Using Your Lakehouse as a Customer Data Platform
Webinar Speaker: James Kobielus, Senior Research Director, Data Management
Date: Wednesday, April 10, 2024
Time: 9:00 a.m. PT, 12:00 p.m. ET
Customer data is the heart of any enterprise’s operations. Much of this is event data drawn from real-time applications in customer service, multichannel experience personalization, and other operational use cases. However, enterprises find themselves increasingly challenged to collect, store, and process this and other customer data. Data is complex. Its volumes continue to grow. It’s distributed and fragmented across operational silos. Ultimately, its quality may be insufficient, rendering it unusable for mission-critical applications.
Enterprises are increasingly centralizing customer data in cloud-based lakehouses. To serve today’s real-time, customer-focused applications, lakehouses provide a unified storage layer, integration pipeline, and processing engine in support of operational business intelligence, predictive analytics, machine learning, and other mission-critical applications. The lakehouse enables decision support directly on the freshest and most complete customer data, and it makes customer data analytics more accessible and scalable across the engagement life cycle.
Join TDWI’s senior research director James Kobielus on this webinar to explore the importance of the data lakehouse as the foundation of the enterprise customer data stack. Kobielus will be joined by guests Spencer Cook, Databricks senior solutions architect, and Eric Dodds, RudderStack head of product marketing. After their opening presentations, Kobielus, Cook, and Dodds will discuss:
- How should enterprises synchronize, collect, integrate, transform, and consolidate customer data into their lakehouses?
- How should enterprises use their data lakehouses to build, deploy, and optimize customer engagement use cases of artificial intelligence (AI) and machine learning (ML)?
- What are best practices for developing AI/ML applications that leverage the enterprise data lakehouse in support of different stages of the multichannel customer engagement journey?
- How should enterprises build AI/ML-driven recommendation engines on the lakehouse to drive improved targeting and personalization of the customer journey?
- What are best practices for building, training, deploying, and optimizing AI/ML models on the customer data lakehouse?
- What is reverse ETL and how can it leverage the lakehouse to synchronize customer data to applications in sales, service, marketing, and other operational applications?
Guest Speakers
Spencer Cook
Senior Solutions Architect
Databricks
Spencer Cook, M.S., is a data professional with experience delivering end-to-end analytics solutions in the cloud to iconic brands. Since 2021, Spencer has been a financial services solutions architect at Databricks focused on revolutionizing the industry with lakehouse architecture.
Eric Dodds
Head of Product Marketing
RudderStack
Eric Dodds is the head of product marketing at RudderStack, a leading lakehouse native CDP. He also leads management of their internal data stack, including the data pipelines and infrastructure that drive marketing reporting and activation. He has over a decade of experience working with and implementing customer data infrastructure and has advised companies like BMW, WeWork, and a host of start-ups on marketing strategy and data-related projects.
James Kobielus