## 1 Introduction  
In exploring recent advancements in manufacturing and industry, interested practitioners and researchers might find themselves deciphering a series of seemingly related, sometimes interchangeable, but actually distinct terms that mean different things to different parties in different contexts. In some cases, specific terminology might be used in one part of the world whereas another term is employed elsewhere. Consider the following examples, representative but not exhaustive, that are commonly seen today. One familiar idiom, \"smart manufacturing\", is a general term for the use of sensors and wireless technologies to capture data in all stages of production or product lifecycle. Examples include vehicle engines collecting and transmitting diagnostic information or optical scanners detecting defects in printed circuits [1; 2]. Another common term, \"Industrial Internet of Things (IIOT)\", initially coined by General Electric (GE) in 2012, refers to a network of industry devices connected by communications technologies for the purposes of monitoring, collection, exchange, analysis and delivery of insights to drive smarter, faster business decisions [3; 4]. Examples of consortia targeting the IIOT include the Industrial Internet Consortium (IIC) [5] and the OpenFog Consortium [6]. Industry 4.0 [7; 8], China Manufacturing 2025 [9], and Connected Industries [10; 11] are specific paradigms of the IIOT applied in the manufacturing context with common fundamental conceptssuch as smart manufacturing, cyber-physical systems, self-organization, adaptability and corporate social responsibility. Finally, the Connected Enterprise (CE), a Rockwell Automation term [12] to describe its vision for the future of industrial automation, is an enterprise-wide extension of Industry 4.0 and China Manufacturing 2025. The integration of information technology (IT) and operational technology (OT) enables the interaction between manufacturing process data, people, and the business enterprise to optimize key performance indicators (KPI) at all levels of the organization: factory level, enterprise level and global supply chain level. Figure 1 is a pictorial representation of the CE strategy that enables the seamless convergence of the information and operation functions of an enterprise. The Connected Enterprise is one of: [13]:  
* enterprise-wide visibility and collaboration;
* interconnected people, equipment, and processes;
* real-time learning of enterprise status;
* organizational agility by means of increased information to make informed, adaptive, proactive decisions. The purpose of this literature review is to survey and discuss representative examples along the spectrum of existing research into a specific key enabler to the Connected Enterprise in manufacturing: big data. A good working definition of a \"big data\" environment is one such that the size, structure, or variety of information strains the capability of traditional software or database tools to capture, store, manage, and analyze it [14; 15]. Big data is clearly both an enabler to the CE and an obstacle. It is an obstacle in that, by definition, it requires innovation to truly harness; it is an enabler in that it is precisely the availability of vast untapped data that undergirds the enormous potential of the CE. [PERSON] (2010) explores this duality in big data for The Aspen Institute in [16]. Three related factors provide the motivation for this research. First, the rapid advent of technology to capture and store manufacturing data without the parallel development of corresponding analytical capabilities has resulted in the circumstance by which vast quantities of data are collected but not effectively analyzed or interpreted [1]. Second, the dual nature of big data as both an enabler and an obstacle to the CE perpetuates the state of affairs by which manufacturers do not have a clear picture as to what manufacturing data, of the vast volumes collected, is truly valuable versus what can be discarded.