Table Of Contents
- What was happening
- The impact
- Approach
- Implementation
- Technical stack
- Business impact
- What did not change
- Key takeaway
- Final note
In one of our recent projects, reporting cycles weren’t measured in minutes or hours.
They were measured in weeks.
Data existed across multiple systems. Teams were working with it daily. But getting a consolidated, reliable view across finance and operations still took 10–14 days.
Not because the data was inherently complex.
Because it was distributed across systems without a unified data layer.
What was happening
The organization used:
- SAP S/4HANA
- SAP FSM
- SharePoint
- other operational sources
Each system functioned independently, but there was no centralized layer for consistent data integration.
A typical reporting cycle involved:
- Extracting data from multiple systems
- Aligning formats and structures manually
- Reconciling inconsistencies
- Reapplying business logic for each reporting cycle
These steps were repeated for every reporting requirement.
The Impact
- Reporting cycles extended up to 10–14 days
- KPI definitions varied across teams
- Business users relied on manually prepared reports
- Scaling to 50–100M+ records per year introduced performance and consistency challenges
The limitation was not the availability of tools, but the absence of a structured and centralized data foundation.
Approach
The objective was to establish a centralized data platform with:
- standardized data ingestion
- repeatable transformation workflows
- governed data models for reporting
The implementation used Microsoft Fabric and Power BI.
Implementation
Centralized data platform
A Lakehouse was implemented on OneLake using a medallion (Bronze–Silver–Gold) architecture:
- Bronze: raw ingested data from source systems
- Silver: cleansed and standardized datasets
- Gold: curated datasets aligned with reporting requirements
This provided a centralized data layer for downstream analytics.
Data ingestion and processing
- Data was ingested from SAP S/4HANA, SAP FSM, SharePoint, and flat files
- Fabric Dataflows Gen2, Pipelines, and Notebooks were used for ingestion and orchestration
- Transformations were implemented using Fabric Notebooks (Spark) and pipeline activities
- Data pipelines were scheduled at defined intervals (e.g., daily batch processing), not event-driven or streaming
These workflows reduced manual intervention and improved consistency of data preparation.
Data modeling and access
- Power BI Semantic Models were built on top of Gold layer datasets
- DAX was used to define KPIs and business logic
- Row-Level Security (RLS) was implemented for role-based data access
This ensured that reports referenced a consistent data model.
Visualization layer
- Reports were developed in Power BI Service
- Direct Lake mode was used to query data from the Lakehouse without requiring data import into the model
- This reduced query latency compared to import-based models, depending on model design and storage optimization
- Data freshness remained dependent on upstream pipeline execution schedules
Automation and orchestration
- End-to-end data workflows were orchestrated using Fabric Pipelines
- Manual data extraction and consolidation steps were replaced with scheduled processes
- Pipeline dependencies and execution sequences were configured to maintain data consistency across layers
Governance, monitoring, and security
- Access control was managed using Azure AD
- Data models and transformations enforced standardized definitions
- Monitoring and alerting mechanisms were configured to track pipeline execution and failures
Technical stack
- Ingestion & orchestration: Fabric Dataflows Gen2, Pipelines
- Processing: Fabric Notebooks (Spark)
- Storage: OneLake (Lakehouse architecture)
- Modeling: Power BI Semantic Models (DAX, RLS)
- Visualization: Power BI Service (Direct Lake mode)
- Security: Azure AD
Business impact
- Reporting timelines reduced from 10–14 days to under 24 hours, based on scheduled pipeline execution
- 20+ reports transitioned from manual preparation to automated workflows
- 60+ users accessed centralized datasets through role-based access
- 10+ dashboards were built using standardized KPI definitions
- Automated pipelines with monitoring improved consistency of data delivery
What did not change
- Data processing remained batch-based (scheduled execution)
- No real-time or streaming architecture was introduced
- Data quality depended on defined transformation, validation, and governance practices
Key takeaway
Most reporting delays were not caused by reporting tools. They were caused by the absence of a centralized and consistent data layer.
Once data ingestion, transformation, and modeling were standardized:
- reporting became repeatable
- data definitions became consistent
- access to insights improved
Final note
This implementation replaced fragmented, manual reporting workflows with a structured analytics platform built on Microsoft Fabric.
The system now supports:
- centralized data storage
- automated and scheduled data pipelines
- governed semantic models for reporting
It also provides a foundation that can support additional analytical workloads without requiring a redesign of the core data architecture.
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