The Planning Paradox More Data, Less Confidence

What Keeps Finance Leaders Stuck

When we examine why forecasting capability stalls, the same structural patterns appear with remarkable consistency across sectors, geographies, and planning technology choices.

The Technology-First Investment Trap

The most common pattern is selecting a planning or analytics platform before defining the business outcomes it is expected to support. Organisations acquire sophisticated FP&A tools, extend their data warehouse, or deploy self-service BI, and then discover that the underlying data is too inconsistent, too slow, or too poorly governed to power reliable forecasting. The technology becomes an expensive interface to an unreliable foundation. Gartner estimates that poor data quality costs organisations an average of approximately £10 million annually in direct operational and decision-making costs, a figure that compounds over time as planning inaccuracies cascade into resource allocation, inventory, and workforce decisions.

The implicit assumption that a powerful enough platform will generate value on its own rarely holds. Without a clear connection between platform capability and specific, measurable planning outcomes, even technically excellent solutions become shelfware within 18 months.

Data Treated as an IT Responsibility, Not a Finance One

The second pattern is the structural disconnect between data engineering and finance planning. When data is owned entirely by technology teams, it is managed as infrastructure: ingestion pipelines, schema design, storage optimisation. What is missing is commercial context. Finance teams cannot interrogate assumptions, validate drivers, or trust outputs when they have no ownership of the definitions, hierarchies, and logic that govern the data they are planning from.

The consequence is a growing gap between what the platform contains and what finance leaders are willing to sign off on. Reconciliation exercises multiply. Confidence in the numbers declines. And the energy that should be directed at forward-looking analysis is consumed by backward-looking verification.

The Annual Cycle as a Strategic Liability

The third pattern is the persistence of annual budgeting as the dominant planning rhythm in organisations that are operating in demonstrably non-annual environments. According to the BARC Planning Survey 2025, approximately 45 per cent of organisations still rely primarily on static annual budgets, and around 30 per cent report that a single forecast cycle takes more than ten working days to complete. In a market where cost structures can shift materially within a quarter, as UK manufacturing, retail, and financial services organisations have all experienced through 2024 and 2025, a ten-day forecasting cycle is not a planning tool. It is a retrospective exercise.

High-Performing Finance Functions

The finance functions we have observed achieving genuine forecasting agility share a set of deliberate choices that distinguish them from the majority.

They have replaced the annual budget as the primary planning artefact with rolling monthly forecasts, updated continuously against a driver-based model that connects operational inputs, including volume, pricing, headcount, energy consumption, and supplier costs, directly to financial outcomes. This is not a technology choice. It is a planning philosophy that requires aligned assumptions, defined business drivers, and a data foundation that can be trusted without manual reconciliation.

They invest early in scenario capability. According to the BARC Planning Survey 2025, 63 per cent of top-decile organisations by planning outcomes consider standard scenario analysis essential to their process, compared to a minority of lower-performing organisations. Critically, high performers do not produce scenarios in response to crises. They maintain a standing set of prepared scenarios, including optimistic, base, and downside, each with pre-defined operational and financial responses. When conditions shift, the analysis is already done. The question is which playbook to execute, not how to build one under pressure.

Underpinning all of this is a governed data foundation. A diversified chemicals group we worked with had fragmented legacy systems across its mining, agriculture, and chemicals divisions that were producing inconsistent planning data, making validated scenario analysis effectively impossible. By migrating to a governed lakehouse architecture with automated pipelines and formal data quality controls, the organisation established a single trusted foundation from which finance and operations could plan with confidence. Verification time was eliminated. The focus shifted from confirming whether the data was correct to using it for forward-looking analysis.

No Grand Transformation Required

For finance and operations leaders recognising these patterns in their own organisations, the instinct is often to commission a comprehensive review or initiate a major programme. The evidence suggests a more effective approach.

The organisations that have broken through the planning paradox did not start with an enterprise-wide transformation. They started with an honest diagnostic, an objective assessment of where the planning foundation actually stands, what the data quality and governance gaps are, and which specific business use cases would generate the most measurable value from targeted investment.

From there, they executed in focused, time-boxed sprints,that delivered specific, quantifiable improvements to forecast reliability, scenario capability, or reconciliation effort. Each sprint was designed with scale in mind, so that the governance decisions, data architecture, and process changes made in sprint one were reusable foundations rather than point solutions.

Our approach begins precisely here: a targeted diagnostic assessment led by specialist certified data architects, establishing a clear baseline and a target-state architecture that reflects where the business needs to go, not just where it is today. Many organisations are surprised to discover how much planning infrastructure they are already funding that is delivering little measurable value to the forecasting process.