The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.
For marketers running time-sensitive promotions, the traditional ETL process of moving data to a lakehouse for analysis is too slow. By the time insights on campaign performance are available, the opportunity to adjust tactics (like changing a discount for the second half of a day-long sale) has already passed, directly impacting revenue and customer experience.
A logical data management layer acts as middleware, disintermediating business users from the underlying IT systems. This data abstraction allows business teams to access data and move quickly to meet market demands, while IT can modernize its infrastructure (e.g., migrating to the cloud) at its own pace without disrupting business consumption.
Denodo's logical approach is significantly faster because it fetches only the specific query results needed for an analysis, rather than physically moving entire datasets into a central repository. This is analogous to getting a single cup of water from a pitcher instead of carrying the entire heavy pitcher, explaining a 75% reduction in integration time.
Despite promises of a single source of truth, modern data platforms like Snowflake are often deployed for specific departments (e.g., marketing, finance), creating larger, more entrenched silos. This decentralization paradox persists because different business functions like analytics and operations require purpose-built data repositories, preventing true enterprise-wide consolidation.