Nikhil Gaekwad

Identity resolution sits at the core of modern data and AI. As signals disappear and privacy constraints tighten, the ability to unify fragmented customer records across partners directly impacts data quality, analytics accuracy, and model performance. In this session, we show how Databricks Clean Rooms enable organizations to build a trusted view of customer data by resolving identities across datasets without exposing raw PII. Join us for a technical deep dive with LiveRamp as we demonstrate how a durable identity backbone supports accurate, privacy-safe matching across organizations. You will learn how to standardize fragmented data into interoperable models, improve match rates, and unlock higher-fidelity analytics through governed collaboration using Clean Rooms. We will also cover practical implementation patterns, including enforcing privacy controls and scaling compliant collaboration across multiple partners. The result is a foundation for identity-first analytics that improves your marketing outcomes while maintaining strong privacy standards.

Databricks Clean Rooms make privacy-safe collaboration possible for data, analytics, and AI — across clouds and platforms. Built on Delta Sharing, Clean Rooms enable organizations to securely share and analyze data together in a governed, isolated environment — without ever exposing raw data. In this session, you’ll learn how to get started with Databricks Clean Rooms and unlock advanced use cases including: - Cross-platform collaboration and joint analytics - Training machine learning and AI models - Enforcing custom privacy policies - Analyzing unstructured data - Incorporating proprietary libraries in Python and SQL notebooks - Auditing clean room activity for compliance Whether you're a data scientist, engineer or data leader, this session will equip you to drive high-value collaboration while maintaining full control over data privacy and governance.