Laissez-faire interpretation of what constitutes a digital twin may catalyze a broader diffusion of the principles (ideas) and perhaps even accelerate adoption of digital representations of physical entities, albeit in select parts of the affluent world (nations with significant amount of disposable income, per capita). The limits of efficiency and efficacy of digital proxies will affect the value of actionable (bidirectional) information which may be extracted/shared/exchanged from data and analytics (contextually connected causal relationships, Figure 33). Applications are easier in the mechanical context (manufacturing, automotive, buildings). Digital duplicates of natural systems (environment, health, agriculture) are beguiling. Representation in the form of “twins” suggests exact/identical twining (of data) which may be difficult to duplicate between the physical and digital. Hence, digital cousins of tiny sub-segments of systems may be useful if we grasp the science of the data and avoid the less understood cognitive processes (cognition refers to mental action or process of acquiring knowledge and understanding through thought, experience, and the senses). If parameters are well understood (e.g., causality), if the acquired data is rigorous, mathematically robust (e.g., proportionality, rate, ratio) and informative (e.g., blood glucose levels and type II diabetes mellitus), then digital cousins may be less irrational as an aspirational goal. Directly or indirectly, knowingly or unknowingly, in astronomical events or in infinitesimal instances, all tools, technologies and techniques (e.g., statistical, operations research [OR], mathematical) converge to catalyze our need to be data-informed, to make sense of data before the value of the data perishes, and extract actionable information (e.g., process optimization in OR). At the core of almost any system with a popular “buzz” (digital twins, internet of things, cyberphysical systems, cloud, machine learning, smart cities, “big data”, “DL”, “AI”, “Industry X.O”) we commence with data to extract meaningful information of value. Relevant semantics or “meaning” must arise from the anastomosis of causality with context as well as metrics and measurements. Value is related to “performance” depending on the context and actions (feed-back, feed-forward) which could become a highly complex decision process (e.g., explosion of state space when synthesizing or analyzing data from percepts, environment, actuators, and sensors, referred to as PEAS, the superset of the OODA loop: the cycle of observe-orient-decide-act). The underlying glue that permeates the fabric of continuum between meaning and value is causality. Almost every “thing” (made of atoms) or processes or systems we dissect, deconstruct and reconstruct, is made significant when and if associated with data (bits). The continuum of meaning and value is in dynamic interaction with the continuum between atoms to bits. The constructs of this multi-string, multi-dimensional continuum are connectivity, data, analytics and context (ACDC). In this chapter, we explore examples of this “electricity” which powers the engines of science, decision science, and data-informed systems across a broad and diverse spectrum of verticals and applications. However, economics of technology could make or break digital representation. It may remain prohibitive for decades, if not centuries, in resource constrained communities, which comprises ~80% of the global population of ~8 billion. Therefore, one begs to ask how suitable are digital twins?