Looker vs Power BI vs Tableau — which is best?
Looker excels when you want a governed semantic layer (LookML) and embedded analytics; Power BI wins on Microsoft-stack economics and ubiquity; Tableau on visual analysis depth for analyst teams. The honest answer depends on your stack, licensing, and who consumes the output — we implement all three.
Why do our dashboards disagree with each other?
Metric definitions live in too many places. The fix is a semantic layer: one certified definition of revenue, churn, or utilisation, referenced by every dashboard. We run KPI-certification workshops with finance and ops so the argument happens once, not weekly.
How do you make self-service BI actually work?
Three layers: governed semantic models analysts cannot break, certified curated datasets, and training measured by adoption metrics. Self-service fails when raw tables are exposed; it works when exploration is safe by construction.
Can you fix slow dashboards?
Yes — usually the model, not the tool. Aggregate tables, partition pruning, PDTs or composite models, and pushing logic to the warehouse routinely take dashboards from 30+ seconds to under one second.
Do you build embedded analytics for products?
Yes — customer-facing dashboards embedded in SaaS products via Looker embedding, Power BI Embedded, or Superset, with row-level multi-tenant security designed in from the start.