Business Intelligence - Looker
Looker is powerful and expensive.
Most teams use ten percent of it.
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.
Get your free AI roadmap.
See exactly where AI and automation fit your Looker stack - delivered to your inbox. No call required.
Free, personalized roadmap. We never share your data.
$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build with
Most Looker deployments stall because the data model was never finished
Looker's power comes from LookML - the semantic layer that sits between your raw warehouse tables and every chart a user touches. When that layer is incomplete, inconsistent, or built by someone who left the company, every Explore becomes a trust problem. Sales pulls pipeline numbers that don't match finance. Marketing reports a CAC that conflicts with the CFO's spreadsheet. Developers add dimensions without governance, so the same field appears four times with four different definitions. Looker's native features - derived tables, persistent derived tables, symmetric aggregates, datagroups for caching - exist precisely to prevent this, but they require deliberate architecture. Most mid-market teams never get there because the initial implementation was scoped too thin or handed to an analyst who was also running reports full-time.
Revenue Institute comes in at the LookML layer, not the dashboard layer. We audit your existing views and models, identify broken or redundant joins, enforce field naming conventions, and build the core semantic objects your business actually needs - ARR, pipeline coverage, headcount, whatever your operating model runs on. We also configure Looker's access controls, user attributes, and folder structure so the right people see the right data without a free-for-all in the shared space. The result is a Looker instance that 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 every existing view file, model file, and Explore definition, remove redundant or broken joins, and rebuild the semantic layer around your actual business objects. That means clean dimension and measure naming, proper fanout prevention using Looker's symmetric aggregate functions, and a model structure your team can extend without breaking things downstream.
Persistent derived tables and caching strategy
Slow dashboards kill adoption faster than anything else. We implement persistent derived tables for your heaviest aggregations, configure datagroups to control rebuild schedules, and align caching strategy with your warehouse query costs - so finance gets their morning numbers fast without burning your BigQuery or Snowflake budget on redundant full scans.
Governed Explores with access controls
We configure user attributes and access filters so field-level and row-level security work the way your data governance policy requires. Sales reps see their own accounts. Regional managers see their region. Executives see everything. Looker's access grant and access filter system handles this natively - we make sure it is actually set up correctly instead of left wide open.
Operational dashboards for revenue and ops teams
We build the dashboards your operators actually need - pipeline by stage and close date, ARR waterfall, headcount by department, support ticket volume by product area - using Looker's dashboard filters, cross-filtering, and drill paths. These are not static screenshots. They are live, filterable views tied to the governed Explores we built.
Looker Alerts and scheduled delivery setup
Looker's native Alerts and scheduled Look delivery are underused by almost every team we encounter. We configure threshold-based alerts for the metrics that matter - pipeline drop, churn risk signals, quota attainment - and set up scheduled dashboard delivery to Slack or email so stakeholders get the numbers without logging in every morning.
Embedded analytics and Looker API integration
For software companies that want to surface data inside their own product, we implement Looker's embedded analytics using signed URLs or the Looker API, configure iframe embedding with proper user authentication, and build the LookML content that makes sense in a customer-facing context - scoped, clean, and without exposing internal business data.
Our framework
How a Looker engagement with us runs
Audit and architecture
We connect to your Looker instance and your warehouse, review every model and view file, map your existing Explores against your actual business questions, and identify the gaps and structural problems. You get a written findings document with a prioritized build plan before we write a single line of new LookML.
Build and govern
We build or rebuild the LookML objects, derived tables, and access controls according to the agreed architecture. Every dimension and measure gets a description and a label. Every Explore gets tested against real queries before it goes to users. We document naming conventions so your internal team can extend the model without creating new debt.
Train and hand off
We run working sessions with your analysts and data team on how the model is structured, how to add new views and joins correctly, and how to use Looker's content validator and SQL runner to catch errors before they reach end users. You leave with a model you own and a team that can maintain it.
Why Looker succeeds or fails at the semantic layer, not the dashboard layer
Looker's architecture is fundamentally different from tools like Tableau or Power BI, and that difference is both its strength and the source of most implementation failures. In Looker, every chart and dashboard is generated by querying an Explore, and every Explore is defined by LookML - a proprietary modeling language that describes how your database tables relate to each other and what business logic applies to each field. When that layer is built correctly, every user in the company is pulling from the same definitions. When it is built poorly or left incomplete, you get what most mid-market Looker customers actually have: a collection of Explores that nobody trusts, a shared space full of abandoned Looks, and a data team that spends most of its time answering one-off questions instead of building anything durable.
The most common structural failure we see is fanout - a situation where joining a many-to-many or one-to-many relationship without Looker's symmetric aggregate functions causes measure values to be overcounted. A sales pipeline report that double-counts opportunities because the Explore joins the opportunities table to the activities table without proper fanout handling is a textbook example. Looker has native tooling to prevent this, but it requires the person building the model to understand the problem and apply the right pattern. Most initial implementations skip this because the data looks correct on small samples and the error only becomes obvious at scale. By then, the dashboard has been shared with the CFO and the trust problem is already baked in.
What production Looker actually looks like in a mid-market operation
A production Looker environment for a mid-market company has a small number of well-governed Explores - typically five to fifteen - that cover the core business domains: revenue, pipeline, product usage, support, and headcount. Each Explore has a clear owner, documented field definitions, and access controls that match the company's data policy. Persistent derived tables handle the aggregations that would otherwise require a full warehouse scan on every dashboard load, and datagroups control when those tables rebuild so the warehouse bill stays predictable. The content folder structure separates certified dashboards from personal and team-level work so new employees know where to start without wading through three years of abandoned experiments.
Getting to that state from a typical mid-market Looker instance - one that was stood up two years ago, touched by four different analysts, and never fully governed - takes deliberate architectural work. It is not a configuration project. It is a data modeling project that happens to use Looker as its expression layer. Revenue Institute approaches it that way: we start with your business logic, build the semantic model that represents it accurately, and then surface that model through Looker's Explores and dashboards. The platform becomes reliable not because we added more dashboards but because the foundation underneath them is finally correct.
We're vendor-agnostic
Other Business Intelligence 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.
Make Looker actually earn its license fee.
Tell us your two biggest bottlenecks and we'll send back a custom Looker implementation blueprint - by email, no call required.
- A specific plan for your Looker stack, not a generic pitch
- Reviewed by an operator, delivered to your inbox
- No call required, no obligation
Get your free AI roadmap.
Free and personalized. We never share your data.
Prefer to talk first? Book a strategy call.