Enterprise Asset Intelligence Platform for A Leading North American Clean Power Generation Company
Our Clients
Engagement Overview
Our client is one of North America’s largest and most diverse clean power generation companies, operating more than 20 nuclear and hydroelectric plants. They needed a clearer, faster way to see how assets were performing, where risks were emerging, and how operations linked to financial outcomes. Instead, analytics and reporting were spread across BW on HANA, Power BI, and SQL, with heavy manual reconciliation and delayed insight.
TechBlocks was selected as their full-stack engineering partner to modernize the mobile application and IoT ecosystem, stabilize device-to-cloud connectivity, and prepare the platform for scalable growth and regulatory readiness.
Key Challenges
The client’s data & analytics landscape was fragmented across multiple SAP & non-SAP systems.
Finance, operations, and asset management teams worked from silos, reconciled numbers by hand, and waited too long for insights. At the same time, the broader SAP estate added complexity without delivering real-time visibility or predictive capability at scale.
Fragmented Analytics & Manual Reconciliation
Analytics and asset reporting lived across BW on HANA, Power BI, and SQL-based reporting. Each group had its own view of performance. Heavy manual reconciliation was required to align numbers, which delayed insight and increased the risk of error.
Complex SAP Landscape without Real-Time Visibility
The SAP estate included SAP ECC, BW, Datasphere, and S/4HANA. Despite this investment, the organization lacked real-time visibility into asset and financial performance across 20+ plants. Data moved slowly between systems, and it was hard to build a single, trusted picture.
Limited Predictive & Scalable Asset Intelligence
Siloed data and legacy reporting meant limited predictive maintenance capability and constrained scalability. It was difficult to model asset risk, forecast OPEX, or simulate scenarios across the fleet, which kept decision-making largely reactive.
Governance Gaps & Inconsistent Data Control
Governance was inconsistent across systems. Different tools and models created duplicated logic, unclear lineage, and gaps in control. This slowed decision-making, made it harder to trust the data, and undermined the reliability required for a critical infrastructure provider.
Approach & Solutions
1. Business Data Fabric & Unified Platform Foundation
We modernized the ecosystem with a unified Business Data Fabric that connected SAP and non-SAP sources.
- Designed a Business Data Fabric anchored in SAP Datasphere, Azure, and Snowflake as the core data and analytics platform
- Connected SAP S/4HANA and EAM/PM systems via Azure Data Factory for structured enterprise data flows
- Modeled cross–SAP and non-SAP data in DataHub with clear semantics and lineage to support consistent reporting
- Created a single source of truth for asset intelligence that could support both operational and financial use cases
2. SAP & Field Data Integration for Real-Time Visibility
We brought operational technology data into the same fabric as enterprise data.
- Streamed operational telemetry from SCADA and AVEVA PI into Azure through Azure IoT Hub and Event Hubs
- Integrated SAP S/4HANA and field systems so asset, maintenance, and financial data could be analyzed together
- Enabled dashboards that refreshed every five minutes to give near real-time visibility across more than 20 plants
- Supported monitoring of asset performance, alarms, and operational KPIs in a unified view
3. Predictive Maintenance & Financial Planning Analytics
On top of the integrated data layer, we delivered predictive and planning capabilities.
- Built predictive maintenance models using Azure ML to anticipate failures and reduce unplanned downtime
- Developed OPEX forecasting models to improve budgeting and operational planning across plants
- Enabled scenario simulation to test the impact of different operating conditions and investment choices
- Used SAP Analytics Cloud and Power BI to support business-led data profiling, cleansing, and visualization for these use cases
4. Governance, BI Modernization, & Self-Serve Analytics
We closed governance gaps and enabled business users to build on a governed, modern BI stack.
- Retired BW on HANA and centralized governance in SAP Datasphere to reduce duplication and enforce consistent controls
- Enabled SAP Analytics Cloud and Power BI as strategic BI tools for self-serve analytics and reporting
- Implemented automated data quality checks to cut reconciliation effort and improve trust in reports
- Supported more than 170 self-serve analytics use cases, allowing business teams to explore data without waiting on IT
Impact & Results
The client moved from fragmented, manually reconciled reporting to a unified enterprise asset intelligence platform
SAP and field data were brought together, predictive models reduced unplanned downtime, and business teams gained a governed, self-serve analytics environment that accelerated decision-making and strengthened reliability across the fleet.
Reduction in reconciliation
Automated data quality checks and centralized modeling delivered an 80% reduction in reconciliation effort. Teams spent far less time aligning numbers and more time acting on insights.
Reduction in unplanned downtime
Predictive maintenance models reduced unplanned downtime by 15%. Earlier detection of issues allowed maintenance teams to intervene before failures impacted generation and reliability.
Faster reporting
Reporting became 42% faster, with versioning and approvals 96% quicker. Standardized data models and modern BI tools streamlined how reports were created, reviewed, and deployed.
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