Towards a Process Model for Data-Driven Business Model Innovation

The growing importance of data and analytics in a broad range of industries has led to an increased attention of business model literature on this matter. The question of how to capture the business potential associated with data, drives researchers to explore the impact of data and analytics on the business model concept. However, little research has been conducted on how an organization should approach to enrich its existing business model with data or, yet, to develop a completely new, data-driven business model. Our research aims to address this issue by providing a process model for data-driven business model innovation. Therefore, we conduct an extensive review of existing business model innovation processes and derive distinct requirements, taking into account both data specific and business model innovation specific characteristics. The requirements are used to assess the suitability of the identified processes for the development of a data-driven business model. We disclose that existing processes are not appropriate for creating such business models. Based on these findings, we design a new, data-driven business model innovation process that meets the data specific requirements.

I. Introduction

The amount of data that is created in an industrial context is rapidly increasing. Structured data, such as physical states of machinery, as well as unstructured data, like information on human interactions, captured by organizations produces an overwhelming flow of data nowadays [1]. While academic literature agrees on its inherent potential value, the available data, referred to herein as big data, provides an opportunity for companies and their offerings in terms of strategic business differentiation. In this context, the combined use of big data and analytics seems promising [2]. Hence, the goal of reaping competitive advantages based on a utilization big data drives researchers and practitioners to transform existing businesses incorporating data [3].