Business Intelligence - Looker
Looker is powerful and expensive.
Most teams use a fraction of what they pay for.
We build production-grade LookML data models, governed Explores, and operational dashboards that sales, finance, and ops actually open - so your Looker investment stops being a line item nobody defends.
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Operators and teams we've worked with
Most Looker deployments stall because the data model was never finished
Looker's power comes from LookML - the semantic layer between your warehouse tables and every chart a user touches. When that layer is incomplete or built by someone who left, every Explore becomes a trust problem. Sales pulls pipeline numbers that don't match finance. The same field appears four times with four definitions. Looker's native features - derived tables, symmetric aggregates, datagroups for caching - exist to prevent this, but they require deliberate architecture most mid-market teams never get to.
Revenue Institute comes in at the LookML layer, not the dashboard layer. We audit your views and models, fix broken joins, enforce naming conventions, and build the core semantic objects your business runs on - ARR, pipeline coverage, headcount. We also configure access controls, user attributes, and folder structure so the right people see the right data. The result is a Looker instance a non-technical operator can use without calling an analyst for every question.
What we do with Looker
What we build inside your Looker instance
LookML model architecture and cleanup
We audit your views, models, and Explores, remove broken joins, and rebuild the semantic layer around your business objects - clean naming, fanout prevention with Looker's symmetric aggregate functions, and a model your team can safely extend.
Persistent derived tables and caching strategy
Slow dashboards kill adoption. We implement persistent derived tables for heavy aggregations, configure datagroups for rebuild schedules, and align caching with warehouse query costs - so finance gets morning numbers fast without burning your BigQuery or Snowflake budget.
Governed Explores with access controls
We configure user attributes and access filters so field-level and row-level security match your governance policy. Sales reps see their accounts, regional managers their region, executives everything. Looker's access grant and filter system handles this natively - set up correctly, not wide open.
Operational dashboards for revenue and ops teams
We build the dashboards operators need - pipeline by stage and close date, ARR waterfall, headcount by department, support volume by product - using Looker's filters, cross-filtering, and drill paths. Live views tied to governed Explores, not static screenshots.
Looker Alerts and scheduled delivery setup
Looker's native Alerts and scheduled delivery are underused by almost every team we meet. We configure threshold-based alerts for the metrics that matter - pipeline drop, churn risk, quota attainment - and schedule delivery to Slack or email automatically.
Embedded analytics and Looker API integration
For firms that want to surface data inside a client portal or internal reporting tool, we implement Looker's embedded analytics using signed URLs or the Looker API, configure iframe embedding with proper authentication, and build client-facing LookML content that is scoped and clean.
Our framework
How a Looker engagement with us runs
Audit and architecture
We connect to your Looker instance and warehouse, review every model and view file, map your Explores against your business questions, and find the gaps. You get a written findings document and prioritized plan before any new LookML.
Build and govern
We build or rebuild the LookML objects, derived tables, and access controls to the agreed architecture. Every dimension and measure gets a label, and every Explore is tested against real queries. We document conventions so your team extends it cleanly.
Train and hand off
We run working sessions with your data team on how the model is structured, how to add views and joins correctly, and how to use Looker's content validator and SQL runner. You leave with a model you own and can maintain.
Why Looker succeeds or fails at the semantic layer, not the dashboard layer
Looker's architecture differs from Tableau or Power BI, and that difference is both its strength and the source of most failures. Every chart is generated by querying an Explore, and every Explore is defined by LookML - a modeling language describing how your tables relate and what logic applies to each field. Built correctly, every user pulls from the same definitions. Built poorly, you get what most mid-market Looker customers have: Explores nobody trusts and a data team stuck on one-off questions.
The most common structural failure is fanout - joining a many-to-many or one-to-many relationship without Looker's symmetric aggregate functions, which overcounts measure values. A pipeline report that double-counts opportunities because the Explore joins opportunities to activities without proper fanout handling is the textbook example. Looker has native tooling to prevent this, but the model builder has to apply the right pattern. Most implementations skip it because the data looks correct on small samples and the error only surfaces at scale - by then it is in front of the CFO and trust is gone.
What production Looker actually looks like in a mid-market operation
A production Looker environment has a handful of well-governed Explores - typically five to fifteen - covering the core business domains: revenue, pipeline, product usage, support, and headcount. Each has a clear owner, documented field definitions, and access controls matching the company's data policy. Persistent derived tables handle aggregations that would otherwise require a full warehouse scan on every load, and datagroups control when they rebuild so the bill stays predictable. The folder structure separates certified dashboards from personal work so new hires know where to start.
Getting there from a typical mid-market instance - stood up two years ago, touched by four analysts, never fully governed - takes deliberate architectural work. It is not a configuration project; it is a data modeling project that uses Looker as its expression layer. Revenue Institute approaches it that way: we start with your business logic, build the semantic model that represents it, then surface it through Looker's Explores. The platform becomes reliable because the foundation underneath is finally correct.
We're vendor-agnostic
Other Business Intelligence & Analytics platforms we specialize in
Not sure Looker is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Looker questions, answered
We already have a Looker instance. Can you fix what we have or do we start over?
Almost always we fix what you have. Starting a LookML model from scratch is rarely necessary. We audit the existing views, models, and Explores, identify what is salvageable, and refactor from there. The exception is when the original model was built against a data structure that no longer exists or when the technical debt is so severe that refactoring costs more than rebuilding - we will tell you honestly which situation you are in after the audit.
Our dashboards are slow. Is that a Looker problem or a warehouse problem?
Usually both, and they compound each other. Looker can generate inefficient SQL when Explores are joined incorrectly or when there are no persistent derived tables for heavy aggregations. At the same time, a poorly partitioned BigQuery table or a Snowflake virtual warehouse that is too small will make even well-written LookML slow. We diagnose both sides - Looker's query history and the warehouse's query profiler - and address the root cause rather than just adding caching on top of a broken model.
How long does a typical Looker implementation or rescue take?
A focused audit and core model rebuild for a mid-market company typically runs four to eight weeks depending on the number of data sources, the complexity of your existing model, and how many business domains we are covering. Embedding projects or API integrations add time. We scope precisely after the audit so you know what you are committing to before work begins.
Do we need a dedicated data engineer on our side to work with you?
It helps but it is not required. We need someone who can answer business logic questions - what counts as a closed deal, how you define active users, what the ARR calculation is - and someone with warehouse credentials. If you have an analyst or a part-time data person, that is usually enough. We handle the LookML writing, the Explore configuration, and the access control setup ourselves.
We use Looker with BigQuery. Does that change how you approach the model?
Yes, in practical ways. BigQuery's partitioning and clustering behavior affects how we write derived tables and set datagroup rebuild schedules to control scan costs. We also use BigQuery-specific SQL functions where Looker's LookML dialect supports them. The same principles apply to Snowflake or Redshift but the specifics of query optimization and cost management differ by warehouse, and we adjust accordingly.
What is the difference between Looker and Looker Studio? Which one should we be on?
Looker is the governed semantic layer and Explore-based BI platform that lives at looker.com. Looker Studio is Google's free, connector-based report builder - a different product with a similar name. For mid-market companies that need a governed data model, row-level security, and a shared semantic layer across teams, Looker is the right tool. Looker Studio is fine for simple, self-contained reports but it has no equivalent to LookML and no real governance layer. If you are trying to decide, we can walk through your use case and give you a direct answer.
Can you help us figure out what Looker features we are paying for but not using?
Yes, and this is often one of the most valuable parts of an engagement. Looker licenses include features like Alerts, Schedules, the Looker API, embedded analytics, and Actions that most teams never configure. We review your contract entitlements against your actual usage and identify where you are leaving paid capability on the table - whether that is automated delivery, API-driven workflows, or embedding that could replace a separate tool you are paying for.
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