The traditional approach of building a central data lake fails because data is often stale by the time migration is complete. The modern solution is a 'zero copy' framework that connects to data where it lives. This eliminates data drift and provides real-time intelligence without endless, costly migrations.
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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.
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.
Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.
Many leaders mistakenly halt AI adoption while waiting for perfect data governance. This is a strategic error. Organizations should immediately identify and implement the hundreds of high-value generative AI use cases that require no access to proprietary data, creating immediate wins while larger data initiatives continue.
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.
To enable AI tools like Cursor to write accurate SQL queries with minimal prompting, data teams must build a "semantic layer." This file, often a structured JSON, acts as a translation layer defining business logic, tables, and metrics, dramatically improving the AI's zero-shot query generation ability.
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.
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.
According to Salesforce's AI chief, the primary challenge for large companies deploying AI is harmonizing data across siloed departments, like sales and marketing. AI cannot operate effectively without connected, unified data, making data integration the crucial first step before any advanced AI implementation.
