This leading renewable energy provider operates across 38 locations in the UK and North America, managing a vast network of solar, wind, and biomass initiatives. For an organization of this scale, maintaining high-quality data that is both accurate and secure is non-negotiable. With sensitive data scattered across various platforms—Azure, PowerBI, Oracle, SQL Server, and AWS Glue—the company needed a solution to monitor and enforce data quality and privacy in near real-time, ensuring both operational integrity and regulatory compliance.

 

Enter Alex Solutions. The company turned to Alex for its advanced data quality monitoring capabilities and robust data sensitivity profiling. By implementing a real-time data quality solution, Alex delivered granular visibility across critical data elements (CDEs), while providing near real-time insights into data accuracy, reliability, and privacy. Key features such as data profiling, lineage tracking, and sensitivity analysis enabled the organization to monitor and protect its data across multiple platforms actively.

 

Alex’s ability to automate data cataloging and profiling was vital in meeting the company’s needs. The solution ingested over 200 million data rows from 60 key databases, applying 24 distinct data quality rules to validate schema integrity and completeness and detect anomalies. Alex’s sensitivity profiling capabilities were precious, allowing the organization to define and apply 25 sensitivity types—ranging from personal identifiers to proprietary business data—across its entire data landscape, ensuring compliance with privacy standards.

 

Why did the company choose Alex? Simply put, due to its comprehensive suite of tools that seamlessly managed data quality and privacy across complex enterprise environments. The platform’s ability to apply real-time data checks within data pipelines and automatically tag sensitive information was exactly what the organization needed to maintain confidence in its data for operational decision-making and regulatory reporting.

 

The implementation was executed with precision. Automated connectors integrated with more than 300 databases, enabling Alex to profile and apply rules across vast data sets. With the ability to continuously monitor data quality and privacy at scale, the organization gained a powerful tool for data certification and compliance that will grow with them as their data expands.

 

The immediate benefits were clear: near real-time data quality monitoring helped identify potential issues before they impact operations, while a framework for sensitive data classification ensured that privacy risks were mitigated and compliance requirements met. This was only the beginning; the long-term impact of Alex’s solution was even more transformative. The company now had a scalable model for ongoing data certification, providing consistent monitoring as its data landscape evolved.

 

Over 200 million data rows were profiled across 60 databases, while 25 sensitivity types were tagged and tracked. The platform’s 24 types of data quality rules significantly improved data accuracy and privacy compliance, ultimately reducing risks associated with data mismanagement.

 

For this renewable energy leader, Alex has improved data quality and privacy management and built a foundation for ongoing compliance and security, enabling the company to manage its ever-expanding data needs confidently. With the ability to monitor and protect data at scale, Alex has proven to be a crucial partner in the company’s mission to lead the way in sustainable energy.

 

 

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Customer Overview

 

• Region: UK and North America

• Industry/Sector: Renewable Energy

 

Challenge

 

• Capability Area: Data Quality and Privacy Management

• Data Complexity: The organization faced the challenge of ensuring data quality and privacy across a vast network of 38 locations, with sensitive data dispersed across various platforms.

• Technical Landscape: Data was stored across multiple systems—Azure, PowerBI, Oracle, SQL Server, and AWS Glue—complicating the management and monitoring of data quality and privacy.

• Operational Inconsistencies: The lack of a real-time, centralized solution to monitor data accuracy and apply sensitivity profiling created gaps in ensuring consistent and compliant data management.

 

Technologies

 

• Azure

• PowerBI

• Oracle

• SQL Server

• AWS Glue