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16 BRINGING INTELLIGENCE TO THE EDGE and even preventing potential issues before they become problems. AI can restructure data collection and analysis operations in real time and reduce unanticipated network interruptions. The marriage of AI and data science can improve data set structuring, help reach conclusions and significantly enhance IoT operations. Data Science and Machine Learning Data science is the integrated approach of extracting insights from growing volumes of data using various scientific methods, algorithms, and processes. Data science is driven by software that recognizes hidden patterns within unprocessed data and draws conclusions from those patterns. These valuable insights help facilitate corporate decision-making by interpreting problems analytically and generating viable solutions. Cross-Industry Standard Process for Data Mining (CRISP-DM) is a process model that provides an overview of the data science life cycle. It's essentially a framework that assists in planning, organizing, and implementing a data science project. CRISP-DM consists of the steps in Figure 1: Data Science and AI-Driven Real-Time Analytics KAUSHAL VORA | SENIOR DIRECTOR RENESAS ELECTRONICS SUAD JUSUF | SENIOR MANAGER RENESAS ELECTRONICS AI is proving to be a more precise and time-efficient tool in processing the big data crunch by recognizing patterns and noticing inconsistencies in real time. M ost modern consumer-driven industries accumulate large volumes of data, but without a filtering mechanism that maps, charts, and trends data models, the raw data has little to no utility. Traditional analytical mechanisms are no longer up to the task—the sheer amount of data and the speed at which it's being accumulated has made it economically and operationally necessary to scale up analytical capacity using data science. The Internet of Things (IoT) is constantly collecting data, and the organization, distribution, and analysis of all this data is the new frontier of artificial Intelligence (AI). While conventional data analysis methods are still being implemented, AI is proving to be a more precise and time- efficient tool in processing the big data crunch. AI can recognize patterns and notice inconsistencies in real time. AI algorithms can save significant time and energy by compiling data from multiple sources and presenting it with a uniform, consistent approach. AI and machine learning can substantively improve secure and predictive analytics operations in the data center and beyond, anticipating