The field of data analysis is undergoing a significant transformation with the integration of Large Language Models (LLMs) and Knowledge Graphs (KGs). This convergence enables the creation of dynamic, collaborative analytical ecosystems that can extract deep insights from complex, heterogeneous data. Recent research has focused on developing innovative methods for multi-dimensional data analysis, fault cause identification, and knowledge representation. Notable advancements include the use of LLM agents to extract product data from unstructured data and construct KGs in real-time, as well as the development of frameworks that enhance FMEA reusability and achieve state-of-the-art performance in fault cause identification. The integration of LLMs and KGs has also led to the creation of knowledge-driven frameworks that integrate standardized clinical terminology with graph databases, enabling multi-hop reasoning and ensuring terminological consistency. Furthermore, novel knowledge modeling frameworks have been proposed, which elevate domains to first-class elements of conceptual representation, enabling context-aware reasoning and cross-domain analogy. A comprehensive survey has also been conducted, providing an overview of recent progress in LLM-empowered knowledge graph construction and outlining key trends and future research directions. In addition to data analysis, the convergence of LLMs and KGs is also being explored in other fields, such as multi-agent systems, autonomous agentic AI systems, scientific research and analysis, agentic systems and web search, urban intelligence and collaborative perception, and large language models. These developments have the potential to transform various applications, including smart city management, collaborative workflows, and autonomous data science. Overall, the convergence of LLMs and KGs is enabling the creation of more robust, trustworthy, and efficient analytical ecosystems, and is expected to continue to be a major area of research in the coming years.