The Data Analytics: Enabler for Systems Engineering By Rick Hefner, PhD Data Analytics: Enabler Systems Caltechfor Center for Engineering Technology
and Management Education, California Institute of Technology By Rick Hefner, PhD
Copyright © 2021 Rick Hefner. Authored for PPI SyEN.
Data analytics involves inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Since systems engineers rely on decision-making in conceiving, designing, integrating, and testing complex systems, it is not surprising that data analysis would be an important enabler. In this article, we examine the basic tenets and tools of data analytics and how they can be applied to developing complex systems. Introduction Successful systems engineering efforts rely on hundreds, if not thousands, of decisions. Historically, decision-making has relied on the experience and heuristics of the systems engineers involved. In their seminal text, The Art of Systems Architecting [1], Mark Maier and Eberhardt Rechtin describe heuristics as succinct expressions of lessons learned from one’s own or others’ experiences. They might take the form of guides to selecting the right architecture for a specific type of problem, or ideas for how to modify and existing designs to improve some property, like reliability. As our systems become increasingly complex, it is tougher to make proper decisions based solely on experience. In some cases, we are developing unprecedented systems or using emerging technologies for which we have few applicable experiences. In other cases, we are applying systems engineering in relatively new domains, like health care and social sciences, where fundamental system principles are not widely known. Finally, we lack sophisticated methods for capturing the wisdom of an aging systems engineering workforce and passing their knowledge on to the next generation [2]. Numerous efforts are underway to address this knowledge gap. One approach is model-based systems engineering (MBSE), which attempts to capture knowledge about an evolving system, and use it to support decision-making. Information from past system development efforts, captured in a centralized corporate repository, can serve as guide to developers of future systems. Data Analytics The data analytics discipline is focused on extracting insights through the collection, organization, storage and reporting of data. It is part of the broader field of data science, and includes subfields such as business analysis, which uses data mining, statistical analysis, and predictive modeling to drive better business decisions. The types of data analytics, and the questions they answer, include: •
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Descriptive analytics: What has happened and what is happening right now? Uses historical and current data from multiple sources to describe the present state by identifying trends and patterns.
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