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.

In an AI-driven ecosystem, data and content need to be fluidly accessible to various systems and agents. Any SaaS platform that feels like a "walled garden," locking content away, will be rejected by power users. The winning platforms will prioritize open, interoperable access to user data.

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.

Data governance is often seen as a cost center. Reframe it as an enabler of revenue by showing how trusted, standardized data reduces the "idea to insight" cycle. This allows executives to make faster, more confident decisions that drive growth and secure buy-in.

Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.

Instead of large, multi-year software rollouts, organizations should break down business objectives (e.g., shifting revenue to digital) into functional needs. This enables a modular, agile approach where technology solves specific problems for individual teams, delivering benefits in weeks, not years.

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.

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.