Objective and main results

This article reports on case studies of four organizations from the insurance, banking, telecommunications, and e-commerce (travel) industries. The study shows how technologies supporting big data analytics (BDA) enable innovation in terms of improved service individualization.

Main findings: 
The research suggests that there are two main types of BDA-enabled service innovation.

• Automation of customer-sensitive service provision: 
  Organizations use key features of BDA technologies to automate service processes in order to provide (a) trigger-based service actions and (b) preference-based service actions to customers.
• Human-material customer-sensitive service practices: 
  Organizations identify new ways for IT-enabled service processes where human service actors interact with BDA technologies to engage in trigger-based interactions and preference-based interactions with customers.

Summary of practical implications

Practitioners designing BDA infrastructures to support service innovation can use the categories of features identified in this study to identify suitable and scalable technologies. The categories are:

• sourcing and storage features
• analytic features in terms of event recognition and prediction and behavior recognition and prediction
• exploitation features

Practitioners can revisit their IT infrastructures to determine to what extent such features are present that might be exploited to afford service innovation or to determine whether they can be created through reprogramming.

Practitioners can use the theoretical model to analyze their need for service automation or human-material service practices. The appropriate strategy depends on the type of service as well as the customer’s expectation.

IT managers must have a holistic grasp on how BDA technologies afford different models of service provision such that the service provision is aligned with the organization’s strategic goals.

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