Suhas Jangoan

Accelerating your Enterprise Data for AI

Abstract:

Decades of corporate investment in data warehouses, lakes, and business intelligence have left enterprises with a massive paradox: they have data everywhere, but very few AI initiatives that actually work in production. The true bottleneck of modern enterprise AI is not the modeling layer, but rather the data platform itself; with an estimated 80% of project timelines consumed by data preparation, engineering plumbing, and access limitations.

In this keynote, we explore what it truly takes to transition from legacy data architectures to an AI-native ecosystem where data is accessible, trustworthy, contextual, and composable. The session introduces a comprehensive four-stage maturity model that maps the evolutionary journey from raw operational ingestion (Ground) and trusted dimensional modeling (Curated) to AI-native semantic layers and high-dimensional vector stores (Intelligent). Finally, we will look toward the frontier of data engineering: designing "Agentic-Ready," self-describing data architectures optimized for autonomous LLM workers using the emerging Model Context Protocol (MCP). Data leaders and engineers will leave with a strategic blueprint on how to turn their core infrastructure into a callable, governed API surface that successfully fuels next-generation AI workloads.

Profile:

Suhas Jangoan is an accomplished Senior Data Engineer at Zendesk and a recognized expert in data architecture, artificial intelligence, and machine learning platforms. An alumnus of the University of Texas at Dallas with a degree in Computer Science, he has spent over a decade constructing high-performance data infrastructures that drive business intelligence and optimize consumer experiences. His leadership background includes managing data engineering teams and spearheading emerging technology programs at EAB Inc., where his data-driven strategies for revolutionizing higher education and boosting college enrollments were prominently featured in SiliconIndia.

Suhas is heavily involved in the global engineering community, currently serving as the Treasurer for the IEEE Richmond Section (2025–2026). As a top-ranked mentor for Data Engineering on ADPList and an active mentor on MentorCruise, he is deeply committed to guiding the next generation of IT professionals. An active researcher, his scholarly publications on Google Scholar span critical advancements in explainable AI, privacy-preserving AI/ML application architectures, predictive maintenance, and dynamic resource allocation. He also regularly shares industry insights as a published technical writer on platforms like DZone.