Emerging Data Trends Competency (Intermediate Level)

  • 27m
  • 27 questions
The Emerging Data Trends Competency (Intermediate Level) benchmark measures your knowledge of modern data governance key concepts. You will be evaluated on your recognition of practical data fabric, data observability benefits, challenges, tools, best practices, and use cases, as well as your knowledge of converged and composable systems and AI TRiSM. A learner who scores high on this benchmark demonstrates that they have the necessary skills and knowledge to be a data-savvy visionary. They can drive innovation, operational excellence, and competitive advantage through the strategic use of data and emerging trends, ensuring their organizations thrive in an increasingly data-centric world.

Topics covered

  • describe how AI TRiSM leverages AI for informed decision-making
  • describe how data fabric can be operationalized in an organization
  • describe how organizations can achieve success with AI TRiSM
  • describe key ways in which cloud computing enables convergence and composability
  • describe reasons, issues, and considerations for implementing an AI strategy
  • describe the core components of a data fabric architecture
  • describe the key elements of a converged system
  • describe the purpose of an observability strategy and how to develop one
  • describe the three types of observability (infrastructure, data, and machine learning)
  • identify key concepts surrounding the integration of diverse technologies into a unified system
  • identify strategies for integrating AI and automation into data management processes
  • identify the building blocks for enabling composability in systems
  • identify the ways data fabric addresses the challenges of data silos and complexity
  • outline case studies showcasing organizations that have successfully embraced data innovation
  • outline popular data observability tools
  • outline similarities and differences between data observability and data quality
  • outline the importance and key features of AI TRiSM for organizations
  • outline the importance of adaptive data governance in dynamic business landscapes
  • outline the pillars of the AI TRiSM framework
  • outline the role of artificial intelligence (AI)-driven storytelling in making data insights actionable
  • provide an overview of best practices for data observability
  • provide an overview of how augmented analytics enhances decision-making through AI-powered insights
  • provide an overview of key issues for championing composable systems
  • provide an overview of strategies for balancing data governance with agility
  • provide an overview of the benefits offered by data fabric
  • provide an overview of the role of leadership in driving data adoption across an organization
  • provide an overview of the significance of democratizing real-time data across an organization