Data Quality in Financial Analysis: Why Garbage In Means Garbage Out in Due Diligence

Data Quality in Financial Analysis: Why Garbage In Means Garbage Out in Due Diligence

Every due diligence engagement depends on the quality of the underlying data. When GL exports are complete, properly structured, and consistent across periods, the analytical team can focus on the work that matters: identifying adjustments, assessing risks, and forming conclusions. When data quality is poor, the team spends its time fixing data rather than analyzing it.

The cost is not just time. Poor data quality creates analytical risk. Missing accounts, duplicated entries, or misclassified transactions can lead to incorrect conclusions that survive to the final report. On a deal where the advisory team's findings directly inform the purchase price, data quality is not a technical detail. It is a material risk.

Common Data Quality Issues

Data quality problems in due diligence fall into several categories:

Completeness Issues

Missing periods. The target provides monthly trial balances for 36 months but month 14 is missing. The gap is not always immediately obvious, particularly on multi-entity engagements where each entity's data is processed separately.

Missing accounts. The trial balance export excludes accounts with zero balances, but those accounts had non-zero balances in other periods. The resulting time series has inconsistent account populations across periods, which distorts trend analysis.

Truncated data. ERP exports sometimes truncate account descriptions, transaction references, or memo fields. This reduces the analyst's ability to understand account activity without going back to the source system.

Consistency Issues

Account renumbering. The target restructured its chart of accounts mid-way through the analysis period. Revenue that was in account 700100 in years one and two appears in account 411000 in years three and four. Without a crosswalk, the mapping exercise produces inconsistent results.

Entity restructuring. Legal entity changes (mergers, demergers, new subsidiaries) create discontinuities in the time series. Revenue shifts between entities not because the underlying business changed but because the corporate structure did.

Currency inconsistencies. For international groups, trial balance data may be extracted in local currency for some periods and reporting currency for others. Mixed-currency data produces meaningless consolidation results.

Accuracy Issues

Duplicate entries. Data exports sometimes include duplicate rows, particularly when extracted from multiple system sources or through manual compilation. A duplicated trial balance row doubles the reported balance for that account.

Sign conventions. Different ERP systems and chart of accounts structures use different sign conventions. SAP typically reports credits as negative values. Some mid-market systems report all values as positive, relying on the account type to determine the direction. When sign conventions are inconsistent, the analysis produces incorrect totals.

Rounding and aggregation. Trial balance exports at different levels of aggregation may not reconcile due to rounding differences. A detail-level export that sums to 10,000,023 while the summary-level export shows 10,000,000 creates a reconciliation difference that must be investigated.

Impact on the Diligence Process

Data quality issues affect the engagement at every stage:

Mapping. When account populations are inconsistent across periods, the mapping exercise is more complex. Accounts that appear in some periods but not others must be investigated and handled correctly.

Analysis. Trend analysis and period-over-period comparisons are unreliable when the underlying data has completeness or consistency issues. Apparent spikes or dips in financial metrics may reflect data problems rather than business events.

Review. Partners and managers reviewing the analysis spend time on reconciliation questions rather than analytical conclusions. This extends the review cycle and delays delivery.

Client confidence. When data quality issues surface late in the engagement, the client (and the opposing advisory team) may question the reliability of the broader analysis. A team that cannot reconcile its own working papers to the target's audited accounts loses credibility.

Detecting Data Quality Issues Early

The highest-leverage approach to data quality is early detection. Validation checks performed at the point of data ingestion catch issues before they propagate:

• Balance checks: Does the trial balance actually balance (total debits equal total credits)?
• Completeness checks: Are all expected periods, entities, and account ranges present?
• Consistency checks: Are account populations consistent across periods?
• Reconciliation checks: Does the imported data reconcile to reference points (audited financials, management accounts)?
• Sign convention checks: Are the sign conventions consistent across accounts and periods?

Teams that build these checks into their data ingestion process catch issues in hours rather than days. Teams that rely on downstream discovery (analysts noticing anomalies during analysis) lose time to investigation and rework.

The Process Connection

Data quality is not a standalone problem. It is directly linked to the team's data ingestion and mapping processes. When GL data imports follow a structured process with built-in validation, quality issues are caught and resolved before they affect analysis.

When data ingestion is manual (copy-paste from Excel exports into working papers), quality control depends entirely on individual analyst diligence. This is unreliable, especially under the time pressure of a live deal engagement.

Protecting Margins Through Data Quality

Every hour spent investigating and resolving data quality issues mid-engagement is an hour that cannot be spent on analysis. On a fixed-fee engagement, it is margin lost.

The teams that protect their margins invest in data quality controls upfront. They validate data at ingestion, flag issues immediately, and resolve them before analysis begins. The incremental time invested in validation is a fraction of the rework time saved later.

This is a process discipline, not a technology question. But the discipline is easier to maintain when the process is standardized and repeatable rather than ad hoc and manual.