AI doesn’t fail at the algorithm – it fails at the data. EWSolutions builds the clean, governed, AI-ready data foundations that turn machine learning investments into measurable business outcomes, reducing program costs by over 91%.


Before a model can learn, data must be clean, consistent, and governed. EWSolutions builds the unified data infrastructure that eliminates the quality gaps causing 87% of enterprise AI projects to stall or fail before reaching production.
AI trained on ungoverned data produces ungoverned outputs - and in regulated environments, that is a liability. EWSolutions integrates data governance directly into your AI pipeline, ensuring every model is trained on data that is accurate, traceable, and compliant.
Organizations spend millions on AI infrastructure and models, then watch initiatives collapse under poor data quality. EWSolutions has delivered 155+ programs where data foundations translated directly into measurable AI performance - every single time.
AI data strategies that work for a single use case often collapse at enterprise scale. EWSolutions designs AI data management architectures built for scale from day one - so your pilot becomes a platform, not a proof-of-concept graveyard.
Every EWSolutions AI data management engagement is personally directed by David Marco, PhD, whose three decades at the intersection of enterprise data strategy and emerging technology make him the authority organizations turn to when AI initiatives must succeed.
While others offer AI consulting built on enthusiasm, David Marco, PhD brings the foundational expertise that determines whether AI investments deliver or disappear – a track record of 155+ enterprise programs, zero failures, personally led from scoping to delivery.

President & Executive Advisor, EWSolutions
“Mayo Clinic’s Enterprise Data Trust is already realizing palpable success from our cancer center projects, enabling analysis of clinical trial capture and accrual patterns, patient volumes, and clinical trial patient filtering. An infection analytics project standardized data definition and capture of infection-related case data across the enterprise – enabling a single standardized, enterprise-based reporting and analysis environment.”
Mayo Clinic
Enterprise Data Trust - Published in JAMIA, Scholarly Journal of Informatics in Health & Biomedicine
Ronald R. Schrimp, Sr.
SVP & Chief Data and Security Officer, UHG / Ingenix
Ian Rowlands
Vice President of Product Management, ASG
Monica B. Cunningham
Director of Information Resource Management, Harvard Pilgrim HealthCare
Stephen C. Grohovsky
Manager, Consumer/Product Services, Thomson Consumer Electronics
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