FP&A data gathering inefficiency is the time finance teams spend locating, reconciling, and reshaping inputs instead of interpreting them. In some analytics projects, data acquisition and cleanup can consume 60% to 80% of the work, according to research summarized by MIT Sloan; that range is a warning about the burden, not a universal FP&A benchmark. The practical fix is to trace each handoff, standardize the planning data model, and automate repeatable collection and aggregation before adding more analysis.
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Data work is part of planning. A forecast cannot be more reliable than its underlying inputs. But when analysts repeatedly chase files, confirm definitions, repair formulas, and combine department-level spreadsheets, the collection process can crowd out scenario analysis and decision support. The answer is not to remove finance from data quality. It is to make reliable inputs easier to produce, trace, and update.
This article focuses on the operational causes of data gathering inefficiency and the changes that help finance teams reclaim time. For the broader transformation from spreadsheet dependency to strategic finance, see our FP&A transformation guide.
Why does finance spend so much time gathering data?
The familiar “80% of time on data” figure is best treated as a signal, not a promise that every finance team has the same ratio. MIT Sloan cites an estimate that 60% to 80% of a data analytics project may go to acquiring and cleaning data. Finance planning has its own mix of systems, processes, and controls, so measure the time in your own cycle rather than adopting a generic percentage as a benchmark. MIT Sloan’s discussion of finance teams and AI makes the broader point: useful analytics depends on getting the data into usable shape.
In FP&A, the burden usually comes from repeated manual steps rather than one dramatic failure. A single monthly cycle may involve collecting actuals, updating assumptions, asking budget owners to refresh templates, mapping accounts, reconciling versions, and explaining discrepancies. Each task appears small. Together, they can consume the days when finance needs to investigate what changed and advise business leaders.
Common conditions include:
Inputs live in different places. Actuals, headcount, pipeline, and operating assumptions may come from separate tools or spreadsheets.
Definitions are inconsistent. Teams may use different names, time periods, hierarchies, or levels of detail for the same business concept.
Work depends on individual memory. Analysts know which file is current, which tab to update, or which owner needs a reminder, but those steps are not documented or repeatable.
Data is copied instead of connected. A value is exported, pasted, reformatted, and sent onward, creating more opportunities for version drift and errors.
Late changes restart the work. A revised assumption can force finance to update several files and then verify that dependent schedules still agree.
That is why the first step is not simply “buy automation.” It is to identify which inputs are repeated, which are decision-critical, who owns them, and what must be true before finance accepts them. The role of an EPM system is to make planning logic and data more connected; it cannot resolve unclear ownership or definitions by itself.
Where does the data gathering time go?
To locate the waste, follow a representative forecast input from its source to the final report. Record who creates it, who checks it, where it changes format, and how finance knows it is complete. A useful process map includes both the visible spreadsheet work and the less visible coordination around it.
Requesting inputs: Finance sends templates, describes the required fields, and follows up with contributors.
Collecting submissions: Analysts download or receive files and sort out missing tabs, old versions, or incomplete entries.
Mapping and cleaning: They align account names, cost centers, product lines, regions, employee identifiers, time periods, and other dimensions.
Combining datasets: They join actuals with assumptions and operational drivers, often through formulas or manual copy-and-paste.
Reconciling: They compare totals with source systems, investigate unexplained differences, and decide which version to trust.
Refreshing outputs: They update reports and slides, then repeat the process when assumptions or actuals change.
For example, a revenue forecast may depend on actual bookings, open opportunities, sales capacity, quota assumptions, and expected timing. If those inputs have different update schedules and levels of detail, an analyst may spend more effort aligning them than examining what the forecast says. Similarly, a workforce plan needs a clear relationship between positions, employees, hiring timing, compensation assumptions, and the financial view. These are connected planning questions, not just data-import tasks.
A short time study makes the pattern visible. For one full planning cycle, track time by task rather than by job title. Separate hands-on manipulation from waiting for contributors, investigating discrepancies, and rebuilding outputs. Note which tasks happen once and which recur every cycle. This prevents a common mistake: automating a task that looks slow while ignoring a smaller step that triggers repeated rework.
Use a simple log for the observation period: task, owner, trigger, time spent, dependencies, and what caused a correction or delay. Record elapsed waiting time separately from active work. A submission that takes five minutes to review but arrives four days late may have a different process fix than a report that takes an analyst two hours to rebuild. Ask contributors where they repeat the same entry, and ask analysts which exceptions consume disproportionate investigation. Compare answers across teams; a step that seems obvious to one group may be invisible to another.
Then rank the opportunities by recurrence, business impact, and readiness. A high-volume, rules-based transformation with stable definitions is often easier to standardize than a rare judgment call. A critical input with unclear ownership may need process design before automation. Keep a baseline so the team can later compare cycle time and quality, not just count the new connections or workflows. The goal is to reduce avoidable work while preserving review where finance judgment matters.
Work step | Manual-process signal | Improvement to assess |
|---|---|---|
Input collection | Repeated reminders and multiple versions arrive by email | Define owners, due dates, required fields, and one controlled submission path |
Mapping and cleanup | Analysts repeatedly rename, recode, or reshape source values | Standardize dimensions and map source fields to agreed planning definitions |
Aggregation | Totals depend on copied formulas or manually combined files | Use governed, repeatable aggregation logic and validate the result |
Reconciliation | Differences surface late and require a search across files | Set source-of-truth rules and checks that flag exceptions early |
Reporting refresh | Every changed input means rebuilding several outputs | Connect reporting views to the maintained planning model |
The table is a diagnostic, not a software shopping list. Some teams can eliminate friction by clarifying handoffs and template design. Others need better data integration or a planning platform. Make that distinction before selecting a solution.
What does FP&A data gathering inefficiency cost beyond hours?
Hours are the easiest consequence to see, but they are not the only one. If a team spends most of its cycle preparing data, it has less capacity to test assumptions, explain variances, and evaluate trade-offs. Business partners may receive a forecast later, or receive a number without a clear explanation of what changed.
Manual collection also creates process risk. When contributors use different versions, finance may reconcile the wrong file. When mappings live in a personal workbook, a staff change can make the logic difficult to reproduce. When an assumption is changed in one schedule but not another, totals can diverge. These risks do not mean spreadsheets are inherently unsuitable; they mean that a process built around uncontrolled copies becomes harder to govern as contributors and planning needs increase.
There is an opportunity cost as well. Analysts asked to maintain a monthly patchwork have less time to support questions such as:
Which operating assumptions explain the gap between plan and actuals?
How would a change in hiring pace affect expense and capacity?
Which revenue drivers are moving, and where should leaders investigate?
What decisions need to be made now, rather than after the next reporting cycle?
Faster collection alone does not guarantee better decisions. It creates capacity for better analysis when finance also agrees on business drivers, model logic, and the questions leaders need answered. That is a central idea in a gradual path to connected planning: begin with a useful planning scope, prove the process, and expand with purpose. For the broader shift from spreadsheet dependency to strategic finance, see our FP&A transformation guide.
How can an EPM platform reduce manual data gathering?
An enterprise performance management platform can provide a shared environment for planning data, dimensions, assumptions, calculations, and outputs. Depending on the design and available integrations, it can reduce repeated file handling by bringing validated inputs into a common planning model and recalculating dependent views when an approved value changes. It does not eliminate every source-system limitation or make unreliable data trustworthy automatically.
Finance leaders should assess four capabilities and the process decisions behind them:
Repeatable input flows. Identify which source data can be brought into planning on a defined cadence and which assumptions still require a contributor to enter or review them. Confirm the owner, timing, and exception process for each input.
Consistent dimensions. Agree on the structures used to analyze results, such as account, department, product, geography, or period. Map source values deliberately and decide how changes to those structures are governed.
Transparent calculations. Replace hidden chains of formulas with logic that finance can inspect, test, and explain. Document key business rules and make material assumptions visible to their owners.
Connected outputs. Build reports and scenarios from the maintained model rather than rebuilding each deliverable from separately copied files. Confirm that totals reconcile and users can trace a result to its drivers.
Integration is not synonymous with automation. A connection can move data while leaving duplicate definitions, missing records, and unclear ownership untouched. Plan for controls such as completeness checks, reconciliation to source totals, exception review, and a clear record of when data was refreshed. Where those checks identify a discrepancy, finance needs a named person to resolve it.
Before redesigning the flow, classify each important input by source, refresh frequency, level of detail, and business owner. Decide whether it is a system-generated actual, an operational driver, or a management assumption. Those categories need different handling: an actual may require reconciliation to a source total, while an assumption needs a responsible contributor and a review deadline. Write down the expected grain of each input, such as monthly by department or weekly by product, so that data is not forced into a misleading level of detail during aggregation.
For the first release, set acceptance criteria with the people who will use the model. These might include a successful reconciliation for a selected period, a clear exception path, and a user who can explain how a reported result was produced. Test both expected inputs and common edge cases, such as a newly added department, a missing submission, or a late adjustment. A small, well-understood scope is easier to validate and learn from than a wide rollout built on untested assumptions.
Implementation scope matters, too. Start with the use case causing the greatest recurring effort and with inputs that are sufficiently understood. A team may begin with core financial planning, then expand to revenue, workforce, or supply-chain planning as its definitions and adoption mature. Amvent's planning use cases reflect these distinct domains. The objective is not to connect everything on day one; it is to build a reliable flow that users can operate and improve.
Amvent Consulting is a Toronto-based boutique EPM consulting firm and official Pigment Delivery Partner. Its customer-to-consultant perspective comes from founder Rasagya Monga leading Gusto's 250+ user Anaplan-to-Pigment migration as an end-customer before founding the firm. For finance leaders evaluating how a planning model should handle inputs and connections, learn about Pigment integrations for connected finance planning or the firm's implementation and consulting work.
What does a finance team's week look like after automation?
A more effective process changes the rhythm of work, not just the software screen. Instead of spending the first days of a cycle asking where the files are, finance can focus on whether the inputs are complete and what exceptions need attention. Contributors know what they own. Analysts spend less time reformatting data and more time examining its implications.
A practical future-state cycle might look like this:
Before the cycle: Owners, definitions, calendar, and required inputs are agreed and documented.
During collection: Recurring data follows a consistent refresh path. Contributors enter or validate assumptions in a controlled workflow.
At validation: Finance reviews exceptions, compares key totals with source information, and resolves material differences with the right owner.
During analysis: The team reviews variance drivers, updates scenarios, and explains changes to decision-makers.
After the cycle: Finance captures recurring problems and updates the process, rather than carrying informal workarounds into the next forecast.
The exact division of work depends on the planning model and source systems. The useful measure is whether the team can answer three questions without a forensic search: What changed? Where did the value come from? Who is accountable for the assumption? When the answers are clear, finance has a stronger foundation for faster and more reliable planning.
That future state still requires ownership after launch. Models need maintenance as structures, business rules, and planning needs change. Teams should define who handles user questions, investigates defects, reviews enhancements, and supports new planning cycles. Read about what ongoing Pigment support can look like, and consider how a structured implementation methodology can build validation and handover into the work rather than treating them as afterthoughts.
Frequently Asked Questions
Do finance teams really spend 80% of their time gathering data?
Not as a universal, verified FP&A benchmark. MIT Sloan summarizes an estimate of 60% to 80% of time spent acquiring and cleaning data in a data analytics project. Individual finance teams should measure their own planning cycles and distinguish collection, cleanup, coordination, and analysis time.
What should an FP&A team automate first?
Start with a recurring task that consumes time, creates rework, and has sufficiently clear inputs and ownership. Map the process first. Then prioritize repeated collection, transformations, or reporting steps that can be standardized without hiding important review controls.
Does an EPM system eliminate the need for spreadsheets?
No. Spreadsheets can remain useful for analysis, one-off work, or collaboration. An EPM model is most valuable when finance needs governed shared planning logic, repeatable workflows, and connected views at a scale that makes uncontrolled file handling cumbersome.
How do we know if an integration has improved the process?
Compare the process before and after using practical measures: time spent collecting and reshaping inputs, number of manual handoffs, frequency of reconciliation issues, and time available for analysis. Also verify that the new flow preserves completeness checks, clear ownership, and traceability.
Get in touch to talk through your finance team's data gathering process.
Make data gathering a smaller part of the planning cycle
FP&A data gathering inefficiency is a process problem before it is a technology problem. Measure where the time goes, clarify ownership and definitions, then automate the repeatable steps that keep finance from focusing on decisions. If your team is considering a Pigment implementation or improving an existing planning process, Amvent can help you evaluate a practical next step.
