Client & Context

The engagement was with a large-scale American banking institution operating across multiple domains including Retail Banking, Wealth Management, Treasury etc. and Core Banking operations. The organization operates at an enterprise scale with complex financial structures, high data volumes, and multi-layered reporting requirements.

The bank relies on core banking systems as primary data sources, with downstream financial planning and consolidation processes historically dependent on fragmented systems and manual interventions.

The finance leadership team aimed to standardize planning processes, improve system performance, and establish a scalable architecture to support growing data volumes and business complexity.

Challenges

Before OneStream implementation, the planning function faced several operational inefficiencies:

Key challenges included:

• Manual Excel-Based Reporting
  • Heavy reliance on spreadsheets for planning cycles
  • High risk of data inconsistencies and lack of governance
• Slow Planning and Close Cycle
  • Time-consuming aggregation and validation processes
  • Limited time for business analysis
• Data Reconciliation Issues
  • Data inconsistencies across systems required manual intervention
• Regulatory and Reporting Challenges
  • Limited traceability and auditability of financial data
• Consolidation and Calculation Issues
  • Inconsistent logic across entities and systems

Post OneStream – Initial Implementation Challenges:

While OneStream replaced Excel-based processes, the initial technical implementation introduced performance bottlenecks:

• Data load implemented using SetDataCellsUsingMemberScript
• Iterative cell-by-cell processing approach
• Poor scalability for large datasets (3-year planning data) Impact:
• Data load time of ~5 hours
• Inefficient execution for enterprise-scale planning

Our Approach

The optimization was delivered through a focused redesign of the data load and reporting architecture within OneStream.

Phase 1 – Analysis & Problem Identification (2 Weeks):

• Reviewed existing data load logic using member scripts
• Identified performance bottlenecks in iterative cube write operations
• Analyzed matrix-based data structure for 3-year planning data

Phase 2 – Data Load Optimization (3 Weeks):

• Replaced member script logic with Connector Data Source
• Enabled direct data retrieval from SQL tables in structured format
• Designed optimized transformation logic for matrix data
• Implemented Extender Business Rule to:
  • Trigger workflow execution
  • Automate Stage → Cube data load
  • Run consolidation as part of process

Phase 3 – Workflow Automation (2 Weeks):

• Designed workflows for:
  • Yearly planning loads
  • Quarterly updates
  • Monthly processing
• Introduced single-click execution mechanism:
  • Fully automated data load pipeline
  • Eliminated manual steps

Phase 4 – Reporting & Cube View Optimization (2 Weeks):

• Optimized Entity Hierarchy to reduce aggregation overhead
  • Applied data filters to limit unnecessary processing
  • Implemented zero suppression to improve rendering performance
• Results:
  • Faster Cube View execution
  • Improved dashboard responsiveness

Results

After optimization, the organization achieved significant improvements in performance and efficiency.

Quantitative outcomes:

• Data Load Time:
  • Reduced from ~5 hours to ~30 minutes
  • Includes full 3-year planning data and consolidation
• Performance Improvement:
  • Eliminated iterative processing bottlenecks
  • Improved system scalability
• Automation:
  • Introduced single-click execution
  • Reduced manual intervention significantly

Quantitative outcomes:

• Improved data accuracy and consistency
• Enhanced user experience for business teams
• Faster reporting and decision-making
• Reduced dependency on manual processes

A key stakeholder highlighted that the focus shifted from managing system limitations to enabling faster financial insights

What’s Next?

With a strong planning and performance foundation in place, the organization is now focused on extending OneStream capabilities:

• Enhancing planning models for advanced forecasting
• Expanding reporting capabilities across business units
• Further optimizing data integration pipelines
• Scaling the platform to support future growth

The solution is now positioned as a scalable and high-performance financial planning platform aligned with enterprise banking requirements.