Enterprise Lakehouse Power BI Migration Modernization Services

A U.S.-based manufacturing organization relied on a combination of operational, commercial, and enterprise systems to support critical business functions, including production, inventory management, procurement, order fulfillment, financial reporting, and enterprise analytics. Although the organization had access to a significant volume of business data, that data was distributed across SQL Server, ERP systems, Excel trackers, CSV exports, and API-fed applications, with each source following different data structures, refresh patterns, and ownership models.

As reporting requirements continued to grow, teams were maintaining separate datasets and repeatedly performing data extraction, transformation, and reconciliation activities. Business logic was often duplicated across spreadsheets, report-specific queries, and individual reporting processes, which increased operational effort and made it difficult to establish a single, trusted version of important business measures.

The fragmented data environment also created challenges around data lineage, ownership, and traceability, making it harder for teams to understand where information originated, how it was transformed, and which reports or processes depended on it. At the same time, the growing number of reports and independent refresh paths introduced additional complexity in maintaining reliable refresh schedules and scaling analytics workloads.

The key challenges included:

• Fragmented source systems and ownership models, which required analysts to locate, extract, and reconcile information from multiple systems before it could be used for reporting.
• Repeated manual data transformations, where similar business logic was recreated across spreadsheets and report-specific queries instead of being implemented through reusable data pipelines.
• Inconsistent KPI definitions, resulting in different reports potentially calculating the same business metric differently and reducing confidence in analytical results.
• Limited data lineage and ownership visibility, making root-cause analysis, change impact assessment, and accountability for curated datasets more difficult.
• Increasing refresh and scaling complexity, as additional reports and data workloads created more refresh paths and operational dependencies that needed to be maintained.