October 2026 Summaries

Migrating between BI platforms such as Tableau, Power BI, Looker, and Metabase is presented as an opportunity to improve analytics rather than simply replicate existing dashboards. The recommended approach begins with auditing dashboard usage by business function, archiving inactive items rather than deleting them, and checking subscriptions to avoid disrupting automated reporting. Organizations should also standardize duplicated or conflicting calculations by moving logic from dashboards into a managed data layer such as dbt or Metabase Data Studio, creating a reliable semantic layer with shared metric definitions. Dashboard rebuilding can be accelerated with AI tools, screenshots, MCP integrations, and version-controlled automation, though key metrics should be validated with end users against the prior system. Clear, ongoing stakeholder communication is emphasized throughout the transition, including advance notice, progress updates, adoption monitoring, support, and reminders before retiring the old platform.

Semantic layers translate raw data into standardized business definitions for metrics, dimensions, and rules, helping people, BI tools, and AI systems produce consistent answers to questions about concepts such as revenue, active users, and customer churn. While they traditionally required lengthy, centralized upfront modeling, AI and modern analytics tools are making them more iterative: candidate definitions can be inferred from existing queries, expressed in plain language, reviewed by domain experts, version-controlled, and updated quickly. The growing use of AI analytics raises the risk of ambiguous or hallucinated interpretations when definitions are absent, while cited research and benchmarks suggest that strong data curation and curated semantic layers can improve AI accuracy. Current approaches increasingly favor a shared core of governed definitions with flexibility for team-specific views, but organizations still face choices over whether layers should be built upfront or discovered through usage, centralized or distributed, generic or model-specific, and delivered as standalone infrastructure or within BI platforms.