CASE STUDY
Building a Scalable Productivity Data Platform for Montgomery Homes
AT A GLANCE
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Client: Montgomery Homes
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Industry: Construction
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Solution: Data Engineering & Workforce Analytics
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Key Impact: 10–15% improvement in workforce efficiency, 15 hours saved per week through automation, daily automated API-driven refresh and reliable, structured productivity metrics across teams
Background
Montgomery Homes is a residential construction company that manages a distributed workforce across multiple projects. The business relies on workforce productivity data to monitor utilization, engagement, and operational performance across teams.
As reporting needs grew, leadership required a reliable, repeatable way to track productivity trends and compare performance across roles and departments. That required a stronger data foundation than what was in place.
The Challenge
Montgomery Homes captured productivity data through Insightful, but the raw logs were not suitable for analytics or executive reporting.
The data lacked a structured model and consistent KPI logic. Reports relied on manual reconciliation, which slowed delivery and introduced inconsistencies. Incremental data ingestion was not in place, so refreshes required unnecessary reprocessing. These issues reduced confidence in dashboards and limited their operational value.
Before reporting could scale, the underlying data engineering approach needed to be rebuilt.
The Solution
GrowthBI designed and delivered a production-grade productivity data platform on Microsoft Azure.
The solution introduced automated API ingestion, incremental processing, and a structured SQL data model that standardized productivity metrics across the business. Business logic moved into the database layer, which removed inconsistencies between reports and simplified downstream analytics.
The architecture separated orchestration, transformation, and visualization to support scale and governance.
Key capabilities included:
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Automated API ingestion from Insightful
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Watermark-based incremental loads
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Structured fact and dimension tables at employee by date by application level
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Centralized KPI calculations in SQL
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Clean separation between ETL and reporting layers
This approach created a trusted analytics foundation for workforce reporting.
Results
The new platform delivered measurable operational gains and improved confidence in reporting.
Key performance outcomes included:
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10 to 15 percent improvement in workforce productivity
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15 hours saved per week through automated data processing
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Daily, reliable data refreshes without manual intervention
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Consistent productivity metrics used across teams
Leadership now has a scalable data foundation that supports workforce trend analysis, department comparisons, and future initiatives such as predictive productivity and AI-driven insights.