Overview
A global manufacturing organization with regional headquarters in the United States operates across various sectors, including agriculture, construction, marine, and industrial. The company produces advanced diesel engines, diesel-powered equipment, and gas engine-based energy systems, with finance operations supporting complex, high-volume ERP-driven reporting needs.
95%
Faster Financial Reports
100%
Automated ETL Jobs
20+
Years Historical Data Coverage
20–30
Hours Saved Weekly
Customer Challenges
The finance team relied on a legacy reporting pipeline where data was extracted from the GPAC ERP system, lightly transformed, and then consumed in Power BI. As data volumes and reporting requirements increased, this setup became a bottleneck for scalability, performance, and future analytics.
Legacy and Fragmented Data Pipeline
The existing pipeline involved multiple handoffs between GPAC, Dr. Sum, and Power BI, which increased operational complexity and made the system harder to manage. There was also limited visibility into the end-to-end data flow, making it difficult to identify and resolve failures quickly.
Historical and Incremental Load Limitations
The system was not designed to support large one-time historical data loads, which restricted the ability to backfill data when needed. Incremental data capture processes were also inefficient and unreliable, leading to potential data inconsistencies.
ERP Performance Risk
Reporting workloads relied heavily on GPAC extracts, creating a dependency on the ERP system. This increased the risk of performance issues and could potentially impact core transactional operations.
Manual Master Data Handling
Four critical master data tables were maintained manually outside the main data pipeline. This lack of integration resulted in inconsistencies, and there was no standardized mechanism to bring this data into reporting workflows.
Scalability and Future Readiness Challenges
The architecture was not flexible enough to support additional tables, new data sources, or increased data refresh frequencies. It also lacked strong governance, auditability, and standardization, limiting its ability to scale with future business needs.
Cloud-Native Finance Analytics Architecture
This architecture illustrates the automated data pipeline that integrates GPAC and SEAQ systems with Snowflake and Power BI. It showcases how historical and incremental financial data is ingested, transformed, and consolidated into a centralized reporting layer, enabling real-time dashboards, scalable analytics, and secure, governance-ready financial insights.
Solutions
To address the limitations of the legacy pipeline, a modern, cloud-native data architecture was implemented using Amazon Web Services, Snowflake, and Microsoft Power BI. This new setup replaced the Dr. Sum-based workflow with an automated, scalable, and governance-ready data platform designed to support growing finance analytics needs.
01.
Automated ERP Data Ingestion Using AWS
The legacy data pipeline was replaced with a cloud-based architecture that automated data movement from the ERP system to the reporting layer. Historical data was successfully migrated, and incremental data loads were streamlined to run on a scheduled basis. This eliminated manual effort, improved data reliability, and introduced validation checks to ensure consistent and accurate reporting.
02.
Centralized Staging and Audit Layer in Amazon S3
A centralized staging layer was established using Amazon S3, where both historical and incremental datasets were stored in structured formats. This layer provided a durable and traceable foundation for the pipeline, enabling easy reprocessing, backfills, and improved auditability across the data lifecycle.
03.
Snowflake as the Central Finance Data Platform
Snowflake was implemented as the core data platform for finance analytics. Secure file and object integrations were configured to ingest data from S3, with raw data loaded into a dedicated schema to preserve lineage and granularity. Transformations were built using Snowflake SQL and stored procedures to cleanse, enrich, and integrate transactional and master data. The processed data was then organized into a reporting schema optimized for performance and analytics consumption.
04.
Consolidated Finance Reporting View
A unified reporting layer was created by consolidating transactional and reference data into a single, standardized dataset. This ensured consistency across reports and simplified data consumption for business users. A scheduled weekly refresh cycle was implemented to align with finance reporting timelines and maintain up-to-date insights.
05.
Power BI Reporting Enablement
Power BI was directly connected to Snowflake’s reporting layer, removing Dr. Sum as an intermediate dependency. This enabled faster data access, reduced complexity, and improved report performance while establishing Snowflake as the single source of truth for finance dashboards.
Streamline Your Finance Analytics Today
Talk to an ExpertServices
Benefits
Operational Efficiency
The pipeline from GPAC to Power BI was fully automated, eliminating manual handoffs and reducing operational complexity. This significantly lowered maintenance overhead and allowed teams to focus on analysis rather than data preparation.
Improved Data Completeness and Timeliness
A one-time historical data load was successfully completed, followed by frequent incremental updates. Finance users gained access to complete and consistently refreshed datasets, enabling more timely and reliable reporting.
Centralized and Governed Finance Data Platform
Snowflake was established as the single source of truth for both transactional and master data. This improved report consistency, strengthened data trust, and enhanced auditability across finance processes.
Performance and ERP Load Reduction
Reporting workloads were shifted away from GPAC and managed within Snowflake. This reduced dependency on the ERP system and minimized the risk of performance impact on core transactional operations.
Scalability and Future Readiness
A serverless architecture built on AWS, including AWS Glue and Snowflake, enabled seamless onboarding of new tables and data sources. The platform is now well-positioned to support advanced analytics, forecasting, and future data science initiatives.
Improved Data Quality and Governance
Standardized transformations and validation checks improved overall data accuracy and reliability. Clear data lineage and structured schemas strengthened governance, compliance, and long-term maintainability.
Conclusion
By modernizing its finance data integration architecture with AWS Glue, Amazon S3, Snowflake, and Power BI, the client successfully transitioned from a fragmented, legacy ETL process to a scalable, governed, cloud-native analytics platform. The new solution delivers complete historical visibility, reliable incremental updates, integrated master data, and high-performance Power BI reporting-while significantly reducing operational complexity and positioning the organization for future data-driven initiatives.
FAQ
Here are some common questions about how the automated finance analytics solution transformed reporting and operational efficiency:
How did NeenOpal automate the finance data pipeline?
NeenOpal implemented an Azure- and Snowflake-based ETL pipeline that automated data ingestion, transformation, and reporting, eliminating manual processing and ensuring real-time financial insights.
What improvements were achieved in financial reporting?
Financial report generation became 95% faster, ETL jobs reached 100% automation, and leadership gained access to up-to-date dashboards with complete historical data coverage.
How does the solution support scalability and future growth?
The cloud-native architecture, combined with Snowflake and automated pipelines, allows the addition of new tables, data sources, and users without rework, enabling scalable and reliable finance analytics.
Authors
Contact Us
We’d love to hear from you.
Lets discuss how we can transform your business with AI. Talk to our AI expert team. Lets do AI journey together.
