ABSTRACT
A data analyst collects, cleans, and interprets data to answer a question or solve a problem. Data analysts work in many industries, including business, finance, criminal justice, science, medicine, and government. The role not only includes plenty of time spent with data but also entails communicating findings. During the process of data analysis, analysts use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry are Microsoft Excel, Google Sheets, and SQL.
This chapter analyses the concept of maintenance analytics (MA), which focuses on new knowledge discovery in maintenance. MA addresses the process of discovery, understanding, and communication of maintenance data from four time-related perspectives. These time-related perspectives match with the determination of the past, present, and future state of an asset summarised in four questions: What happened? why did it happen? what will happen, and how can we make it happen? These are the questions involving the determination of the state of an asset. This chapter analyses these questions, showing how they are related to maintenance descriptive analytics, maintenance diagnostic analytics, maintenance predictive analytics, and maintenance prescriptive analytics.
This chapter also discusses the types of data analytics: prescriptive analytics, diagnostic analytics, and predictive analytics.
