The data quality problem in FP&A
Finance teams are increasingly data-rich. Actuals flow in from accounting systems. Headcount data arrives from HRIS platforms. Pipeline data comes from CRM. Billing data streams from subscription management tools. The volume of data available for planning has never been greater.
But volume is not quality. Duplicate records, inconsistent coding, missing fields, stale data, and mapping errors corrupt the planning process in ways that are difficult to detect and expensive to fix.
The cost of poor data quality
Direct costs. Hours spent reconciling, investigating, and correcting data issues. For a typical FP&A team, data quality problems consume 20-30% of the monthly reporting cycle.
Decision costs. A revenue forecast built on incorrect pipeline data leads to overhiring. A headcount budget built on outdated compensation data leads to either budget overruns (if actual salaries are higher) or failed offers (if the budget is too low).
Credibility costs. When the finance team presents numbers that are later revised because of data errors, stakeholder trust erodes. Rebuilding that trust takes months.
A data quality framework
1. Define data ownership. Every data element should have a named owner responsible for its accuracy. Revenue data is owned by the finance team. Pipeline data is owned by sales operations. Headcount data is owned by HR. Ownership does not mean the FP&A team cannot use the data -- it means there is a clear escalation path when quality issues arise.
2. Validate at the point of entry. The cheapest place to catch data errors is before they enter the planning model. Implement validation rules:
- Range checks: salary cannot be negative; headcount cannot be a decimal
- Completeness checks: every revenue entry must have a customer, product, and period
- Consistency checks: the sum of departmental headcount must equal total headcount
- Timeliness checks: actuals for the prior month must be loaded by working day five
3. Reconcile at every boundary. When data moves between systems (accounting to FP&A, HRIS to workforce plan), reconcile the totals. Revenue in the accounting system must match revenue in the planning model. Headcount in the HRIS must match headcount in the workforce plan. Discrepancies should be investigated immediately, not left for month-end.
4. Monitor trends. Sudden changes in data patterns often signal quality issues, not business changes. If average deal size doubles overnight, it is more likely a data entry error than a sudden shift in market dynamics. Build automated alerts for metric movements that exceed reasonable thresholds.
5. Document data definitions. Ambiguity is the enemy of data quality. Does "revenue" mean invoiced revenue, recognised revenue, or cash received? Does "headcount" include contractors? Does "churn" include downgrades? Publish a data dictionary that precisely defines every metric used in the planning process.
Common data quality issues in FP&A
Chart of accounts misalignment. The GL structure does not map cleanly to the planning model's account structure. Costs end up in the wrong category, distorting margin analysis.
Duplicate records. The same customer appears twice in the CRM, inflating pipeline. The same employee appears in two departments, doubling headcount.
Timing mismatches. The accounting system closes on the last calendar day. The billing system closes at midnight UTC. The HRIS runs payroll on the 25th. These timing differences create reconciliation gaps that look like data errors.
Manual overrides. Someone "fixed" a number in the spreadsheet without documenting the change. The fix was correct for one period but now distorts the trend.
Building a data quality culture
Data quality is not a one-time project -- it is a discipline. Build it into the operating rhythm:
- Review data quality metrics in the monthly close meeting
- Celebrate improvements in reconciliation accuracy
- Escalate persistent data quality issues to system owners
- Include data quality in the criteria for evaluating new tools and integrations
The FP&A team that treats data quality as a strategic priority -- not an administrative chore -- builds models that stakeholders trust, reports that drive decisions, and forecasts that prove accurate.