Achieve data readiness for AI with data ingestion

Every company has an AI initiative. Most have fragmented data — churn risk split across Salesforce, Gong, NetSuite, and Zendesk; spend data locked in a finance system no one else can query. AI models can't reason across what they can't see. Before you build agents, you need a data foundation that's current, governed, and accessible. This session covers how to build one with data ingestion and activation, in one platform. WHAT YOU'LL LEARN ▸ Why manual integration flows break every time a schema changes — and why building hundreds of them to feed a data warehouse doesn't scale for AI use cases ▸ How Celigo's adaptive sync replaces dozens of hand-built pipelines with one self-correcting sync that automatically picks up schema changes from source systems like Salesforce ▸ Why unifying operational data in a platform like Snowflake unlocks analytics and AI that no single SaaS app can provide — and what Snowflake's agentic control plane actually enables ▸ What the four requirements for AI-ready data mean in practice: trusted, fresh, governed, and accessible — and why most teams are missing at least two ▸ How reverse ETL closes the loop — turning a churn risk score back into a Salesforce account update, a HubSpot email campaign, or a Slack alert to the renewal team ▸ The five questions to ask before choosing a data ingestion platform — including whether it's genuinely one platform or just stitched-together tools under a common name CHAPTERS 00:00 Intro and housekeeping 01:00 Poll 1: What's your biggest data pain point? 02:05 Why AI mandates expose fragmented data problems 03:15 Use cases: churn risk, win/loss, spend analysis — why they require a data platform 05:00 The problem with manual flows: 176 Salesforce schema changes in one week broke production data 07:00 Celigo adaptive sync: one sync, hundreds of objects, self-correcting schema drift 07:50 Demo: existing HubSpot, NetSuite, and Shopify syncs already running live in Snowflake 09:05 Demo: adding Salesforce in four clicks — accounts, contacts, opportunities 10:35 Configuring merge mode and schema drift policy 11:35 Setting the schedule and launching the historical backfill 12:00 Demo results: tables created, primary keys handled, zero lines of DDL written 13:10 Prabhath Nanisetty (Snowflake): why companies consolidate onto a unified data platform 14:50 Snowflake's data foundation: structured and unstructured data, Horizon catalog 17:00 What unified data unlocks: cross-system scoring, anomaly detection, automated follow-ups 17:40 Semantic layers and Apache Ossie: making data machine-readable across tools 19:00 Churn and renewal risk: combining Salesforce, Gong, NetSuite, and Pendo into one score 21:00 Reverse ETL: activating churn scores in Salesforce, HubSpot, and Slack 22:30 Four requirements for AI-ready data: trusted, fresh, governed, accessible 23:45 Five questions to ask when evaluating a data ingestion platform 25:30 Poll 2: What outcome are you trying to unlock with data ingestion? Data ingestion is how you build the foundation. Reverse ETL is how you act on it. Both in one platform, with shared connectors and governance you can actually audit. 🚀 Never miss Celigo Sessions: https://www.celigo.com/webinar_series/celigo-sessions 👉 Get a demo: https://www.celigo.com/request-a-demo/ #Celigo #ipaas #snowflake #DataIngestion #AIReadyData #ReverseETL #datapipelines #CeligoSessions