Data management, as an interdependent set of practices, is foundational to the success of enterprise AI and machine learning initiatives. In on our AI & Machine Learning, Infrastructure 2023 survey, data quality was the second-most-cited reason for organizational AI/ML project abandonment over the last 12 months. Preliminary data from our AI & Machine Learning, Infrastructure 2024 survey suggests the same. As businesses increasingly look to interact with large language models and adopt GenAI-enabled technologies, the pressure to maintain high-integrity datasets and properly govern data sources has risen. Not only is the quality of data a key concern, but so are the protection and privacy controls for potentially sensitive data such as personally identifiable information and intellectual property.
The term "AI" is often used broadly, but has nuance regarding enterprise implementation. Organizations can build their own AI models, acquire AI-enabled technology, or do both. The Data & Analytics, Data Architecture for AI 2024 survey backing this report suggests that blending both built and bought AI is currently the most popular business option. In all cases, foundational data management practices can accelerate AI outcomes. This survey explores data architecture's role in advancing AI initiatives.
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