Using AI to Transform Modern Data Platforms: Bridging the Gap between Data and Business Users

Authors

  • Ashrith Reddy Mekala Cloudwick Inc, USA Author

DOI:

https://doi.org/10.32628/CSEIT251112148

Keywords:

Data Democratization, Natural Language Query Processing, Metadata Automation, Conversational Analytics, AI Governance

Abstract

The integration of artificial intelligence in modern data platforms has fundamentally transformed how organizations interact with their data assets. This transformation encompasses several key innovations: natural language interfaces that enable direct SQL query generation, AI-powered business catalogs that automate metadata management, and conversational analytics systems that facilitate intuitive data exploration. These advancements have democratized data access across organizational hierarchies, reducing dependency on specialized technical teams while enhancing operational efficiency. The evolution from traditional rule-based systems to sophisticated neural network architectures has enabled more accurate query processing, improved schema mapping, and context-aware interactions. Additionally, the implementation of active metadata management and automated governance frameworks has strengthened data quality and compliance measures. As these technologies continue to mature, organizations face both opportunities and challenges in scaling their AI implementations while maintaining security, privacy, and model explainability.

Downloads

Download data is not yet available.

References

Rama Ryali, "Today's Imperative for Data Democratization," GetRightData Resources, 2022. Available: https://www.getrightdata.com/resources/todays-imperative-for-data-democratization

Josh Howard et al., "The State of Enterprise AI: How Early Adopters are Driving Success," 2024. Available: https://www.databricks.com/blog/state-enterprise-ai-how-early-adopters-are-driving-success

Muhammad Shahzaib Baig, "Natural Language to SQL Queries: A Review," 2022. Available: https://www.researchgate.net/publication/379839268_Natural_Language_to_SQL_Queries_A_Review

Neelu Nihalani et al., "Natural language Interface for Database: A Brief review," 2011. Available: https://www.researchgate.net/publication/266863909_Natural_language_Interface_for_Database_A_Brief_review

Alation Blog, "Why Metadata Maturity Matters for AI-Ready Data: Key Points from Gartner," 2024. Available: https://www.alation.com/blog/metadata-maturity-ai-ready-data-gartner/

Gartner Research, "Market Guide for Active Metadata Management," 2021. Available: https://www.gartner.com/en/documents/4004082

MarketsandMarkets, "Conversational AI Market by Technology (Supervised Learning, Reinforcement Learning, Sentiment Analysis, ASR, Speech to Text, Data Mining, Voice Activity Detection), Conversational Agents (Generative AI, AI Bots, IVA) - Global Forecast to 2030," 2024. Available: https://www.marketsandmarkets.com/Market-Reports/conversational-ai-market-49043506.html

Coherent Solutions, "NLP in Business Intelligence: 7 Success Stories, Benefits, and Future Trends," 2024. Available: https://www.coherentsolutions.com/insights/nlp-in-business-intelligence-7-success-stories-benefits-and-future-trends

Deloitte, "Becoming an AI-fueled organization," 2024. Available: https://www2.deloitte.com/content/dam/insights/articles/US144384_CIR-State-of-AI-4th-edition/DI_CIR_State-of-AI-4th-edition.pdf

Aleksandra Sidorowicz, "AI in data analytics: opportunities and challenges," 2024. Available: https://www.future-processing.com/blog/artificial-intelligence-in-data-analytics-opportunities-and-challenges

Downloads

Published

31-01-2025

Issue

Section

Research Articles

How to Cite

Using AI to Transform Modern Data Platforms: Bridging the Gap between Data and Business Users. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1391-1398. https://doi.org/10.32628/CSEIT251112148