What I deliver

Data audit — a clear picture of what data you have, where it lives, and what’s missing
Tool recommendations — the right stack for your stage, not the most expensive one
Architecture design — data warehouse, lake, or lakehouse — whatever fits your needs
Roadmap — a phased plan that prioritizes quick wins and builds toward long-term goals
Vendor evaluation — unbiased comparison of tools and platforms for your specific use case

Project examples

Seed-stage SaaS — building from zero

A common situation at the pre-Series A stage: product data lives in the production database, marketing data is in spreadsheets, and there’s no analytics capability to speak of. Leadership is making product and growth decisions based on gut feel, and investor conversations require metrics that don’t yet exist.

This type of engagement starts with a data audit to understand what’s available and what gaps need to be filled, then a tool recommendation that matches the team’s engineering capacity and budget. The typical output is a lightweight modern stack and a 3-phase roadmap — with Phase 1 scoped to deliver investor-ready metrics within weeks, not months.

Series B SaaS — consolidating data chaos

A growing company has accumulated data across ten or more SaaS tools — product analytics, CRM, billing, support, marketing — with no single source of truth. Every team has their own numbers, and cross-functional reporting requires manual exports and reconciliation.

This type of project involves designing a centralized data warehouse architecture, mapping all data sources, and creating an integration plan that brings everything together. The goal is to move from “which number is right?” to a single, trusted platform that all teams can rely on — without requiring a large internal data team to maintain it.

Who this is for

Early-stage SaaS startups that want to get data right from day one before bad habits set in
Growing B2B companies drowning in disconnected tools and spreadsheets with no unified view
Founders and CTOs who know data matters but aren’t sure where to invest first
Teams without a data lead who need strategic guidance without hiring a full-time role

Why Selvi Data

Working with a solo practitioner means no account managers, no handoffs between teams, and no overhead baked into the price. You work directly with the person doing the work, which keeps costs lower and timelines shorter.

I’m not aligned with any particular platform or vendor, so recommendations are based on what’s right for your situation, not what generates the largest implementation. And as an independent, I have no incentive to scope a project larger than it needs to be.

Data Strategy & Architecture