Essential business data was spread across systems like NetSuite ERP and SharePoint, stored in inconsistent formats. This setup made it difficult to generate a single view of financial and operational performance.
Data extraction and reporting processes were heavily manual, time-consuming, and prone to errors. These delays directly impacted financial accuracy and slowed down payment processing timelines.
Automated ingestion workflows using Python and Spark connected data from NetSuite, SharePoint, and internal systems into a single environment.
Azure Data Lake Storage (ADLS Gen2) was used to store raw, processed, and curated datasets in structured formats for quick access.
Databricks and Azure Synapse transformed raw data into analytics-ready formats, lowering dependency on manual workflows.
Power BI dashboards were built on curated datasets to deliver real-time visibility into financial and operational metrics.
Azure Purview was configured to maintain data lineage, governance, and consistency across reporting environments.
The enterprise was able to speed up payments by 2X with automated data workflows and structured data models.
100% Data Centralization by combining data in Azure Data Lake and Synapse, the enterprise now has a single centralized repository for analytics.
Near Real-Time Insights by using Power BI dashboards deliver near real-time insights into key business metrics to make quicker and more informed decisions.
