Automated Month-End Financial Consolidation
Business Problem
A large organization with multiple business entities faced significant challenges with its month end financial consolidation process. They could not efficiently produce key financial reports like the Income Statement and Balance Sheet due to a manual and inefficient process relying on Excel files and email-based reporting, leading to delays in the month end close. There was no direct integration with Microsoft Dynamics 365 Finance, increasing the risk of human errors and inconsistencies in financial reporting. The existing workflow was unoptimized and required extensive manual intervention.
Our Approach (Evolution Analytics – EA)
Evolution Analytics (EA) played a key role in automating and modernizing the financial consolidation process through a structured approach:
- Data Strategy Assessment: EA conducted in-depth interviews with all business entities to map financial flows, identify bottlenecks, and highlight areas for improvement. They developed a strategic roadmap for automation and integration with Microsoft Dynamics 365 Finance.
- Modernization:
- Unified Data Management: EA leveraged a single Snowflake account with entity-specific databases to centralize financial data storage and processing.
- Automated Dimensional Mapping: They integrated Dynamics 365 Finance data into Snowflake to ensure accurate mapping of financial dimensions.
- Data Pipeline Automation: EA developed a robust Azure Data Factory (ADF) pipeline to trigger data loads from Snowflake, utilizing newly created data marts that housed trial balance data combined with Dynamics Finance mappings.
- Interactive Financial Processing: EA built a Streamlit-based application within Snowflake to provide financial users with real-time trial balance calculations, immediate visibility into mapping discrepancies, and a gateway validation check before pushing data to Dynamics Finance, ensuring data integrity.
- End-to-End Automation: EA eliminated manual extractions, spreadsheet-based consolidations, and email-based reporting, replacing them with an integrated, automated solution.
- Supporting Technologies: The solution leveraged Snowflake for centralized data storage and processing, Streamlit for the interactive financial validation and automation tool, Microsoft Dynamics 365 Finance for centralized financial reporting and management, and Azure Data Factory (ADF) for automated data movement and integration. The solution prioritized data quality validation and traceability.




The Result (Value Delivered):
While direct revenue increases were not explicitly quantified, the significant reduction in manual effort, faster month-end close, and enhanced financial accuracy contribute to improved operational efficiency. These improvements support better decision-making, potential cost savings, and increased governance and compliance, ultimately leading to a stronger financial reporting foundation that positively impacts profitability.