Transforming decision-making: The value of a Data Quality process in companies.

(AI). This evolution results in a significant increase in the volume of data to be processed and managed, posing challenges in ensuring complete and reliable monitoring, while also opening opportunities to optimize decision-making, improve operational efficiency, and strengthen business resilience—yielding financial and reputational advantages in a dynamic environment.

In this context, data quality becomes a critical factor, as any inaccurate, incomplete, or inconsistent information can compromise operational and strategic decisions, affecting efficiency, regulatory compliance, and operational safety.

Faced with this reality, XM –which is the Independent System Operator (ISO) and Wholesale

Energy Market Operator (EMO) in Colombia– identified the need to evolve its data management practices to meet the sector’s challenges. The organization faced limitations stemming from isolated practices, lack of formal guidelines, and absence of metrics and indicators, making it difficult to ensure effective data management and continuous improvement. To overcome these gaps, through a governance and quality program based on the

PDCA (Plan-Do-Check-Act) cycle and supported by the integration of business processes, technology, and organizational culture, XM improved master data quality by over 7 percentage points in five years, incorporated continuous monitoring of 100% of its master data, and established systematic correction and prevention plans. Master data refers to data representing key business entities described through attributes — for example, a power transformer defined by its electrical parameters. This experience consolidates data as a strategic asset for informed decision-making and offers a replicable approach for other energy organizations.