In fact, while there is no agreed-upon definition of analytics, some may use "analytics" and "data mining" interchangeably [17] or use "analytics" as a synonym for "business intelligence" [18]. 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%

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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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Section: Data-driven Servicesmentioning

confidence: 99%

Section: Source Of Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…For realizing service analytics for cost accounting, there is a need to capture, process, and analyze data within such a service system. This application where analytics supports services systems has been proposed as services analytics [18]. Service analytics can be decomposed along two different dimensions.…”

Section: Service Analyticsmentioning

confidence: 99%

Service System Analytics: Cost Prediction

Seiringer

Cardoso

Bischhoffshausen

2013

IFIP Advances in Information and Communication Technology

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Abstract. Services become every day more important for society. Cost prediction is one important aspect which has not received the required attention. Nonetheless, its impact on the economic success of service-based industries is considerable and, so, it cannot be overlooked. Therefore, in this paper we tackle the problem of cost prediction of service systems. Since previous work generally followed an ad hoc approach, we present a three layered approach based on customer factors to measure customer involvement since it is a good predictor for service cost. Service co-creation between customer and provider is the central concept of our costing approach. For service modelling we use Linked USDL, and to reason on costing data we use methods from service analytics. On the one hand, we reduce the uncertainty introduced by customers and can predict more accurate service costs. On the other hand, the systematic approach enables a better understanding of the costing problem and the service systems under study, and increases reusability of service analytics algorithms.

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