Unified data platform across 40+ gas stations
- 20K+
- transactions processed in a single day
Self-Service Analytics Platform
When every data question goes through the analytics team, the analytics team becomes a bottleneck and everyone else waits.
Self-service analytics gives department heads and operational managers the ability to answer their own data questions without submitting a request to the data team. Instead of waiting for an analyst to pull a custom report, the marketing manager can filter by campaign, the operations manager can slice by region, and the product manager can look at feature usage by customer segment, all without writing SQL or waiting a week. RaftLabs builds self-service analytics platforms on Metabase, Power BI, and custom front ends, with a clean, well-documented data layer that non-technical users can query safely without producing incorrect numbers or accessing data they shouldn't. Row-level security, guided exploration, and the curated data model that makes self-service analytics work in practice rather than in theory.
Pre-built data model exposing business entities (customers, orders, products) in plain language, no SQL required
Row-level security ensuring each user sees only the data their role permits
Guided exploration with suggested filters, dimensions, and metrics for each data domain
Saved query and dashboard library so department teams can build on work already done rather than starting from scratch
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
Are department managers waiting days for custom reports from the data team when the underlying data is already in your warehouse and the question could be answered in minutes with the right tool?
When non-technical users have access to raw database tables, are they producing correct analyses or finding ways to generate numbers that confirm what they already believe?
Short answer
RaftLabs builds self-service analytics platforms on Metabase, Power BI, and custom front ends, with a curated semantic layer, certified metrics, and row-level security so department teams answer their own questions without waiting for the analytics team. A first set of 3 to 5 data domains launches as a validated v1 in 6 to 10 weeks from around $25K to $45K, then grows across more domains toward a full platform. Fixed cost, agreed before development starts.
Key takeaways
- RaftLabs builds self-service analytics platforms on Metabase, Power BI, and custom front ends with a curated semantic layer
- A first set of 3 to 5 data domains launches as a validated v1 in 6 to 10 weeks, then grows across more domains
- A fuller platform with a custom front end and data catalogue is a 10 to 16 week build
- Pricing is land-and-expand: a first data domain starts around $25K to $45K and grows into the $70K to $130K range for a full platform
- Certified datasets carry the metrics leadership reports on; everything outside the certified set stays open for ad-hoc exploration
- Row-level security is enforced at the data layer so the boundary cannot be bypassed via a copied share link
Trusted by
Your analytics team's queue fills with requests that are each simple but together eat the week: filter last month's sales by region, show active customers by product tier, break support tickets down by category. Each one takes an analyst half an hour. Each manager who asked waits two days. The queue quietly crowds out the deeper work only that team can do.
A self-service analytics platform does not remove the analytics team. It changes what they spend the week on. Instead of pulling the same standard reports on request, they build and own the curated data model that managers query themselves. Their time moves to the work that needs real expertise: investigating anomalies, defining new metrics, and reading what self-service surfaces.
The payoff is real. So is the reason an ungoverned rollout backfires.
- of finance executives call self-service analytics a driver of employee productivity
- 49%
- Gartner, 2022
- of data and analytics leaders rank data literacy among their top organizational challenges
- 47%
- Gartner
Hand people raw tables with low data literacy and you get confident wrong answers, not faster decisions. Closing the gap between that upside and that barrier is the whole job of a curated, governed platform.
Capabilities
What we build
- 01Curated semantic data model
Business-friendly semantic layer between the raw data warehouse and the analytics tool, exposing entities in plain language: Customers and Orders rather than dim_customer and fct_orders. Metrics like Total Revenue are pre-calculated named measures, so users get the correct number without knowing to exclude refunds. Enforced join paths and field-level documentation prevent the classic wrong-join, wrong-number failure.
- Built with
- dbt Semantic Layer · Metabase · Power BI
- 02Row-level security and access control
Row-level security enforced at the data layer, so the security boundary is the data model itself, not a UI toggle a copied share link can bypass. Sensitive domains like HR compensation and customer PII are restricted by role. SSO sync removes access automatically when someone leaves, and every query is audit-logged for security reviews.
- Built with
- Metabase · Power BI · Looker · Okta · Azure AD · Google Workspace
- 03Guided exploration and discovery
Guided exploration reduces the blank-canvas problem where non-technical users open the analytics tool with no intuition for where to start. Each data domain opens with suggested filters, one-click metrics, and 10 to 15 published template queries, such as last month's churn by customer tier. Advanced filters stay behind a toggle, so new users see a clean interface and experienced users keep full capability.
- 04Custom chart and dashboard creation
Chart builder for non-technical users: pick a metric, a dimension, and a date range, and the right chart type is recommended automatically. Specialist charts cover cohort heatmaps, MRR waterfalls, and conversion funnels. Dashboards compose from saved questions with dashboard-level filters that cascade to every chart at once, and sharing supports public links, signed-in access that respects row-level security, product embeds, and scheduled email delivery.
- Built with
- Metabase · Power BI · React
- 05Saved query and dashboard library
Organisation-wide library of saved questions, dashboards, and template queries, curated by the analytics team so every domain has a useful starting point rather than an empty tool. Content is tagged by domain and type, with a freshness indicator showing when it was last verified. Any saved query can be forked as the starting point for a new analysis, then promoted to the shared library.
- 06Data documentation and data catalogue
Data catalogue providing in-platform documentation, so users never leave the analytics tool to understand what they are querying. Every table and field carries a plain-language description, known caveats, and the business logic behind derived fields such as Churned. A business glossary links terms like Net Revenue Retention to the exact fields that implement them, and a lineage view traces each derived field back to its source system.
Governed self-service: freedom where it helps, guardrails where it counts
Governance is a dial, not a switch. Turn it all the way toward freedom and every team ships its own version of revenue. Turn it all the way toward control and you have rebuilt the ticket queue you set out to kill. The design job is to fix the definitions that must be shared and leave everything else open.
We do that with certified datasets. The metrics leadership reports on live in the semantic layer, defined once, owned by the team that owns the domain: finance owns revenue, growth owns activation, support owns resolution time. A metric defined once means the marketing manager and the board deck read the same number. Everything outside the certified set stays open for ad-hoc work, labelled un-certified so nobody mistakes a draft for a source of truth.
The semantic layer is where that governance lives, and each tool draws it in a different place. That choice shapes how far metric definitions travel.
The semantic-layer landscape
| Tool | Semantic / metric layer | Where it fits |
|---|---|---|
| Metabase | Lightweight model and glossary built in the UI, no code | Fast self-service exploration for business teams |
| Power BI | DAX measures over a tabular model | Microsoft-stack teams and executive reporting |
| Looker | LookML, a version-controlled code layer | Central metric governance across many teams |
| Tableau | Published data sources and the Tableau data model | Visual analysis and dashboard-heavy cultures |
| dbt | dbt Semantic Layer defines metrics once in the warehouse | One definition every downstream tool inherits |
A domain is not ready for self-service the day the data lands. Readiness arrives when the metrics, joins, and access rules are settled enough that a non-technical query cannot quietly produce a wrong number. Run each one through this before you open it up.
Is a data domain ready for self-service?
- A named owner: one team accountable for the domain's metric definitions and access rules
- Certified metrics: the handful of numbers leadership reports on, defined once with agreed formulas
- Clean join paths: the safe ways tables connect, so a query cannot silently double-count
- Row-level rules: who sees which rows, expressed in the data model rather than a dashboard toggle
- Plain-language names: Customers and Orders, not dim_customer and fct_orders
- Documented caveats: the known gotchas for each field, written where the query happens
Most teams start with one data domain as a v1, roughly $25K to $45K, launched in 6 to 10 weeks, then expand domain by domain toward a full platform in the $70K to $130K range. Fixed cost, agreed before development starts.
Have a self-service analytics project?
Tell us which teams need data access, what questions they're currently waiting to have answered, and what data sources you have. We'll scope the platform and give you a fixed cost.
Related business intelligence services
Business Intelligence and Analytics, full BI capability overview
Executive Dashboard Development, executive-level dashboards for leadership teams
KPI Reporting System, structured metric reporting with period comparison
Automated Reporting Software, automated scheduled report delivery
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USAEntrepreneur at Aggie Technologies
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
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Read moreFrequently asked questions
Direct database access exposes raw tables with technical field names, no predefined metric calculations, no row-level security, and no join guidance. Users who don't understand the data model produce incorrect queries, join tables incorrectly, and generate misleading numbers they have no way to validate. Self-service analytics provides a curated layer on top of the database: plain-language tables and fields, predefined metrics, enforced join paths, and row-level security. Users answer questions correctly without writing SQL and without accessing data they shouldn't see.
The curated data model is the primary defence, if users can only query named metrics with predefined formulas, they can't accidentally apply the wrong formula. Predefined join paths prevent incorrect table joins that produce Cartesian products or double-counting. Row-level security prevents querying data outside the user's scope. Beyond the technical guardrails, data documentation helps users understand what each metric measures and when it applies. For high-stakes analyses used in board reports or financial decisions, a review step by the data team is built into the workflow.
A first set of 3 to 5 data domains launches as a validated v1 in 6 to 10 weeks: a curated semantic model, certified metrics, row-level security, and a Metabase or Power BI deployment. From there it grows. A fuller platform with a custom front end, an advanced data catalogue, and more business domains is a 10 to 16 week build. Timeline depends on the number of data domains in scope and the state of the underlying data warehouse.
Most self-service analytics platforms allow SQL access alongside the no-code interface for users who need it. In Metabase, users with SQL permissions can write native queries against the warehouse alongside the visual query builder. In Power BI, DAX and M queries are accessible to power users. The distinction between no-code users and SQL users is a permission setting. Advanced SQL users work against the same curated data layer, not raw source tables, so they still benefit from the predefined metric calculations and documented join paths.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Self-Service Analytics Platform in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.