Project Type :Subcontracting

Project Timeline : May 2025 – December 2025

Context & Objectives

The client is a Fortune-1000 enterprise with complex financial operations across multiple business units and regions, managing Accounts Receivable, Payable, Payroll, and Collections data from various ERP systems. Cash forecasting was fragmented and heavily spreadsheet-driven, leading to inconsistent results, manual reconciliations, and limited visibility into actual liquidity.
To address these gaps, the project aimed to build a governed, auditable cash-forecasting engine in BigQuery/Dataform, replacing manual spreadsheets with version-controlled SQL models and automated backtesting. The solution delivers executive-grade dashboards and finance workbooks through Looker/Omni, providing drill-downs into key drivers like payment terms, delinquency, and payroll timing. The platform ensures end-to-end governance, auditability, and SOX-compliant security, enabling a single, reliable source of truth for enterprise cash management.

Project Goals

The project aimed to modernize and streamline the client’s cash forecasting process — moving away from scattered spreadsheets to a unified, governed, and automated analytics framework. By combining BigQuery, Dataform, and Looker/Omni, the solution was designed to give finance teams a single, trusted view of cash flow while improving accuracy, compliance, and decision-making.

1. Build a Unified and Reliable Forecast Engine
   Consolidate AR, AP, Payroll, and Bank data from multiple ERPs into a centralized forecasting model in BigQuery. This created a single, auditable view of cash positions across business units and reduced the dependency on manual spreadsheets.

2. Enhance Accuracy, Governance, and Automation
   Introduce version-controlled Dataform models, automated transformations, and backtesting logic to ensure consistent, reproducible, and SOX-compliant forecasting cycles. This improved forecast accuracy while reducing manual intervention during month-end closes.

3. Enable Insightful and Cost-Efficient Decision Making
   Deliver executive dashboards and finance workbooks through Looker/Omni with drill-downs into cash drivers like payment terms, delinquency, and payroll timing. Optimized BigQuery design (partitioning, clustering, and cost guardrails) ensured high performance and predictable costs.

Business Problem

Cash forecasting and reconciliation were spreadsheet-driven, inconsistent, and manually intensive. Each finance team maintained its own version of forecast models, causing discrepancies, long refresh cycles, and delays in identifying variances between forecasted and actual cash positions.

Reconciling forecasts with actual cash movements at month-end required manual effort and was prone to errors — slowing the close process and reducing confidence in financial projections.

Constraints & Non-Functional Requirements

Outcomes & Business Impact

Tech Stack

Target Architecture (High-Level)

Flow:
Financial Sources → Ingestion & Staging (Raw → Staging in BigQuery) → Modeling (Dataform)
→ Forecast Engine (cash ladder logic across AR/AP/PO/Payroll + backtesting)
→ Serving & Access (Looker/Omni dashboards & finance workbooks, RLS-controlled)

Supporting Lanes:
Quality & Reconciliation, SLOs & Scheduling (datagroups), Security & Governance.

(Diagram Reference: “Dataform Cash-Forecast — Architecture (PNG)”)

Data Model & Semantics (Highlights)

Core Domains

• AR: Invoices, credit memos, receipts

• AP: Bills, payments

• POs, Payroll, Bank Feeds

Core Entities

fact_invoice, fact_receipt, fact_bill, fact_payment, fact_po, fact_payroll, dim_customer, dim_vendor, fiscal_calendar

Forecast Semantics

• Cash Ladder Logic: Projects expected cash dates using invoice terms, historical DSO patterns, and delinquency curves.

• Aging Buckets: Categorized into 0–30, 31–60, 61–90, 90+ with cohort-based roll-forwards.

• Forecast vs Actuals: Snapshot tables for each run, enabling backtesting and drift analysis.

• Scenario Modeling: Conservative, baseline, and aggressive assumptions with shock factors for seasonality or policy changes.

Ops, Security, Quality & Performance

Deliverables

1. Dataform Repository – End-to-end SQL models (raw → stg → edw) with contracts, snapshots, and incremental logic

2. Backtesting Suite – Forecast vs. actual dashboards for model validation

3. Executive Dashboards (Looker/Omni) – Finance workbooks with drill-downs by region, BU, and term

4. Governance Artifacts – Data contracts, reconciliation pack, freshness monitors, and datagroup policies

5. Runbooks – Recovery, backfill, and change management procedures

Conclusion

By transitioning from spreadsheet-based forecasting to a governed Dataform-BigQuery engine, the client achieved faster closes, audit-ready lineage, and data-driven cash visibility.

Finance teams gained the ability to backtest assumptions, quantify forecast accuracy, and proactively manage liquidity.
Executives now rely on a single, reconciled view of cash, driving confident financial decisions and improved working capital efficiency.