• Written by Azhar Aliyar, Director

A maintenance planner at a power generation plant checks the outage schedule to determine which equipment can be taken offline for routine maintenance, but the planner's decision is only as good as the data that informs it. The planner relies on accurate and up-to-date information about the plant's assets, maintenance history, and regulatory requirements. However, the complexity of energy and utilities operations, with their asset-heavy and geographically dispersed infrastructure, can make it difficult to ensure that the data is reliable and consistent.

Unique challenges in energy and utilities

The energy and utilities sector presents unique challenges that can make it harder to achieve enterprise data readiness for AI. Outage planning, for example, requires careful coordination and planning to minimize the impact on customers and ensure compliance with regulatory requirements. The regulatory reporting burden is also significant, with utilities companies required to submit detailed reports on their operations, maintenance, and performance. Additionally, the geographically dispersed nature of energy and utilities assets can make it difficult to collect and integrate data from diverse sources.

These challenges can lead to data inconsistencies and gaps, which can have serious consequences. For instance, inaccurate data on asset condition or maintenance history can lead to unexpected outages or equipment failures, resulting in costly repairs and reputational damage. Therefore, it is essential to address these challenges and ensure that the data is accurate, complete, and consistent.

Field-level meaning and documentation

Column names in a database are often assumed to be sufficient documentation, but this is not the case. Field-level meaning refers to the specific context and definition of each data field, which can vary depending on the system, process, or location. In energy and utilities, field-level meaning is critical, as small differences in interpretation can have significant consequences. For example, the term "asset" might refer to a specific piece of equipment, a location, or a system, depending on the context.

A thorough understanding of field-level meaning is essential to ensure that the data is used correctly and consistently. This requires detailed documentation and data governance processes to ensure that the data is accurate, complete, and consistent. By investing time and effort into understanding field-level meaning, organizations can avoid costly mistakes and ensure that their data is reliable and trustworthy.

Systems that get left out of scope

When implementing AI solutions, it is common for certain systems to be left out of scope, either due to complexity, cost, or perceived lack of importance. However, these systems often contain critical data that is essential for accurate decision-making. In energy and utilities, systems such as SCADA, GIS, and CMMS are frequently left out of scope, resulting in partial answers and incomplete insights.

For instance, a category manager comparing two suppliers might not have access to data on the suppliers' performance history, maintenance records, or compliance with regulatory requirements. This can lead to suboptimal decisions and missed opportunities. By including all relevant systems in the scope of the AI solution, organizations can ensure that they have a complete and accurate understanding of their operations and can make informed decisions.

To learn more about how AsscherAi can help you include all relevant systems in your AI solution, visit our website here.

Gaps and inconsistencies in data

Stockouts, backfills, and other gaps in data can teach a model the wrong lesson, leading to inaccurate predictions and decisions. In energy and utilities, data gaps can occur due to equipment failures, maintenance outages, or changes in operational processes. These gaps can be filled using various techniques, such as interpolation or imputation, but these methods can introduce new errors and biases.

Therefore, it is essential to carefully evaluate the data and identify potential gaps and inconsistencies. By doing so, organizations can develop strategies to address these issues and ensure that their data is accurate, complete, and consistent. This might involve implementing data validation and verification processes, investing in data quality tools, or developing custom solutions to address specific data challenges.

Limitations and exceptions

While AsscherAi can help organizations achieve enterprise data readiness for AI in energy and utilities, it is not a solution for every problem. There are situations where AsscherAi might not be the best choice, such as when the data is highly unstructured or requires significant manual processing. In these cases, other solutions or approaches might be more suitable.

Additionally, AsscherAi is not a replacement for human judgment and expertise. While AI can provide valuable insights and recommendations, it is essential to have experienced professionals who can interpret and validate the results. If you have questions about how AsscherAi can help your organization or would like to discuss your specific use case, please contact us to learn more.