Foundations of Intelligent Data Analytics

Big data has revolutionized technology by providing previously unheard-of chances to glean insightful information and promote well-informed choices. Big data has traditionally depended significantly on enabling technologies such as statistical modeling, cloud computing, and machine learning. These technologies offer the methods and processing capacity required to effectively handle large datasets. But as data analytics develops further, its importance is moving more and more in the direction of utilizing human traits like experience, judgment, intuition, and value-based decision-making. This inclusion of human-centered perspectives emphasizes that knowledge management is becoming a more important source of big data and data analytics. The Data, Information, Knowledge, and Analytics model provides a strong framework for examining end-users processes in data analytics. This framework begins with raw data, goes through structured information, develops into usable knowledge, and ends with understanding, which utilizes human judgment and intuition. By applying this model, researchers and practitioners can gain a profound understanding of how individuals and organizations make sense of and act on data-driven insights to achieve their goals. The future of harnessing creativity and collaborative processes, similarly, depends on the connection between information management and data analysis. Knowledge management promotes data-driven processes that include the human aspects of decision-making, including ethical considerations, contextual awareness, and critical thinking. Because of this alignment, organizations can address complex problems, adapt to changing conditions, and develop innovative solutions. For example, data-driven insights and human judgment are combined in healthcare-critical decisions with life-changing consequences.