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Business intelligence projects fail for a predictable set of reasons. The technical team builds dashboards that answer the questions they thought the business was asking. The business gets dashboards that look impressive but don't show the metric they actually care about. Or the dashboards show the right metric but use a different definition than the one the finance team uses, so the numbers never reconcile. Or the dashboards take 45 seconds to load because nobody optimized the underlying queries.
Good data visualization is not about aesthetics. It is about accuracy, agreement, and usability. The metric on the dashboard must match the metric the business uses. The definition must be documented and agreed across departments. The dashboard must load fast enough to be used in a meeting. The data must be fresh enough to be relevant to the decision being made.
We build visualization solutions that treat these requirements as non-negotiable — starting with the semantic layer, not the dashboard canvas.
Looker's semantic layer (LookML) is the foundation of a well-built BI implementation. LookML defines dimensions, measures, and relationships between data entities in a way that is reusable, testable, and governed — so that every dashboard using the "Monthly Revenue" metric is using the same definition, and when that definition changes, every dashboard reflects the change automatically.
We build LookML models that represent the business's data domain accurately: properly typed dimensions, correctly aggregated measures, join logic that reflects the actual data relationships, and access filters that enforce row-level security for multi-tenant or department-restricted data.
Dashboard design follows the business questions: what does the person in this role need to know, at what frequency, with what level of granularity? We translate business requirements into dashboard specifications before opening the visualization tool — not the other way around.
We implement dashboards in Looker (for organizations with a Looker license) or Looker Studio (for lighter requirements), with consistent formatting, appropriate chart type selection for each data story, and filter and drill-through interactivity that makes the dashboard useful for exploration, not just reporting.
Slow dashboards don't get used. We optimize visualization performance at the query level: BigQuery clustering and partitioning aligned with dashboard filter patterns, Looker persistent derived tables for expensive aggregations, and caching configuration appropriate for the data freshness requirements.
Structured interviews with the stakeholders who will use the dashboards. We document what decisions they make, what data they need, what metrics matter, and how they currently get that information — before designing anything.
Every metric that will appear in the visualization layer defined in writing: calculation, data source, refresh frequency, and any business rules or filters. Agreed across all stakeholder groups before implementation begins.
LookML model built against the agreed metric definitions and the data warehouse schema. Models reviewed for correctness against source data before dashboards are built on top of them.
Dashboards built against signed-off dashboard specifications. Each dashboard reviewed with the business stakeholder responsible for it before delivery — not after a multi-week build cycle.
Dashboard load time tested under realistic concurrent usage. Query optimization applied where needed. Documentation of metric definitions, data sources, and refresh schedules. User training for self-service exploration.