Data model considerations in the manufacturing enterprise

Data typically requires context or meaning in order to be of value. In an applied sense the context is often implemented in the form of a data model. Contemporary manufacturing environments rely on the use of data models both throughout their automation landscape as well as within most layers of business operations. Industry 4.0 (with a projected market spend of over US$150 billion by around 2026) relies heavily on the use of data models. The scale, complexity and level of integration of data models is set to increase markedly over the next phases of migration towards Industry 4.0.However the nature, location(s) and significance of data models are not always understood by many of the stakeholders within the enterprise. This can lead to decisions around system architecture, ownership and accountability that result in sub-optimal outcomes for the enterprise.This paper clarifies the nature and characteristics of data models in the manufacturing enterprise, providing a context and understanding for stakeholders and decision makers.