This Utility University course is open to any attendee with an all-access or utility all-access badge. These courses are first come, first served onsite in Dallas, however, pre-registration is still required. To pre-register, please visit the registration resource center or reach out to info@distributech.com.

Utility companies today are facing more demands than ever when it comes to asset data. The risk of having inaccurate data driving daily work can lead to non-compliance, inefficiencies, and equipment malfunction. Not only is data critical for service delivery, equipment maintenance and compliance, now it drives business intelligence, strategic decisions, and grid modernization priorities. And, the sensitivity of artificial intelligence (AI) to data accuracy, amplifies the need for high-fidelity data and precise management. 
 
Imagine leveraging AI beyond mere reporting. What if AI could address asset data challenges like quality AND completeness, enhancing your operations and business functions? 
 
The broad scope of systems and business functions supported by asset data reveals the extensive applications of AI. Data enrichment, intelligent automation, and enhanced accessibility are just a few examples of how business needs can be rapidly improved with AI. Tasks such as change detection and spatial corrections become faster, cheaper, and easier to implement. With the right tools around that data, quality continues to improve organically through routine business operations. 
 
AI is a powerful tool for asset management, but its capabilities are limited by the quality of your data, affecting reporting and accurate responses. However, by rethinking AI's role, it can become a catalyst for improving and enhancing data quality. This new approach allows AI to help answer questions within your data, transforming challenges into opportunities for ongoing quality improvement. 
 
By implementing AI to monitor and manage data quality continuously, utility companies can achieve a self-sustaining system where data quality is perpetually enhanced through everyday business processes. This approach transforms AI from a tool that merely relies on good data to one that actively contributes to creating it. As a result, the challenges of data management become opportunities for improvement, leading to more informed decision-making and better operational efficiency. 

Prerequisites: 

• Have past experience with common data challenges and business system strategy / implementation

Acquired Knowledge: 

• Leveraging AI for data enrichment
• Preparing data and systems for modernization
• Identifying data that needs correction / expansion
• Thinking about data from an enterprise approach, with tools designed to enrich data with daily work