Transitioning from the Era of Big Data to LLMs_Deriving Insights

Transitioning from the Era of Big Data to LLMs: Deriving Insights Table of Contents Big Data and LLMs: Evolution Over Time Definition and role of Big Data The emergence and advancements of LLMs (Large Language Models) Differences and connections between Big Data and LLMs Challenges of Big Data and the Introduction of LLMs The initial hype around Big Data and infrastructure expansion Limitations caused by insufficient data utilization New possibilities unlocked by the development of LLMs Current State and Limitations of LLMs Service innovations brought by LLMs Gaps between expectations and reality Data privacy and ethical challenges Complexity in technology management A Successful Transition from Big Data to LLMs Creating business value through data Shifting from domain-focused to process-oriented thinking Developing new business models through service innovation Future Directions for Insight Derivation Integrating AI and data utilization Effective approaches to derive actionable insights Establishing real-time decision-making systems powered by LLMs Key Messages Limitations of Big Data: Despite the expansion of data infrastructure, many organizations struggled to translate it into actionable services or insights. Opportunities with LLMs: LLMs have shown remarkable success in natural language processing and leveraging large-scale data, moving beyond infrastructure to create tangible business value. Challenges Ahead: Leveraging LLMs requires addressing technical complexity, ethical considerations, and operational costs. Path to Success: Rather than a technology-centric approach, adopting a problem-solving mindset and developing innovative processes are crucial for success. Conclusion The transition from Big Data to LLMs represents a paradigm shift in how data is utilized. Overcoming the challenges of LLM adoption and building a business-driven strategy will pave the way for greater insights and value creation. This presentation explores these topics with practical examples, offering strategies for using data and AI to shape the future of business.