Towards Design Principles for Data-Driven Services in Industrial Environments

Abstract

The ever-growing amounts of data offer companies many opportunities to exploit them. Resulting datadriven services hold great potential for creating unique value for customers and the achievement of competitive advantages. Nevertheless, especially companies in the industrial environment struggle to implement successful data-driven service innovations. Surprisingly, there is a lack of scientific research addressing this issue. Thus, our research generates design principles for data-driven services to aid in their development. For this purpose, we present a qualitative interview study with experts in different lines of businesses among the industry sector, holding varying positions and roles in service systems. Through practical examples, we show which challenges exist in the development and use of datadriven services. On this basis, we derive design principles to help understanding data-driven services and to overcome difficulties identified in practice, notably, that allows practitioners to develop new services or redesign existing ones.

Introduction to the Minitrack on Cognitive and Neuroscience Research in IS

“…This results in an interpretive structural model for the barriers, which transparently shows the contextual relations. The study allows managers to systematically tackle and reduce or eliminate barriers paving the way for data-driven services (Azkan et al, 2023).…”

Complementary and contingent value of SMEs' data capability and supply chain capability in the competitive environment

“…Data capability increases the amount of relevant information based on data and therefore helps identify customers' needs in a turbulent environment, making it easier to address them. Further, the knowledge accumulated from the data and the data-driven services can enable SMEs to differentiate themselves from competitors and create value for the customers, spawning a competitive advantage (Azkan et al, 2020(Azkan et al, , 2021. If resources are limited, an SME must carefully consider resource allocation.…”

Data or Business First?—Manufacturers’ Transformation Toward Data-driven Business Models

“…Manufacturers use data internally to improve decision-making and enhance efficiency (Hess et al 2016;LaValle et al 2011;Sjödin et al 2018). Furthermore, manufacturers can leverage data analytics to create new, data-driven service offerings for their customers by including data as the key resource (Azkan et al 2021;Hunke et al 2021). For instance, data-based alert systems and predictive maintenance services provide the customer with maintenance cost reduction, optimized machine operation, or increased machine availability (Hartmann et al 2016;Bertolini et al 2021).…”

“…Hence, DDBMs can vary greatly regarding their technical requirements and value proposition for customers. This poses a challenge to manufacturers, as they must develop capabilities appropriately and aligned to the DDBM archetype to address both K business and technical demands (Hunke et al 2019;Azkan et al 2021;Rashed and Drews 2021). For instance, manufacturers must develop business capabilities for the successful monetization and marketing of their data-driven offering (Baltuttis et al 2022;Häckel et al 2021).…”