The implementation is the strategy

A modern data platform is only as valuable as the implementation that delivers it. Deloitte's 2025 investment management outlook notes that firms seeking to harness AI at scale will find it requires a robust data foundation and strong governance controls first, which means the quality of today's implementation directly determines a firm's readiness for tomorrow's capabilities.

Firms that treat implementation as a project to be completed, rather than a discipline to be practiced, tend to get a platform that works eventually, mostly, for the use cases scoped on day one. Firms that approach each phase with the rigor it requires get something different: a foundation they can actually build on.

That's the difference between deploying a platform and transforming how your operations run.

Key takeaways

Q1. What's the most underestimated phase of a data platform implementation?

Data ingestion and normalization. It consistently surfaces more complexity than expected (e.g., undocumented feeds, inconsistent identifiers, and data quality issues) and problems not resolved here compound downstream.

Q2. Why do modeling and validation stall implementations?

Misaligned definitions between business and technology teams, and validation treated as a one-time event rather than an iterative process throughout the sprint cycle.

Q3. When should governance be established?

Phase 1. Permissioning, data lineage, and access controls retrofitted in Phase 3 are significantly more disruptive and costly than those built in from the start.

Q4. How do you prevent downstream integration surprises at go-live?

Map every consuming system before Phase 3 begins. Agree on formats and delivery schedules early, and test integrations before, and not after go-live.

Q5. What separates firms that get value quickly from those that don't?

Treating implementation as a discipline rather than a project: with structured validation at each phase gate and governance built into the foundation, not bolted on afterward.