Code & Data Audit for Acquisitions
A codebase that looks functional from the outside can be one discovery sprint from a deal-altering write-down. The code and data audit tells you which it is.
A code and data audit that stopped a nine-figure acquisition
While serving as Senior Enterprise Architect at First American Title Company — then the world's largest title insurer — I was embedded in the M&A division reviewing nine-figure real estate acquisitions. A personal review of code, data architecture, and technology infrastructure at one acquisition target revealed structural problems sufficient to recommend walking away from a deal valued at roughly $100 million. That recommendation was taken.
The code and data audit is distinct from integration architecture planning. Integration asks "can these systems work together?" The code audit asks "is this codebase worth acquiring at all?" — and the data audit asks "is this data asset real?" Many acquisitions are sold on the quality of their data assets: customer records, transaction history, proprietary databases. The audit verifies whether those assets are structured, clean, portable, and actually defensible — or whether they are a story dressed up for a sales process.
First American at the time managed a 4TB SQL Server database containing 100 million US property records across 770 applications. Understanding what well-structured financial data at scale looks like — and what it does not look like — is the foundation of a credible data audit.
Domain knowledge is what makes a code audit credible
A code and data audit is only as useful as the judgment behind it. At LERETA — processing $18 billion in annual property tax escrow volume — I led the $20 million modernization that began with a thorough assessment of the existing codebase and data architecture. That assessment defined the entire modernization scope. A pre-acquisition code audit works exactly the same way: it defines the scope of technical debt the buyer is taking on, and the cost to remediate it.
At HBSGI, I worked with healthcare EDI claims processing under HIPAA ANSI 837, 835, and 997 transaction sets — where PHI segregation, claims integrity, and EDI compliance are structural requirements, not optional controls. Healthcare acquisitions require an auditor who knows what a compliant healthcare data architecture looks like, and can distinguish that from an architecture that merely claims to be compliant. A confidential class-action settlement administration platform I architected from zero to acquisition for $50 million is another reference point: the platform was built specifically to be acquirable — maintainable, well-documented, with a clean data model and no structural debt that would surprise a buyer in diligence.
Six dimensions of code and data review
Codebase Architecture Assessment
Evaluating technical debt, dependency risk, and long-term maintainability across the full stack — identifying the structural problems that do not appear in a product demonstration.
Data Asset Validation
Verifying that the data claimed as an acquisition asset is structured, clean, portable, and actually defensible — or identifying the gap between the sales narrative and the real state of the data.
Security & Compliance Review
PHI, PCI, HIPAA, PII — assessing whether the data security controls are implemented and operational, or just documented. A liability that survives close becomes the acquirer's liability.
Scalability & Performance Audit
Does the architecture hold up at two times or five times current load? Identifying the bottlenecks and ceiling conditions that a growth thesis depends on the architecture clearing.
Technical Debt Quantification
Translating architecture findings into cost-to-remediate figures for deal modeling — so the technical debt in the codebase is reflected in price, not absorbed post-close as a surprise.
Deal-Altering Discovery Protocol
Clear escalation criteria for when a finding crosses from "price adjustment" to "walkaway." The most important output of a code and data audit is sometimes the recommendation not to proceed.
The intellectual capacity and technical maturity of Shawn Livermore exceeded expectations.
What a code and data audit engagement looks like
- Scope alignment (1–2 days) — Define the audit scope relative to the deal thesis. A platform acquisition has a different audit focus than a data asset acquisition. The scope should reflect what the buyer is actually paying for.
- Repository and schema access (1–2 weeks) — Code repository review, database schema analysis, dependency audit, test coverage assessment, and security posture evaluation. Hands-on technical review — not a questionnaire exercise.
- Data asset verification (concurrent) — Independent verification of data quality, structure, portability, and compliance posture. If the acquisition thesis rests on a data asset, the audit confirms whether that asset is real.
- Findings with cost modeling — Written assessment covering confirmed risks, technical debt quantification, compliance gaps, scalability constraints, and cost-to-remediate estimates for deal modeling. Clear escalation criteria if walkaway conditions are present.
- Executive briefing — Findings presentation for deal stakeholders, with full technical backup documentation for the team that will own remediation if the deal proceeds.
If the codebase and data assets of an acquisition target have not been independently verified, they have not been verified at all. Reach out to discuss a code and data audit engagement.
Verify the technology asset before the deal closes
Code and data audit from a fractional CTO who has evaluated nine-figure acquisitions — and whose findings have changed deal decisions at the highest level.
