One of the most important processes in any business is being able to record, understand, and plan for your people. At its core, it’s a question of demand and supply: what capability does the organisation need to deliver against its goals, and what do we actually have? That question connects across the HCM system, the finance system, revenue planning, and organisational planning cycles in a way that very few other processes do.
Doing this properly provides a genuinely full organisational view. It combines the strategy, where are we going, with capability, what do we need to get there, and ultimately brings those together in a plan that can guide behaviours and actions across the business. Creating the individual inputs into this process, the people data, the cost data, the demand forecasts, is not usually the hard part. What can be hard is connecting the data across the process, because that requires having clear definitions, clear purpose, and clear lineage for the key organisational data points and their role in end-to-end headcount reporting and planning.
This is where the valuable work happens, in the joins between the systems and data sets.
This matters more than most organisations acknowledge, because people are typically the single biggest cost on the books. The U.S. Bureau of Labor Statistics puts labour costs at up to 70% of total business costs. McKinsey’s research across eight major industries found that companies spend at least three times as much on talent annually as they do on capital expenditure. We agonise over capital investment decisions, run them through detailed business cases and approval committees. The data underpinning decisions about the workforce, our largest cost line, is often the least joined-up in the organisation.
I was working with an organisation that wanted to build exactly this kind of connected workforce plan. Not a headcount tracker dressed up as something more strategic, but something that genuinely linked what the organisation was spending on people with the capacity and capability it was getting back. As the process developed and we gathered the inputs, it was not a system limitation but a structural data issue that slowed things down. HR had one version of the organisation and finance had another, and nobody had ever sat the two side by side, documented them, and understood the differences.
That gap, between how HR sees the workforce and how finance sees the cost base, is one of the most quietly destructive problems I have come across in transformation work. It doesn’t announce itself or show up in a risk register. It just sits there, making every workforce decision slightly less reliable than it needs to be.
Two systems, two truths
HR holds the people data: who’s in the organisation, what role they’re in, what grade, what team, when they started, whether they’re permanent or contract. Finance holds the cost data: salary, NI, pension, benefits, maybe a per-head overhead allocation. Both systems are doing their jobs perfectly well within their own boundaries.
The problem is that when they are not in the same system, they were not necessarily designed to talk to each other, and so they have each developed their own version of what the workforce actually looks like.
Anaplan’s research into workforce planning describes the same structural issue: HR tracks workforce dynamics against the supervisory hierarchy, the management org, while finance tracks headcount and budget according to the cost centre hierarchy, and reconciling between the two is often difficult or impossible. The result is that nobody sees the complete picture.
The org structure in the HR system will not match the cost centre structure in finance. Job titles don’t map cleanly to pay grades. Contractors might sit in one system but not the other, or they appear in both but coded differently. Headcount sounds like a straightforward number to produce, but it can give you a different answer depending on which system you pull it from, how it is defined, and how you count leavers, joiners, and people on secondment or long-term absence.
When you are trying to build a workforce plan without clarity on these definitions, the uncertainty will have a significant impact on your cost-per-head metrics, revenue per employee, attrition modelling, and capacity assumptions. Everything downstream inherits these inaccuracies, making longer-term planning a much less reliable exercise than it needs to be.
Why it persists
This is not a new problem, and most organisations I have spoken to about it recognise it immediately. But it doesn’t get fixed.
Part of the problem is ownership. Workforce data is often a mix of HR and finance requirements, and neither function feels entirely responsible for the connection between systems. HR owns the people records. Finance owns the cost records. The reconciliation owner between the two is often harder to locate. In a lot of organisations the answer is “whoever built the last spreadsheet that seemed to work,” which is how you end up with critical workforce planning models that live on someone’s desktop and break when that person moves on.
Fixing the HR-finance data gap is genuinely unglamorous work. It means agreeing on a shared definition of headcount, reconciling org structures with cost centres, and deciding how to treat contractors consistently across both systems. None of that makes it into a transformation business case because none of it sounds like it’s worth the effort. But the downstream cost of not doing it, the rework, the manual reconciliation, the decisions made on data that doesn’t quite add up, probably exceeds the cost of the fix by a significant margin.
The CIPD’s research into workforce reporting found that fewer than half of UK employers use data to identify skills gaps, and only 31% collect data to identify future skills requirements. Perhaps more telling, only around one in five employers actually calculate the cost of losing someone from their business. If organisations aren’t measuring the basics of workforce cost and capability, it’s not surprising that the harder problem of reconciling those measures across functional systems hasn’t been tackled either.
What this means for workforce planning
Coming back to that full organisational view I described at the start, the one that connects strategy to capability to plan, it simply cannot function if the base data has a headcount discrepancy that nobody has resolved. The plan might look sophisticated with scenarios, sensitivity analysis, and clean formatting, but if the inputs are built on two slightly different versions of who is in the organisation, every output inherits that uncertainty. You are planning on sand.
What I have seen work, in the handful of organisations that have tackled this properly, is treating the HR-finance data reconciliation as a precondition for workforce planning rather than something you sort out along the way. That means investing time upfront in agreeing shared definitions, establishing a single source of truth for headcount, and building a regular reconciliation process that catches drift before it compounds.
It’s not glamorous, and it requires HR and finance to collaborate on something that neither function finds particularly exciting. But the alternative is continuing to build workforce plans on two slightly different versions of reality, which is roughly what most organisations are doing right now, whether they’ve acknowledged it or not.
Where to start
If any of this feels familiar, here are some practical starting points that have made a real difference in organisations I’ve worked with. It is also worth considering the role AI can play in supporting many of these steps, with the right guardrails and definitions in place it can improve the speed to value considerably.
Agree what a “head” actually means. How do you treat contractors, secondees, people on long-term absence, leavers in their notice period? Until that definition is consistent across HR and finance, every number that flows from it will be slightly different depending on who pulls it.
Run a baseline reconciliation. Take the HR system headcount and the finance-derived headcount, work backwards from payroll if you need to, and sit them side by side. Understand the size of the gap, where the timing differences sit, and where the coding inconsistencies are. That exercise alone tends to surface problems nobody realised were there.
Map the org structure to the cost centre structure. Document where they align and where they diverge. This is the structural root of most HR-finance data mismatches, and someone needs to own the mapping and keep it current.
Decide who owns the join. Not who owns HR data or who owns finance data, but who is accountable for the reconciliation between them. If the answer is “nobody” or “whoever built the last spreadsheet,” that’s the first problem to solve.
Treat the reconciliation as a precondition, not a parallel workstream. If you’re building or refreshing a workforce plan, don’t start modelling until the base data is reconciled. Everything downstream inherits whatever inaccuracy you start with.
Set a regular reconciliation rhythm. A one-off fix drifts. Monthly or quarterly, HR and finance should compare their respective views of the workforce and resolve discrepancies before they compound.
The broader pattern
This HR-finance gap is really just one instance of a wider problem where the joins between functional data sets are almost always weaker than the data within each function. The confusion lives at the edges, where one function’s data needs to connect with another, and it is at those edges where the most important cross-functional decisions get made.
Workforce planning is the obvious case, but the same pattern shows up in project costing, where finance tracks spend against budgets that don’t align with how project teams report progress. It shows up in commercial planning, where sales pipeline data and finance revenue forecasts exist in parallel but don’t reconcile until someone forces them together at quarter-end. Anywhere you need two functions’ data to agree before you can make a decent decision, you will probably find they don’t quite agree.
If you are looking at your transformation roadmap, it is worth asking where the joins between your data sets are, and how much confidence you have that they actually connect. The answer might just de-risk your programme and save a significant amount of unneeded spend.
The Impact Architect is a newsletter for CFOs and transformation leaders navigating complex change. If this resonated, I’d welcome you sharing it with someone who’d find it useful. To get in touch about how I can support your business and finance transformation activities, reach me at neilalderson@neilaldersonltd.co.uk
Sources
U.S. Bureau of Labor Statistics via Paycor — The Biggest Cost of Doing Business: A Closer Look at Labor Costs: https://www.paycor.com/resource-center/articles/closer-look-at-labor-costs/
McKinsey / Great Place to Work — Understanding Revenue Per Employee: https://www.greatplacetowork.com/resources/blog/understanding-revenue-per-employee-what-is-a-good-revenue-per-employee-ratio-how-to-calculate-it
Anaplan — Five Common Challenges HR and Finance Face in Practicing Workforce Planning: https://www.anaplan.com/blog/five-common-challenges-hr-and-finance-face-in-practicing-workforce-planning/
CIPD — Effective Workforce Reporting: Improving People Data for Business Leaders: https://www.cipd.org/globalassets/media/knowledge/knowledge-hub/reports/workforce-reporting-1_tcm18-113899.pdf