Building an Analytics Engine to IPO

An internal proposal written for ConnectWise leadership making the case for investing in a centralized analytics engine to support the company’s path from Thoma Bravo ownership to IPO or strategic acquisition. This document captures the real operational tension between scrappy internal teams and the scrutiny required for institutional-grade data.

Context

ConnectWise had already demonstrated enough data maturity to secure the Thoma Bravo investment. But the next step — IPO or strategic acquisition — would demand a higher bar: reproducible metrics, auditable lineage, and institutional trust in the numbers. Monthly board meetings and a pandemic had already exposed the pain points.

The Investment Framework

The proposal breaks investment into three dimensions — a pattern reusable for any analytics pitch:

1. Time — Phased approach: data loading, then reporting automation, then planning/goal-setting/accountability. Each phase builds on the last. See 06-reference/concepts/systems-over-goals — the phasing is a system, not just a project plan.
2. Talent — Internal team (Data Services, FP&A, Partner Success) plus executive steering. Notably, the author advocated for investing in internal talent over consultants, while suggesting Fishtown Analytics (creators of dbt) if outside help was needed. This is the 06-reference/concepts/analytics-as-craft philosophy in action — build the muscle internally.
3. Treasure — Snowflake (already paid), Fivetran for data loading, dbt for transformation. The total cost was remarkably low relative to the value produced.

Problem Scoping

Four key challenges, each generalizable beyond ConnectWise:

• Competing internal definitions — Different teams defining the same metric differently. The classic “what counts as a customer?” problem.
• Handling M&A and system migrations — Building a data model independent of source systems so acquisitions can be integrated by mapping new data into existing shapes. This is directly relevant to 06-reference/2026-04-03-snowflake-rapid-growth-doordash and how fast-growing companies manage data complexity.
• PII/PCI handling — Default to not loading sensitive data; use hashing for identifiers when needed.
• Balancing consistency vs. flexibility — “Once reported, always reported” vs. the need to evolve how the business is analyzed.

Project Sequencing

1. Bookings and Billings (revenue recognition fundamentals)
2. Renewal mechanics (retention, the lifeblood of SaaS)
3. Sales pipeline modeling and marketing attribution

This sequencing — revenue first, retention second, growth third — is a reusable pattern for any SaaS analytics build. See 06-reference/2026-04-03-saas-metrics-that-matter.

Reusable Patterns

• The “analytics engine” framing — Positioning data infrastructure as an engine that needs a tune-up, not a project with an end date. This is working ON the business, not just IN it (06-reference/2026-04-03-the-e-myth-revisited).
• Source-system-independent data model — A transformation layer that decouples reporting from operational systems. When systems change (M&A, migrations), only the mapping needs updating.
• Phased investment pitch — Time, Talent, Treasure as a framework for proposing analytics investment to non-technical leadership.
• Internal-first talent strategy — “Invest in our talent” philosophy, with specific external consultants (Fishtown/dbt Labs) as the accelerator if needed. Relevant to 01-projects/phdata/index and the consulting credibility story — knowing when to bring in outside help and when to build internally.

Consulting Credibility

This document demonstrates the kind of strategic analytics thinking that 01-projects/phdata/career-transition represents — the ability to scope an analytics program end-to-end, frame it for executive audiences, and connect data infrastructure decisions to business outcomes (IPO readiness, board confidence, M&A integration).