Data & Analytics Foundations
Turn raw data into decisions, not reports.
Event tracking, modeling, and dashboards you can trust — the foundation for smarter AI, faster decisions, and honest growth metrics.
What we do in Data & Analytics Foundations
Teams drown in dashboards nobody trusts. The root cause is almost always upstream: inconsistent events, siloed warehouses, and definitions that change every quarter.
Solid data foundations improve every function — marketing attribution, product experimentation, finance forecasting, and AI features that need clean context.
Scalix Space builds pragmatic data stacks: right-sized for your stage, documented for your team, and ready to feed automations and models.
What's included
Event Taxonomy
Most popularConsistent tracking plans across web, mobile, and backend.
Warehouse Modeling
dbt-style transforms with clear grain and business definitions.
Executive Dashboards
Single sources of truth for revenue, product, and ops KPIs.
Experimentation Setup
A/B infrastructure with statistical guardrails.
AI Data Pipelines
AI-drivenCurated datasets for RAG, fine-tuning, and feature stores.
Data Governance
Access controls, lineage, and quality monitors.
How we work
Discovery
Audit existing sources, definitions, and decision workflows.
Planning
Prioritize metrics and architecture for your growth stage.
Design
Tracking plan, schema design, and dashboard wireframes.
Development
Instrument, model, and visualize with automated tests.
Testing
Validate numbers against finance and ops ground truth.
Launch & Support
Train teams, document definitions, and iterate on requests.
Who it's for
Investor-grade metrics from seed through Series B.
Unify reporting across business units and acquisitions.
Product analytics that tie features to retention and ARR.
Attribution and LTV models that survive iOS privacy shifts.
De-identified analytics with compliance-aware pipelines.
Learning analytics that respect student privacy.
Built with the right tools
Why teams choose us
Definitions that stick
Metric docs everyone agrees on — finally.
Right-sized stack
No snowflake warehouses for seed-stage startups.
AI-ready by design
Pipelines structured for models and automations downstream.
Self-serve enablement
Your team can answer questions without filing tickets.
Common questions
Most projects run 8–16 weeks for an initial release, with optional retainer phases. We scope timelines in discovery so you know what ships when.
Related services
Ready to start with Data & Analytics Foundations?
Trustworthy event tracking, modeling, and dashboards that power better decisions and smarter AI.
Talk about data foundations