Services (and what you actually get)

Who it's for

Companies that know something's wrong but don't know what to fix first. You have data infrastructure (warehouse, pipelines, dashboards), but it's slow, expensive, or unreliable.

Problems it solves

• •"We don't trust our numbers" → Identify data quality issues and metric inconsistencies
• •"Our infrastructure is too slow/brittle" → Diagnose pipeline failures, query bottlenecks, cost spikes
• •"We have data but aren't using it" → Pinpoint why dashboards sit unused and what's blocking adoption

What you get

• •Architecture review: data flow diagram, stack assessment, bottleneck analysis
• •Cost analysis: warehouse spend breakdown with optimization opportunities
• •Data quality report: issues found, root causes, severity ratings
• •Prioritized roadmap: what to fix first, effort estimates, expected impact

How it works

1. Kickoff call: understand your stack, pains, and goals (1 hour)
2. Access & discovery: review your warehouse, pipelines, BI dashboards, code (async)
3. Analysis & recommendations: benchmark performance, identify issues, prioritize fixes (1 week)
4. Readout: present findings and roadmap (1 hour)

Who it's for

Teams where different departments report different numbers for the same metric. If "active users" or "MRR" means something different to sales, product, and finance, you need this.

Problems it solves

• •"We don't trust our numbers" → Establish canonical metric definitions everyone agrees on
• •"We have data but aren't using it" → Enable self-service analytics with certified metrics
• •Stop the weekly "what does this metric mean?" meetings. Define each one once, with edge cases and an owner.

What you get

• •Metric catalog: documented definitions for your top 15–30 business metrics
• •Semantic layer implementation: metrics-as-code (using your BI tool, dbt, or custom framework)
• •Governance process: how to request new metrics, approve changes, deprecate old ones
• •Team training: how to use and maintain the semantic layer

How it works

1. Discovery: audit existing metrics and identify conflicts (1 week)
2. Alignment workshops: work with stakeholders to agree on canonical definitions (1–2 weeks)
3. Implementation: build semantic layer in your stack (1–2 weeks)
4. Rollout & enablement: train team, migrate dashboards, establish processes (1 week)

Who it's for

Teams where data pipelines break regularly, data is hours/days stale, or your first data hire is drowning in maintenance. Also for greenfield: you're ready to build pipelines the right way.

Problems it solves

• •"Our infrastructure is too slow/brittle" → Build reliable, fast, monitored pipelines
• •"We have data but aren't using it" → Get fresh data into the warehouse so dashboards are trustworthy
• •Reduce firefighting: stop spending 50% of your week debugging broken jobs

What you get

• •Pipeline design: data flow architecture, orchestration strategy, incremental vs full refresh logic
• •Implementation: working pipelines (using your orchestrator, dbt, or custom code)
• •Monitoring & alerting: data quality tests, freshness checks, error notifications
• •Runbooks: how to debug failures, rerun jobs, add new sources

How it works

1. Requirements: map data sources, frequency, transformations, SLAs (1 week)
2. Design: architect pipeline flow, choose tooling, plan incremental logic (1 week)
3. Build: implement and test pipelines (2–4 weeks)
4. Deploy & monitor: production rollout, monitoring setup, knowledge transfer (1–2 weeks)

Who it's for

Teams that need to go from raw data to dashboards people actually check. Or teams with dashboards that exist but nobody uses because they're slow, confusing, or untrustworthy.

Problems it solves

• •"We have data but aren't using it" → Build dashboards so good people check them daily
• •"We don't trust our numbers" → Connect dashboards to certified metrics for consistency
• •Enable self-service: let teams explore data without waiting for analysts

What you get

• •BI tool setup: configure your platform (Looker, Metabase, Tableau, etc.) with best practices
• •Dashboard design & build: 3–10 core dashboards tailored to your business (exec, product, sales, ops)
• •Performance optimization: fast load times via aggregations, caching, query tuning
• •User training: how to use dashboards, build new ones, and maintain them

How it works

1. Discovery: understand who needs what insights, existing pain points (1 week)
2. Design: wireframe dashboards, align on metrics and layout (1 week)
3. Build: implement dashboards, optimize queries, test (2–3 weeks)
4. Launch & training: roll out to users, train teams, document (1 week)