Data Analysis Services

Answers leadership can act on this quarter

Reproducible queries, documented assumptions, and visualizations tied to decisions.

Analysis fails when metrics definitions drift or dashboards hide caveats.

We align on KPI dictionaries, build transparent SQL/notebooks, and ship dashboards your team can refresh without mysticism.

Discovery workshops: questions, stakeholders, and success metrics first.
Data modeling: facts/dimensions or pragmatic denormalized views for speed.
Visualization: Looker Studio, Metabase, Power BI, or custom—matched to your stack.
Quality checks: null rates, duplicates, and segment sanity tests baked in.
Handover: definitions doc, query repo, and refresh schedules.

Single source of truth for definitions across teams.

Annotations that explain spikes—not just pretty charts.

Weekly demos, shared backlog, and release notes you can forward to stakeholders.

Secrets out of repos, TLS by default, and sensible auth/session patterns for apps.

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FAQs

Yes—start with controlled models; migrate to a warehouse when volume demands.

This line focuses on analytics; see AI & ML services for modeling-heavy work.

Milestone-based: discovery, prototype dashboard, production hardening.

PII minimization, role-based access, and logs without sensitive payloads.

Retainers for new questions, pipeline tweaks, and training.