Before the Term Sheet: Auditing the Target’s Data Estate in US Mid-Market Acquisitions

Acquisition diligence in the US mid-market is usually disciplined about financial statements, customer concentration, contracts, and tax exposure. It is far less disciplined about the target’s data estate. That gap is understandable: data is intangible, technical, and easy to defer. It is also expensive. Integration costs that surface after close often trace back to reporting dependencies nobody mapped, customer records nobody could export cleanly, and spreadsheets that quietly ran pricing, commissions, or inventory. A buyer that treats data as a post-close IT problem is not being conservative; it is accepting unknown risk.

Begin With the Decisions That Depend on Spreadsheets

Ask the target to list the reports used to close the books, set prices, manage inventory, pay sales commissions, and serve key customers. Then ask who owns each report, how often it refreshes, and which systems feed it. The goal is not to shame the target for using spreadsheets. The goal is to identify where critical decisions depend on undocumented manual work.

A useful test is simple: ask the target’s team to recreate three important reports without the person who normally builds them. If the report cannot be reproduced within a reasonable window, the buyer has found key-person risk. If the report requires manual overrides from a file share, the buyer has found a governance gap. Both have a remediation cost that belongs in the deal model.

Map Reporting Dependencies, Not Just Systems

A system inventory is not data diligence. Buyers need lineage from source system to report to decision. Which dashboards feed the board pack? Which KPIs have a single owner and no documented definition? Which reports depend on exports that arrive by email? Request a data flow diagram and verify it with a working session rather than accepting it as documentation.

Look for silent dependencies: Microsoft Access databases, scheduled queries on individual laptops, personal business intelligence licenses, contractor-built models, and spreadsheets that combine data from multiple systems. These are not edge cases in many mid-market companies. They are the operating layer. The buyer should estimate the cost to reconstruct each dependency in a governed environment, including labor, tooling, and parallel run time.

Test Customer Data Portability Before Signing

For many US mid-market businesses, customer data is spread across a CRM, support platform, billing system, marketing tools, and spreadsheets. Portability is not only a contract question. It is an operational question. Ask whether the target can produce a complete customer record for a sample of accounts within one business day, including contact history, consent and suppression status, contract terms, and service notes.

Review vendor contracts for export limits, API restrictions, data residency terms, and termination assistance. Confirm how the target handles opt-outs and state privacy requirements. If customer records cannot be assembled or exported in a usable format, post-close integration will be slower and more expensive than the buyer expects. That risk affects cross-sell plans, service continuity, and compliance posture.

Treat Undocumented Spreadsheets as Liabilities

Request a list of spreadsheets used in financial reporting, pricing, commissions, inventory, and regulatory filings. Sample ten of them. Check for version control, ownership, input validation, and manual adjustments. Ask what happens when the author is unavailable. A spreadsheet is not automatically a problem; an undocumented spreadsheet with no owner and no controls is a liability.

The buyer should estimate the cost to migrate critical spreadsheets into a governed model. That estimate should include business analysis, data validation, testing, training, and a period of parallel operation. It should also include the cost of leaving them in place, because some spreadsheets will survive the integration and continue to create risk.

Model the True Cost of Merging Analytics Environments

Integration budgets often underestimate analytics work because they focus on systems, not decisions. The buyer and target may run different BI tools, different data models, different metric definitions, and different security models. Harmonizing those environments requires more than license consolidation. It requires data model design, ETL rebuild, historical data migration, access control alignment, report rationalization, training, and parallel run support.

Ask for current analytics spend by tool, contract renewal dates, exit clauses, and minimum commitments. Then model three scenarios: lift and shift, consolidate, and rebuild. Include internal labor, contractor support, and productivity loss during transition. Add contingency for discoveries that emerge after close. A single integration number is less useful than a range tied to clearly stated assumptions.

Put Data Diligence Findings Into the Purchase Agreement

Data findings should influence representations, warranties, transition services, and price. If the target cannot provide reliable customer data extracts, the buyer may need a transition services agreement with specific cooperation obligations. If critical reports depend on one employee, the buyer may need retention agreements or a holdback. If remediation is material, the purchase price or integration budget should reflect it.

A pre-close interrogation sequence helps: interview the executive sponsor and data owners, inventory critical reports, request sample extracts, review vendor contracts, run a technical working session, estimate remediation, and draft a 100-day post-close data plan. Red flags include no named data owner, refusal to provide sample data, unclear licensing, heavy contractor reliance, and reports that depend on manual overrides nobody can explain.

The term sheet is not the place to discover that the target’s data estate is a black box. By then, the buyer has less leverage and more urgency. Data diligence belongs in the same conversation as financial and legal diligence, because the target’s data is not a technical footnote. It is the operating record of the business the buyer is paying for.