Effective Data Strategy for AI and Big Data Implementation: Insights from Industry Applications

Abstract

Data strategy is critical to the successful implementation of artificial intelligence (AI), big data, and metadata management across various industries. This paper explores how effective data strategies impact AI implementation, public health systems, emergency department (ED) data management, and the banking sector, with an emphasis on big data and metadata. In AI-centric applications, the emerging concept of data-centric AI (DCAI) emphasizes data quality and maintenance, shifting focus from model development to data optimization. In public health, metadata facilitates real-time data integration and interoperability, enabling faster response times and better outcomes. Emergency departments utilize metadata for patient care optimization, while banks implement both offensive and defensive data strategies to ensure compliance and enhance customer experiences. The role of big data and metadata is further explored, particularly in creating data governance frameworks that support AI-driven analytics. Despite its potential, implementing data strategies faces challenges, including data quality, privacy concerns, and regulatory compliance. Limitations such as resource constraints and the evolving nature of data governance highlight the need for continuous improvement in data strategies. This paper also provides an empirical review and research limitations, stressing the importance of refining data strategies to keep pace with technological advancements. In conclusion, robust data strategies are essential for harnessing the full potential of big data and AI, making them critical drivers of innovation and competitive advantage across multiple sectors.