Explaining the fundamentals of generative AI, large language models, and their applications in business settings. The text focuses on the history, technology, and use cases of LLMs, emphasizing their potential to revolutionize enterprise data management and analysis. It highlights the importance of data governance and security in these contexts, recommending the use of cloud data platforms to provide a secure and scalable environment for gen AI initiatives.

Data mining, why is it important and various concepts, including data preprocessing, data warehousing, OLAP, association rule mining, classification, prediction, cluster analysis, and graph mining. It also discusses the applications of data mining in different domains, such as business, finance, healthcare, and intrusion detection.

Comprehensively exploring various aspects of data analytics. The text covers topics such as data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. The book emphasizes practical applications and utilizes real-world examples to illustrate different data analytics techniques. It incorporates a range of models and algorithms, including statistical methods, machine learning techniques, and optimization methods.

Master Data Management (MDM) and Customer Data Integration (CDI) in a global enterprise setting. The book explores the challenges and opportunities of integrating customer data across various systems within an organization, covering topics such as data governance, data quality, security, and compliance. It also discusses the architecture, design, and implementation of CDI solutions, including Data Hubs, matching and linking algorithms, data synchronization, and the role of information quality in achieving a single, consistent view of customer data.

Data architectures, which are the blueprints for how businesses manage their data. It specifically covers popular data architectures, including the relational data warehouse, data lake, modern data warehouse, data fabric, data lakehouse, and data mesh.

Data Management Body of Knowledge (DMBOK) 2.0, a guide for data management professionals. It explains the principles and best practices for managing and governing data within organizations. The text covers various aspects of data management, including data governance, data quality, data security, data integration, data warehousing, and data modeling. It also discusses the importance of organizational change management and communication for successful data management initiatives. Finally, the text addresses the rise of Big Data and data science, and their impact on data management practices.