Neither relational nor cube-based FP&A (financial planning and analysis) software is inherently easier to maintain. For a team without a dedicated administrator, the better choice is the implementation in which trained finance users can make routine changes, validate results and recover from failed loads. Compare those tasks on your own model rather than relying on the architecture label.
Contents
- What is the difference between relational and cube-based FP&A tools?
- What is the ongoing maintenance burden of a cube-based FP&A platform?
- Which FP&A architecture suits a finance team with no dedicated admin?
- Can another FP&A platform replace TM1?
- Can I report from FP&A software into OneStream?
What is the difference between relational and cube-based FP&A tools?
A cube-based FP&A tool stores data in a multidimensional structure, such as account by department by month by version, with totals calculated along each dimension. A relational tool stores data in tables linked by keys and mappings. Relational, multidimensional and hybrid storage can each use different aggregation and caching strategies, so neither design always pre-calculates or always calculates at query time (Microsoft Learn, 2026).
The design choice shapes how the model changes over time. In a cube, a question such as “what is total operating expense for EMEA in Q3 under the downside scenario” follows hierarchies built into the structure. In a relational tool, the same question is answered from rows, keys and mappings. Response time depends on the product, its storage and caching settings and the model itself, so test it on your own data rather than assuming either design is faster.
| Characteristic | Cube-based | Relational |
|---|---|---|
| Storage | Multidimensional arrays | Tables linked by keys and mappings |
| Calculation behavior | Consolidated along hierarchies and rules, pre-aggregated or calculated on request | Aggregated in queries, formulas or cached summaries, pre-aggregated or calculated on request |
| Change process | New member: added to an existing dimension. New dimension: cube design change and possible reload | New member: new row or mapping value. New dimension: new column or mapping |
| Calculation logic | Cube rules and scripts | Formulas, mappings or SQL |
| Required skills | Product rules, hierarchies and load processes | Data model, mappings, formulas and load processes |
| Often chosen for | Large multidimensional models with complex rules | Models whose tables and mappings change often |
The practical difference shows up in the second year. Year one runs on the structure the implementation team built. Year two brings a new entity, a renamed department or a new product line, and each one tests how easily the structure bends.
Neither design is wrong. Large enterprises with stable structures and heavy what-if modeling often benefit from cubes. Mid-market finance teams whose departments, entities and products change every year often find relational structures easier to keep current.
What is the ongoing maintenance burden of a cube-based FP&A platform?
Most of it comes from change. Every new dimension, rule change or hierarchy change needs someone who understands how the cube was built, how its rules interact and which load processes feed it. In a stable year that is light work. In a year with an acquisition or a reorganization, it is a job.
Calculation rules are the subtle part. In IBM’s TM1 (now part of IBM Planning Analytics), when TM1 rules use SKIPCHECK, rule-derived leaf cells need appropriate feeders for affected consolidations to calculate correctly (IBM Support, 2025). Missing feeders can understate consolidated results, and unnecessary feeders can increase memory use and calculation time. This is a specific TM1 configuration concern, not a universal property of cube-based tools.
Here is an illustrative example of how that can show up. Suppose a team adds a commission rule to a sales cube. The rule calculates correctly for each rep, but no feeder is written for it. Regional totals could then understate commission expense, and the gap might stay hidden until someone asks why regional margin looks unusually good.
Dimension changes come in two sizes. Adding a member to an existing dimension, such as another region, is usually an edit to that dimension, and IBM provides an interface for adding members to existing dimensions in Planning Analytics Workspace (IBM Docs, 2026). A new region does not automatically mean a cube redesign. Adding a new dimension, such as customer segment, is larger. As an illustrative example, adding customer segment to a revenue cube can mean changing the cube design, revising rules that reference its structure and reloading history, which is harder to fit into a budget cycle.
Load processes are the third burden. Cubes usually depend on nightly scripts that extract, transform and load data, and those scripts break when source systems change.
Which FP&A architecture suits a finance team with no dedicated admin?
One that the finance team can run itself, whatever the architecture label. Use a practical ownership test: have a finance owner and a backup perform a routine change, explain a calculation, identify a failed load and follow the recovery instructions. If both can do that without a specialist, the implementation suits a team with no dedicated admin.
The test is the administrator leaving. As an illustrative example, picture a team whose planning cube was built and run by one analyst. When that analyst moves on, the team finds many load processes and rule files and no record of which ones run nightly. Reports keep running from the last good load, but nobody can add the new product line, and the next forecast cycle falls back to spreadsheets.
Various questions separate the architectures in practice:
- Who adds a member to an existing dimension, such as a new region, who adds a new dimension, such as customer segment, and in what tool?
- Can someone who did not write a calculation understand it?
- How are data-load failures surfaced, and who is notified?
Finance teams without an admin also need the structure to live in terms they already use: accounts, departments, entities and mappings. Spreadsheet-connected tools can help here, because the model stays in a workbook the team can read while the data underneath is governed centrally. A familiar spreadsheet is not proof of low maintenance on its own, so run the same ownership test on it.
Speed still matters, but less than most demos suggest. A report that returns in two seconds instead of one makes no difference in a budget review. A dimension change that takes an afternoon instead of three days does.
Can another FP&A platform replace TM1?
Yes, for many teams, and the decision rests on what the TM1 model does today. TM1 is strong at large multidimensional models and complex rules. Teams replace it most often when maintenance depends on scarce specialists, or when most of the model is reporting and budgeting that a simpler tool covers.
Start with an inventory. List the cubes, the rules in each, the load processes and the reports people still use. In most mature TM1 environments, a portion of the cubes and processes are no longer used, and the migration scope shrinks once those are retired.
Then separate reporting from modeling. Budget-versus-actuals, management P&Ls and departmental budgets are usually the simpler parts to move, but they still need testing. Complex allocation rules, driver models with many dimensions and heavy what-if scenarios need a closer test. During evaluation, run a representative rebuild on a closed period that covers calculations, hierarchies, permissions, historical comparisons, integrations and reconciliation, and compare results to the TM1 output line by line. A matching report layout alone does not show that the model has moved.
Datarails FP&A takes a different approach for teams that want to stop administering cubes. The models live in Excel or Google Sheets on top of FinanceOS®, the governed data layer underneath Datarails. There are no cubes to design or feeders to write. Finance analysts change mappings, reports and drivers themselves, and drill-down reaches the underlying transactions. For very large, rule-heavy models with many dimensions, keep TM1 in the comparison until the hardest rebuild passes.
Can I report from FP&A software into OneStream?
Yes, through a configured transfer. OneStream imports data through defined data sources or connectors and validates mappings and dimensional intersections (OneStream documentation, 2026), which is not the same as a native connection from every FP&A product. Groups that run OneStream for corporate consolidation often have subsidiaries or business units that plan and report in a different FP&A tool. The usual pattern is a monthly feed from the FP&A tool into OneStream at the group’s reporting account level, using a mapping both sides agree on.
The mapping is where these feeds fail. As an illustrative example, suppose a business unit sends its monthly actuals and forecast to group as a flat file. The group adds new accounts to its consolidation chart, but the unit’s mapping still points those balances to the old parent accounts. Group-level operating expense can then be right in total and wrong by category until someone compares the feed with the unit’s own P&L.
Three controls reduce that risk. Keep approved, version-controlled mappings owned by the group, and test the transfer process whenever either chart of accounts changes. Validate each feed before it loads: check entity, account, period, scenario, currency and signs against control totals, and confirm how a reload treats data already loaded. Reconcile intercompany balances on both sides before the feed, because consolidation tools eliminate what they receive, including mistakes.
Datarails holds the mappings from each entity’s accounts to the group structure in FinanceOS, so a feed into a group consolidation system could be built from the same mapped data that business unit reports use. Confirm the export mechanism with Datarails and the export format with the group’s OneStream administrator before go-live. The consolidation guide and the comparison of FP&A platforms cover the wider choices.
How to test FP&A maintenance in a demo
List the changes your current model needed over the past year and who made each one. Use that list to discuss maintenance responsibilities in a Datarails demo.
