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BARC Score — Integrated Planning & Analytics (IP&A) 2026

As of 2026-09-27

Edited by Cédric Mary, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is BARC Score?

Planning and analytics are the same cognitive process split by tooling — SAC unifies them because both run on the same dimensions, hierarchies, and currency-translation logic.

Integrated Planning and Analytics, commonly abbreviated IP&A, names a capability model rather than a single product: it describes what happens when financial and operational planning are run on the same governed data foundation as business intelligence, instead of being built as two separate disciplines that only meet during a painful monthly reconciliation. The BARC Score for IP&A evaluates vendors on how completely they deliver that convergence — not just whether a tool can do "planning" and "analytics" as two menu items, but whether the numbers a planner enters and the numbers an analyst reports are, structurally, the same numbers.

Why It Matters

The traditional split between planning and analytics is not a technology limitation so much as an organizational habit. Finance builds a budget in a planning tool or a spreadsheet chain, with its own version of the chart of accounts, its own cost-center hierarchy, its own currency rules. Meanwhile the BI team builds dashboards from actuals sitting in a data warehouse, with a hierarchy that has drifted slightly out of sync because nobody owns keeping the two aligned. Every month, someone reconciles the variance between "what we said we would spend" and "what we actually spent," and a meaningful share of that reconciliation effort is not genuine business insight — it is cleanup work caused by the two systems not sharing a single source of truth. IP&A removes that seam. When budget, forecast, and actuals share one dimensional model, variance analysis is a query, not a project.

Why it matters in practice

Running planning in spreadsheets and analytics in a BI tool causes divergence: different numbers, different hierarchies, different cut-off dates, endless reconciliation.
xP&A extends IP&A beyond Finance so the forecast becomes the sum of real operational inputs (headcount, capacity, territory, capex) rather than a top-down adjustment of last year.
Because SAC planning and analytics share one data model, variance analysis (actual vs plan vs forecast) computes without moving data between systems.

Key points

IP&A unifies planning and analytics on one data model, eliminating the reconciliation cycles that consume planning team bandwidth when spreadsheet plans diverge from BI actuals.
xP&A extends financial planning to operational plans — headcount, supply chain, sales territory, capex — all reconciling to the same consolidated financial view.
SAC planning runs on the same dimensional model as SAC analytics: actuals, budgets, forecasts, and scenarios coexist in one model; variance analysis requires no data movement.
The five maturity markers of IP&A: driver-based planning, rolling forecasts, write-back to ERP, workflow with accountability, and AI-assisted predictive seeding.
Version discipline is the hardest governance challenge: each version (Budget, Forecast, Scenario) must be a system-managed data slice with locking, permissions, and audit log — not a copied spreadsheet tab.
The SAC vs BPC decision hinges on statutory consolidation: BPC's full consolidation capabilities are not replaced by SAC planning for legal entity reporting; SAC is the preferred path for management planning.
Driver-based planning (volume × price → revenue) is fundamentally more resilient than cell-based manual entry — a driver change propagates through the entire model automatically, versus manual updates across hundreds of cells.
IP&A requires master data governance as a prerequisite: planning models built on inconsistent hierarchies produce plans that cannot reconcile to financial statements, destroying user adoption.

Terms used on this page

BARC Score: BARC's proprietary 2x2 vendor positioning visual — analogue to Gartner's Magic Quadrant, but methodology combines real-user survey data with BARC's lab and analyst inputs.
IP&A: Integrated Planning & Analytics — category of platforms combining planning, budgeting, forecasting AND analytics/reporting in one tool. SAC is the canonical example.
Reusable IP: An artifact, checklist, or model that can be reused across clients without copying client-specific data.
xP&A: Extended Planning & Analysis — pushes the IP&A architecture beyond Finance into workforce, supply chain, and sales-capacity planning, so a consolidated financial forecast is the sum of real operational plans rather than a top-down adjustment.
Driver-based planning: A planning method where financial outcomes are formulas of observable operational drivers (volume, price, mix) rather than manually entered cell values, so a driver change propagates automatically through the model.
Predictive seeding: SAC's statistical (Smart Predict) forecast generated as a starting point for a plan, which contributors then adjust rather than building from a blank model — reduces planning cycle time.
Write-back: A planning engine's ability to save user input back into the governed model (not just read it) — native to SAC Planning, and the capability that separates a planning tool from a read-only BI tool.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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