Abstract: Augmented analytics leverages artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate and simplify the data analytics process, making actionable insights accessible to users of all skill levels. By integrating these technologies, augmented analytics streamlines data preparation, discovery, and visualization, reducing reliance on specialized technical expertise and enabling broader participation in data-driven decision-making. Key components include automated data wrangling, smart recommendation engines, and natural language generation, which collectively accelerate time-to-insight and enhance data accuracy while minimizing human bias and error. This democratization of analytics empowers organizations to improve data literacy and agility, as business users can interact with data conversationally, uncover hidden patterns, and derive insights more efficiently. Sectors such as finance, healthcare, retail, and HR benefit from faster, more accurate decisions and operational efficiencies. However, challenges remain, including data quality concerns, potential over-reliance on automation, and ethical considerations regarding AI-driven recommendations. As organizations increasingly adopt data-driven cultures, augmented analytics is transforming business intelligence by fostering more inclusive, agile, and knowledge-driven decision-making across all levels of the enterprise.

Keywords: Augmented Analytics, Data Democratization, Artificial Intelligence, Machine Learning, Natural Language Processing, Business Intelligence, Data Visualization, Insight Generation, Data Wrangling, Natural Language Generation, Data-Driven Decision-Making

| DOI: 10.17148/IARJSET.2025.12451