Tony Cahill, the mind behind Crystal Onyx, dives deep into why data readiness is the critical bottleneck preventing successful AI implementations and shares how his platform transforms months of data preparation into weeks.

• Organizations struggle with data readiness when implementing AI projects 
• The AI project cycle consists of five steps: problem identification, data acquisition, modeling, output, and evaluation
• 42% of organizations identify data availability and quality as a top challenge for AI implementation
• Poor data quality costs enterprise companies approximately $10-12 million annually
• Crystal Onyx creates a virtual representation of data across various storage systems without physically moving files
• The platform can scan millions of files per hour to create a comprehensive map of available information
• A customer reduced their data preparation time from 10-12 months to just 8 weeks using Crystal Onyx
• The system allows for custom metadata, data classification, and governance without disrupting existing workflows
• "AI isn't a solution, it's a force multiplier. If your data goals and processes are broken, AI will amplify that chaos"
• Organizations should align technology decisions with business objectives and start with small, low-risk pilots

For more information on Crystal Onyx, visit crystallonyx.com or email [email protected].

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