Datasheet
Data leaders are under mounting pressure to launch AI use cases. What steps can they take to ensure their data is AI-ready and primed to fuel real business results?
Download this checklist to learn:
The boxes to check for data discovery for AI
The data governance capabilities to put in place to fuel AI
How to prepare for the future of AI use cases
This guide is a companion to the blog, AI Governance Checklist: Is Your Business AI-Ready? It is designed for data management professionals curious to learn how they can prepare their data for AI use cases.
Whitepaper
Leaders are under mounting pressure to implement AI that drives business results. How can they ensure their data is ready to fuel AI models that succeed?
Webinar On-Demand
In today’s rapidly evolving digital landscape, effective data governance is critical for organizations striving to manage diverse and numerous data types. Ensuring the trustworthiness of AI/ML models requires robust governance of both inputs and outputs.
Customer Case Study
This same-day shopping and delivery service faced challenges with data quality, trust, and concurrent processing issues in their backend Postgres databases. To address these, the company’s data leadership implemented a comprehensive data modernization strategy. They migrated their data to the Snowflake data cloud and adopted Alation as the front end for their data for its user-friendly interface. Additionally, they implemented the Monte Carlo data observability platform to ensure data quality with real-time lineage and alerts. This integrated solution provides the delivery company with reliable, high-quality data for business reporting and AI modeling, generating the insights needed to deliver value to their customers, shoppers, and retail partners.