Modern organizations must embrace data governance methodologies to remain competitive and compliant; of late, artificial intelligence (AI) has become a must-have for organizations. AI TRiSM (trust, risk, and security management) is the key concept that ensures AI models are governed and trustworthy. In this course, you will explore the role of AI TRiSM in risk management. Then you will focus on the pillars of the AI TRiSM framework, advantages of AI TRiSM, and how AI can be leveraged for informed decision-making. Next, you will discover how organizations can take advantage of AI TRiSM and examine the fundamental factors that make AI TRiSM successful. Finally, you will delve into how to achieve success with AI TRiSM and implement an AI strategy in your organization.

Converged infrastructure is represented by systems containing preconfigured software and hardware in a single integrated software-defined architecture. Composability is a system design principle that focuses on the interrelationships of components. In this course, you will delve into converged and composable systems, beginning with an introduction to converged and composable systems, the benefits of converged and composable systems, and the elements of a converged system. Then you will focus on the modularity and interoperability in composable systems. You will learn about integrating diverse technologies into unified systems, business considerations for system strategies, composability building blocks, and adaptability in modern business. Finally, you will explore convergence and composability in cloud computing, challenges in converged and composable systems, composable systems and leadership, and use cases for converged systems.

In today's fast-paced business landscape, data is the driving force behind strategic decision-making. Navigating the latest trends in data has never been more important or offered greater potential benefits for savvy leaders. In this course, you will be introduced to adaptive governance, data governance and agility, and unlocking the value of data through monetization. Then you will explore the role of artificial intelligence (AI)-driven storytelling, decision-making with AI insights, augmented data management, and integrating AI and automation into data management. Next, you will discover democratizing real-time data, tools and technologies for real-time insights, and ethical considerations for data practices. You will learn about data privacy and trust and the role of leadership in data adoption, and dive into data innovation case studies. Finally, you will examine continuous learning in the data landscape and emerging data trends.

Data is the driving force behind strategic decision-making, and the most successful organizations are the ones that recognize and champion understanding the health and performance of their data systems. They recognize the need for knowledge and skills in order to harness the full potential of data in our rapidly evolving digital world. In this course, you will unlock data observability, beginning with an introduction to data observability, benefits and challenges of data observability and the data observability framework. Then you will dive a little deeper and compare data observability to data quality. Next, you will explore data observability tools, different types of data observability, and data observability best practices. Finally, you will discover data observability use cases and the importance of having a data observability strategy.

The modern leader needs to gain insights into cutting-edge technologies and strategies. With those insights, the opportunities are practically limitless. In this course, you will be introduced to the power of practical data fabric, beginning with an exploration of data fabric, the benefits of data fabric, data mesh, and the relationship between data fabric and data mesh. Then you will discover how data fabric addresses data silos and complexity. Next, you will investigate data fabric architecture, the advantages of a data fabric architecture, and the role of data fabric in analytics. Finally, you will learn about operationalizing data fabric, the challenges and limitations of data fabric, and data fabric use cases.