By Vaishnavi Medikundam, HEXstream data analyst

Utilities are investing heavily in AI-powered analytics to improve reliability, predict outages, and enhance customer experiences. But success with AI isn’t just about having clean data. It’s about having clear rules and accountability for how that data is used. Without that structure, even the smartest AI models can produce inconsistent results and create
compliance risks. 

The importance of data quality in utilities

Utilities generate massive amounts of data from meters, work-management systems, and customer platforms. Each service request, meter reading, and transformer update adds to a growing pool of operational information that drives outage management and predictive analytics. Yet data from multiple legacy systems often varies in format, accuracy and completeness. Inconsistencies or duplicates can delay reporting and reduce service reliability. 

High-quality data empowers decisions that are based on facts rather than assumptions. It improves operational efficiency and reduces regulatory risks. However, data quality isn’t enough without governance. Without proper governance, maintaining quality over time becomes nearly impossible. 

The role of data governance

Data governance provides the structure that keeps information reliable, accessible and secure. It defines ownership and validation of standards. Strong governance ensures that data is not only accurate, but meaningful in context. 

When utilities establish clear governance policies, they facilitate easier collaboration and consistency among teams. For example, integrating information about field crews, reported outages, and affected customers can streamline service restoration. This alignment enables utilities to respond more quickly to outages while gaining clearer insights into network performance and customer impact. 

Building strong data governance

Establishing effective data-quality and governance practices takes time and commitment. It’s not a one-time effort but a structured, ongoing process. Let’s examine the steps: 

1. Assess current data health: Identify all data sources, define key metrics, and evaluate existing governance practices. 

2. Define ownership: Assign clear accountability by designating data owners responsible for maintaining quality within their domains

3. Establish standards and policies: Create consistent rules for data entry, validation and integration across systems

4. Monitor and measure: Use automated checks and dashboards to continuously track data quality and governance progress

5. Foster a data-driven mindset: Offer training and encourage teams to rely on governed data for everyday decisions

Conclusion

As utilities modernize their operations, the quality of their data will shape the success of their digital transformations (and, ultimately, their overall success). Strong governance and data-management practices help ensure that every insight and decision is built on reliable information. And that reliability is what, ultimately, enables effective AI. 

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