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"AI is Exposing IT Architecture Limitations -- What You Can Do"

This playbook explores the critical questions to ask about how IT architecture can better support high data quality.

The rapid advancement of generative AI is creating urgency for business and technical leaders alike to overcome the barriers to AI adoption. Getting data AI-ready by ensuring it’s current, accurate, and complete is a foundational step. By modernizing their architecture, eliminating silos, and democratizing AI, organizations can confidently embrace an AI-powered future.

AI and models rely on massive volumes of data, most of it unstructured, demanding capabilities collection, storage, processing, and analysis. Today’s data architecture is simply not built for those needs.

How can organizations overcome shortcomings in data architecture?

Download the playbook to learn how organizations are reviewing their data culture – and addressing limitations. Topics include:

• Ensuring your data architecture supports high data quality 
• How business domain experts can contribute to data quality and why is this critical
• What IT leaders are doing to facilitate a data culture that supports data quality

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