Automating Enterprise Data Discovery: Modern Data Catalogs Powered by Generative AI & LLMs

Title: Automating Enterprise Data Discovery: Modern Data Catalogs Powered by Generative AI & LLMs Abstract: Enterprise organizations struggle with data discovery—50% of data professionals' time is wasted on failed discovery tasks, costing organizations $1.7M per 100 employees annually. This talk presents the 4C Framework, an intelligent architecture for automating enterprise data discovery using Generative AI and Large Language Models. We explore how AI-powered data catalogs address root causes spanning technical challenges (legacy infrastructure, data silos, scale complexity), process failures (manual curation, outdated documentation, poor lineage tracking), and organizational barriers (siloed teams, skills gaps, governance weaknesses). The framework comprises four pillars: Comprehensive: Automated discovery across all enterprise data assets with real-time schema evolution tracking Collaborative: Human-AI partnership leveraging agentic AI orchestration for organizational excellence Contextual: LLM-powered semantic understanding of business meaning and context Cognitive: Intelligent learning patterns for predictive recommendations and continuous improvement We discuss the foundational technical stack including data ingestion layers, multi-modal storage (vector embeddings, graph relationships, metadata repositories), intelligent processing engines, agentic AI platforms, and real-time alerting—enabling organizations to reduce data discovery time from weeks to hours. The talk concludes with implementation challenges including AI accuracy, scale optimization, data security, governance frameworks, and ROI timelines, with practical solutions for enterprise adoption.
Who is this presentation for?
Enterprise Data Leaders — CTOs, VPs of Data/Engineering, Data Architecture teams Data Engineers & Architects — Technical professionals building data platforms and catalogs Enterprise Decision Makers — Business leaders evaluating AI/data solutions and ROI
Prerequisite knowledge:
Enterprise data architecture concepts (databases, data warehouses, data lakes) Understanding of data governance and metadata management challenges Basic knowledge of LLMs and Generative AI capabilities (not deep expertise required) Awareness of data discovery pain points in large organizations Exposure to cloud platforms (AWS, GCP, Snowflake, BigQuery) is helpful but not required No advanced machine learning expertise needed—this is architecture and strategy focused.
What you'll learn?
Technical Insights: The 4C Framework architecture for AI-powered data catalogs (Comprehensive, Collaborative, Contextual, Cognitive) How to implement intelligent data ingestion layers, multi-modal storage systems, and agentic AI orchestration Real-time metadata extraction, semantic understanding, and intelligent recommendations at scale Modern technology stack options (Kafka, Spark, LLMs, Vector DBs, GraphQL, Elasticsearch) Strategic & Operational: Root causes of enterprise data discovery crisis and how AI addresses each Decision framework for when and where to invest in AI-powered data solutions How to build trust with risk, security, and legal teams from the outset Practical solutions for AI accuracy, data security, governance, and ROI challenges Takeaway Value: Reusable architecture patterns for your own enterprise implementation Real metrics: reducing discovery time from weeks to hours, cutting costs and improving team productivity Change management strategies for team adoption and organizational transformation

Vipin Kataria is an IEEE Senior Member and Distinguished Fellow with 21+ years of experience in enterprise cloud platforms, machine learning, and AI systems. As Senior Lead Architect - Data/ML at Picarro, he designs cloud data solutions and ML-driven analytics for environmental monitoring and hazardous gas detection, processing real-time IoT sensor data for Fortune 500 companies. His career spans leading technology companies where he built scalable solutions: at Intel Corporation, he architected automated diagnostic systems for XMM modem platforms; at Amazon, he developed enterprise-grade cloud solutions; and at Aricent Technologies and TCS, he delivered telecommunications and enterprise software platforms. This diverse experience across hardware, cloud, and enterprise domains uniquely positions him to solve complex technical challenges. A sought-after speaker in the AI and data science community, Vipin has presented at leading conferences including DSS Miami and IEEE, and participated as a panelist at the Applied AI Summit. As Chapter Lead for AI Collective's South Bay Chapter, he guides a global community of 100,000+ AI practitioners and enthusiasts, fostering collaboration and knowledge sharing in artificial intelligence. He actively contributes to the AI research community as an author and peer-reviewer of cutting-edge research papers, and serves as a judge for international AI awards and hackathons, helping evaluate breakthrough innovations. He's currently writing "The Agentic Enterprise," which explores how AI agents transform marketing, customer experience, and enterprise operations. His expertise spans modern data architecture, advanced analytics pipelines, and next-generation AI systems that drive business transformation. Based in the San Francisco Bay Area, California, he continues advancing cloud architecture, machine learning, and IoT technologies through both industry practice and thought leadership.