In todays global economy, making “good” decisions has become one of the last areas of operations still enabling organisations to gain a competitive advantage. The ability to predict future developments and anticipate which outcomes actions will have based on facts rather than intuition, enables organisations to consistently operate in a more effective manner. Establishing facts requires knowledge, at the core of which lies the creation and utilisation of objective measurements; data. The demand for optimisation and innovation, paired with the growing awareness for the role data utilisation (DU) can play in it, has led to innovations in both data creation (e.g. Cyber Physical Systems in the dawn of the Industry 4.0 revolution) and analysis (Big Data, AI based analytic services, etc.). Some organisations are managing to harness this increasing potential through effective integration into existing DU systems, while others are struggling to even make use of the DU capabilities already available to them. This may partly be due to the fact that organisations are looking to improve their DU capabilities are faced with a lack of comprehensive, reliable and structured resources providing practical guidance in this field of expertise. Although a range of literature dealing with DU related concepts (e.g. Business Intelligence Management, Knowledge Management, etc.) exists, there are no publications providing both an exhaustive overview of the theoretical foundation of what constitutes DU capability, as well as a method by which DU capability could be assessed in organisations. To provide both, a sound theoretical basis for DU requirements, as well as a practical approach to assessing an organisations capabilities the “Maturity Model for Assessing the Capability to Utilise Data in Industrial Enterprises” (MMACUDIE) was developed over the course of this Master Thesis. To make the MMACUDIE development a rigorous, reliable and comprehensible process, development was based on the Design Science guidelines of Hevner and the maturity model specific adaptions by Becker and De Bruin. The model and an accompanying assessment method were developed to the point of being deployable, based on the insights of highly relevant and qualitative pieces of literature that resulted out of a SLR. They were then further developed under the considerations of the feedback that was received in a pilot test, conducted in the context of personal interviews in a Vienna based industrial enterprise. Despite its early evolutionary development stage, the assessment experience through the MMACUDIE was rated as being insightful, accurate and comprehensible by the assessment partners, establishing face validation for both the model and method. The MMACUDIE is ready for deployment and a confirmation of its content validity through experts in the field.