A Multi-Region Energy Provider Standardised 20M+ Energy Records
About The Client
The client is a large energy and utility provider serving residential, commercial, and industrial customers across multiple regions. Its operations generated significant volumes of meter, consumption, asset, billing, customer, and operational data every day. Information was distributed across meter data management systems, customer information systems, asset platforms, billing applications, and legacy databases. As data volumes increased, inconsistencies and fragmented records made reporting, reconciliation, and operational analysis more difficult. The organization needed a scalable energy data management model to standardise critical information, improve data quality, integrate multiple sources, and create a reliable foundation for operations, billing, reporting, and analytics.
0M+
Energy Data Records Managed
0+
Data Sources Integrated
0%+
Data Accuracy
0%
Reduction in Data Exceptions
The Challenge
The organisation generated large volumes of data across meters, customers, assets, field operations, billing processes, and energy consumption activities.
However, information was spread across multiple operational and enterprise systems. Customer identifiers, meter records, asset information, consumption data, and billing records did not always align across platforms.
Meter and consumption data created particular challenges. Missing readings, duplicate records, unexpected values, and inconsistencies between meter and customer information required investigation before downstream processes could continue.
Data quality issues could also affect billing and reporting. Incorrect meter-to-account relationships or incomplete consumption information created additional reconciliation work for operational teams.
Legacy systems added further complexity. Historical energy and customer information remained valuable, but different data structures made integration with modern platforms difficult.
The organisation needed a structured data management framework capable of handling high-volume energy information while maintaining accuracy, consistency, and traceability across connected processes.
What Did KGS Do
KGS began by assessing the client's energy data landscape, source systems, data flows, record structures, quality issues, integration requirements, and existing governance processes.
More than 20 million meter, consumption, customer, asset, billing, and operational records were brought within structured data management workflows.
Information from 40+ source systems was mapped and standardised around agreed data definitions and formats. Validation controls identified missing values, duplicate records, inconsistent identifiers, unexpected consumption information, and other quality issues.
KGS strengthened relationships between meter, customer, account, asset, and consumption records to improve consistency across operational systems. Reconciliation workflows helped identify differences between meter data, billing information, customer records, and other connected datasets, with exceptions categorised and routed to the appropriate operational teams for investigation.
Data quality monitoring and reporting provided visibility into recurring issues, exception volumes, source-system performance, and remediation progress.
The Results
- Managed and standardised 20M+ energy and utility data records
- Integrated information from 40+ operational and enterprise data sources
- Maintained 99%+ accuracy across targeted energy datasets
- Reduced recurring data exceptions by 45%
- Improved consistency across meter, consumption, customer, asset, and billing information
- Created a stronger data foundation for energy operations, reporting, and analytics
What did the client say?
“KGS helped us establish much greater consistency across energy data that previously sat in different systems and formats. Meter, customer, consumption, and billing information is now easier to reconcile, and data issues are identified through a much more structured process. Our teams spend less time correcting information and have greater confidence in the data supporting operations and reporting.”
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