Summary
This guide presents a self-assessment framework for organizations to evaluate their readiness for artificial intelligence (AI) initiatives. It outlines four key areas: Assess, Consolidate, Secure, and Curate. The assessment begins with mapping existing file repositories, including legacy systems and cloud storage, to understand the current data landscape. It emphasizes the importance of reviewing file types, sizes, and access levels to determine data visibility and migration needs. The guide also discusses the costs associated with fragmented file architectures and the opportunity costs of not consolidating data. The consolidation phase involves building a business case for data unification and developing a migration strategy. Security considerations include re-evaluating disaster recovery and compliance strategies. Finally, the curation process focuses on profiling the data set, ensuring its quality, and determining its suitability for AI models. This structured approach aims to help organizations establish a trusted data foundation for successful AI implementation.
