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High-Speed Data in Industrial, Automotive, Healthcare, and Data Centers 24 Accessible In industrial automation, accessibility refers to two different aspects: connecting a distributed workforce and integrating human factors into system design. The first aspect refers to connecting a distributed workforce through integrated, secure systems. Such systems have been evolving for decades, but only recently have the technologies necessary converged to enable seamless and secure collaboration. In earlier decades, we saw various components and pieces of technology make collaboration possible, but only now do we have what we need to fully integrate distributed workforces, resources, and services across all aspects of business. The second aspect refers to integrating human factors into system design in terms of ease of use for installers, operators, technicians, floor workers, and others. Designing with humans in mind used to be an afterthought or a nicety. Now, companies realize that in these cases, the installers, operators, etc. are the consumers. Therefore, considering human factors and applying related guidelines are important parts of design success. In both cases, cloud infrastructure—with system integrators such as infrastructure as a service, platform as a service, software as a service, and similar concepts—are the most significant enablers of accessibility. Teams working in real time with minimum lag time provide considerable extensibility for the types of collaboration possible. An example of this is augmented reality (AR): Rather than engineers, technicians, manufacturers, and other stakeholders flying in to install, operate, troubleshoot, or repair a mechanical system, AR enables stakeholders to access data analytics, see the system in real time, and use visual overlays to make repairs or alterations. The efficiencies related to cost, time, and product life cycle are already significant, and we can expect them to increase the number of AR technologies improved over time. Data Driven In today's industrial automation, data drives many insights. For instance, data can evaluate how a manufacturer is doing in terms of what is most important to the company. Here, value matrices are used to identify the five or six aspects most important in measuring the company's success (e.g., speed, accuracy, conformance to standards, conformance to regulations, and customer satisfaction). Using real- time data and data trends over time, stakeholders can see how the company performs overall, in terms of its values, and compared with industry averages. Data insights have moved humans from simply responding to manufacturing issues to instead proactively preventing and addressing them. Fluctuations in data can be used to identify potential problems and then trigger email messages to technicians, support tickets to the help desk, or text messages to supervisors. In some cases, these fluctuations can be benign; nonetheless, real-time data and trends can help put humans in a proactive rather than reactive role. Finally, data can be used to evaluate the industrial automation characteristics discussed in this article. For instance, data can enable greater manufacturing agility by providing insights into when a manufacturer should start offering a sofa in additional colors or different fabrics rather than manufacturing those instances as product variations. Data can also drive process resilience by minimizing downtime for product variations and ensuring that humans are in the right places at the right times in the processes.