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18 BRINGING INTELLIGENCE TO THE EDGE What Is Data Analytics? Data analytics is the science of scrutinizing raw or unprocessed data to derive meaning so it can be applied to decision-making. Data analytics employs several modern tools and techniques that help to structure data and make it understandable. Several preliminary steps need to be applied in the process before data analytics can begin: • Data needs to be collected from a variety of sources. • Data needs to be interpreted correctly so it may be accurately organized and grouped. • Data needs to be examined to eliminate any replications or transcription errors. • Data may be managed by tables and/or spreadsheets. Data analytics can be subdivided into four main categories (Figure 2): descriptive, diagnostic, predictive, and prescriptive. Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics This is the process where data is employed to examine, understand, and describe an event that's already transpired. This goes deeper than descriptive analytics as it pursues to comprehend the why behind what happened. It is dependent on historical data, past trends, and assumptions to answer projective questions about the future This process aims to identify specific actions that an individual or organization should take to reach future targets or goals Figure 2: Four main categories of data analytics. (Source: Renesas Electronics) Real-time analytics is the process of preparing, assessing, and analyzing data as it becomes available. Coupling real-time analytics with AI has provided businesses with fresh and revealing insights into the consumer experience. It has provided support personnel and IT the opportunity to act preemptively rather than react, resolving potential issues before endpoint users even realize there is an issue to report. The combination of AI and real-time analytics has completely transformed the consumer and consumer support experiences. Endpoint Analytics of Things According to the analytics firm International Data Corporation (IDC), 45% of data collected from IoT devices needs to be analyzed nearer to the endpoint (i.e., closer to the device itself) to optimize overall efficiency rather than transmitted to the cloud. Shifting away from the cloud has several potential advantages: Transferring data to the cloud can be costly, as it requires significant