Big Data for Big Business? A Taxonomy of Data-Driven Business Models Used by Start-Up Firms

The purpose of this paper is to propose a taxonomy of business models used by start-up firms that rely on data as a resource of major importance for their business – aka data-driven business models (DDBMs). Furthermore, it proposes a DDBM framework to systematically analyse and compare DDBMS in the start-up world. To develop the taxonomy of DDBM, business model descriptions of a random sample of 100 start-up firms were coded using a DDBM framework, comprising of six dimensions with in total 35 features, derived from literature. By a subsequent application of clustering algorithms to the coded descriptions different types of business model were identified. Case studies of four companies from the sample were conducted to validate the findings. The taxonomy derived from our research consists of six different types of start-up DDBM. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. The paper helps not only future researchers to structure their work around data-driven business models but also companies to build new DDBMs. The proposed taxonomy will help companies to position their activities in the current landscape. This paper explores how start-up business models capture value from big data. A proposed taxonomy consisting of six types of start-up business model is developed. The paper also introduces the DDBM framework with which the business models can be studied and analysed