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?
Read this white paper to learn:
Why AI-ready, quality data is critical to AI initiatives
How to create a roadmap for AI strategy (and common pitfalls to avoid)
Best practices for aligning people, process, and technology in support of AI projects
This strategy guide is designed for data management experts who want to learn how a data intelligence platform can help them prepare their data AI, alongside their people and processes, to lead AI initiatives that drive results.
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.
Webinar Registration
As AI becomes increasingly integral to business operations, effective governance is essential to ensure innovation remains safe, auditable, and compliant. According to IDC research, the two top concerns for organizations investing in generative AI solutions are: the lack of GenAI skills or expertise within the organization and the accuracy of AI-generated outcomes. Building AI literacy and leveraging accurate, trusted data are crucial for driving successful AI initiatives.