Turn Raw Data Into
Decisions You Trust
Stop the "why don't these numbers match?" meetings. We build governed analytics foundations, trusted dashboards, and metrics models that hold up in the boardroom.
Analytics Built as a System
We cover the full chain: measurement → platform → modeling → dashboards. No more broken funnels.
Measurement & instrumentation
If you don’t capture the right data, analytics becomes a guessing game.
- Measurement plan tied to business outcomes (not random event spam)
- Event taxonomy, naming rules, and property standards
- Funnel coverage: acquisition → activation → retention → monetization
- Identity strategy, deduplication, and source-of-truth rules
- Attribution-ready capture with clear limitations documented
Data platform foundation
Reliable ingestion, storage, and access patterns for reporting.
- Source mapping: CRM, payments, product events, marketing, ops
- Warehouse/lakehouse-ready structure with environment separation
- Incremental loads, backfills, late-arriving data handling
- Secure access boundaries (roles, service accounts, least privilege)
- Cost controls: partitioning strategies and retention rules
Modeling & Governance
This is where analytics becomes trustworthy and repeatable.
- Dimensional modeling for BI performance (facts + dimensions)
- Metric dictionary: definitions, filters, grain, and usage notes
- Semantic layer patterns so metrics are defined once
- Data quality tests on critical models (nulls, uniqueness)
- Documentation that’s searchable and not trapped in someone’s brain
Dashboards & Decision Systems
Dashboards built around decisions, not vanity visuals.
- Executive dashboard: a tight view of health, growth, and risk
- Operator dashboards: what to do today (alerts, queues, backlogs)
- Drill-down paths that answer “why” without opening 10 tabs
- Visualization rules that prevent misleading charts
- Role-based views so the right people see the right detail level
Advanced Analytics
Only after the foundation is solid.
- Cohort and retention analysis with stable definitions
- Segmentation that’s actionable (who to target, what to fix)
- Anomaly detection and monitoring for key KPIs
- Forecasting and capacity signals (demand, inventory, support)
- Experiment measurement: guardrails and success metrics
Privacy & Compliance
Analytics that won’t create headaches later.
- PII inventory: what you collect, where it lives, and who can access it
- Data minimization and retention rules (delete what you don’t need)
- Audit-friendly access patterns and logging strategy
- Governance processes: approvals for new metrics and data sharing
- Alignment to industry constraints based on your situation
From Data to Decisions
A clear path to reliable intelligence. We don’t just dump dashboards on you.
Define decisions and KPIs
We start from decisions you need to make weekly/monthly, then design analytics backward from that.
Instrument and validate data
We standardize tracking and verify event quality so analysis is based on reality, not broken telemetry.
Centralize and normalize
We bring scattered systems into a consistent warehouse layer with clear ownership and access rules.
Build governed metrics
We implement a model that performs well in BI and add tests so breaking changes don’t go unnoticed.
Dashboards that drive action
We ship a small set of dashboards designed around specific questions and drill-down paths.
Keep it healthy over time
Monitoring, documentation, and a change process so analytics stays reliable after the project ends.
What You Walk Away With
Reusable assets and ownership definitions, not just one-off reports.
Analytics Blueprint
- KPI map tied to decisions and owners
- Measurement plan + tracking taxonomy
- Source-to-metric lineage
- Security and access model overview
Data Assets
- Modeled datasets for BI (facts/dimensions)
- Metric dictionary + definitions
- Automated quality checks on key tables
- Dashboards (exec + operator + team)
Operating Model
- Documentation and change workflow
- Dashboard QA checklist and refresh rules
- Access review and governance checklist
- Monthly analytics maintenance plan
Our Technical Stack
Selected for your data volume and budget.
Common Questions
Can you fix existing dashboards instead of rebuilding everything?
Yes. We’ll do a trust audit first: data sources, definitions, and breakpoints. Then we keep what’s salvageable and rebuild only what’s causing wrong answers or wasted time.
How do you stop people from creating conflicting metrics?
We implement a metric dictionary + semantic layer approach, define ownership, and set a lightweight change process so metric changes are reviewed and documented.
Do you handle privacy constraints for US businesses?
We design analytics with privacy risk management in mind (access boundaries, minimization, retention), and we can align workstreams to your industry requirements when applicable.
What’s a realistic timeline?
A focused foundation + core dashboards often takes 3–6 weeks. A broader data platform plus governed metrics across teams is typically 6–12+ weeks depending on sources and data cleanliness.
Want analytics that ends the debates?
Share your top 3 decisions you need to make and your current data sources. We’ll map out a plan to get you actionable insights in weeks, not months.