Executives worry about long timelines, but with the right approach, value comes quickly. By starting with a high-priority domain (such as customer 360, sales ledger, or supply KPIs), organisations often see governed, production-ready datasets in as little as 6-10 weeks. Full foundation scale - spanning multiple domains and feeding advanced analytics, typically arrives in 3-6 months. The secret is prioritising by business impact, not technical completeness, so leadership sees tangible results early while the foundation continues to grow in parallel.

A recurring pain in forums is analysts spending days reconciling numbers from different reports. This stems from the absence of a single source of truth. By building a governed semantic layer and standardising metrics definitions, teams stop debating “which number is correct” and focus on decisions. High-volume ad-hoc requests can be productised into datasets with owners and SLAs. Over time, ticket volumes fall, reconciliation drops, and business users gain faster, more confident access to insights. The result is both speed and trust in reporting.

AI readiness is more than dumping data into a model. Practitioners are focused on three operational capabilities: (a) data quality and lineage so models train on known-good signals; (b) access & privacy controls so sensitive attributes never leak into model training or retrieval; and (c) retrieval & indexing design (semantic indexes, RAG-safe retrieval) for production agents. Start by certifying training datasets (tests, bias checks, provenance), enforce masking/tokenization where needed, and build retrieval layers that respect PII guardrails and prompt-redaction. Also, treat production ML with the same SLOs as pipelines - track model drift, data drift, and set alerting.

Many teams report unexpected cost spikes and cutover headaches during migration (billing surprises, broken refreshes, connector edge-cases). The practical approach is a phased “migration factory”: identify high-value flows, run them in parallel (dual-run), run deterministic parity checks, and stage cutovers so you can rollback safely. Use backfill + reconciliation gates, enforce CI/CD for pipeline changes, and instrument cost telemetry early (per-feature cost dashboards, query-level billing alerts). Automate tests that verify not only schema parity but also business-level metrics (row counts, aggregates) before decommissioning old systems. Finally, treat migration as a product: dedicate sprints, owners, and a rollback playbook so operational teams aren’t firefighting during the cutover.