Predicting the likelihood and timing of an asset breakdown enables us to act preemptively, ultimately saving valuable resources such as time, money, and energy. We have partnered with several companies over the last ten years, leveraging our expertise to help them overhaul their AI-driven predictive maintenance models. We recently spoke with Enel Group, a leading multinational electricity and gas manufacturer, who has been our client for the past two years, to discuss how they benefited by partnering with a data annotation company like iMerit.
Enel Group collaborated with us for data labeling and annotation for asset inspection for rapid failure detection, faulty asset isolation, and network reconfiguration. The company is automating electrical distribution network analysis using AI and machine learning models and needs high-quality training datasets to increase the accuracy of these models.
This blog summarises key takeaways from our conversation with Mario Larcher, Head of Computer Vision at the Enel Group.
Benefits of Outsourcing Data Annotation Needs
Enel Group regularly inspects its 2.3 million kilometers-long electric distribution network, which is almost six times the distance between the Earth and the moon. The sheer amount of data that needs to be analyzed includes high-resolution photographs taken every few meters and 3D point clouds obtained through LiDAR technology. Manual analysis of this data is cumbersome, prone to errors, and can lead to inaccurate results and increased costs. With our specialized expertise in labeling these data sources, we increased the speed and accuracy of asset inspection, making it more efficient and reliable.
Working with iMerit helped the Enel team improve its data analysis capabilities and enabled them to focus on their core business objectives and growth. We helped them streamline their operations, reduce costs, and scale.
“Setting up data labeling is not a trivial task. Especially in deep learning, it is perhaps one of the most delicate and critical steps with the strongest potential impact on the final result,” said Mario Larcher, Head of Computer Vision at Enel Group.
Challenges with Outsourcing Data Annotation
Our project with the Enel Group had its challenges, particularly with communication. The language barrier posed a significant challenge as the Enel team in Brazil was more comfortable communicating in Brazilian Portuguese rather than English.
Miscommunication can significantly impact project efficiency, particularly in cases where a clear and mutual understanding of specific guidelines and ground truth is crucial. Given that the Enel project required labeling very complex electric elements and components, it was essential to have a clear and mutual understanding of the guidelines. Despite the challenges with communication, our collaboration with Enel Group was a success, and we delivered results that exceeded their expectations.
When outsourcing data annotation projects, it can be challenging to ensure annotation accuracy and consistency with the guidelines provided. Hence, it is crucial to have robust quality control measures, including regular checks, frequent communication, and feedback loops with the data annotation partner.
At iMerit, we understand the importance of quality control in data annotation. That is why we have developed a powerful tool called iMerit Data Studio, which allows real-time monitoring and secure access to project analytics and insights, edge case management, and other capabilities through the client account. With iMerit Data Studio, our clients can have complete transparency and control over the data annotation process to ensure quality and accuracy.
Ensuring Successful Collaboration with a Data Annotation Partner
Based on our experience with the Enel Group and several other leading companies in different industries, here are some of the best practices to ensure successful collaboration with a data annotation partner:
We come across many clients that need clear project definitions and guidelines for data annotation. In this case, it can take a while for the client stakeholders to define those, thereby delaying the overall project. By choosing a data annotation partner with domain expertise, you can get a quick start on your data annotation projects. For many of our clients with undefined guidelines, we offer quick start guides built by our team of solution architects and subject matter experts to help accelerate ML data pipelines.
Setting Clear Expectations
Clearly define the project goals, timelines, annotation guidelines, and specific requirements at the outset. These will ensure that both parties are on the same page and that there is a shared understanding of what is needed.
“By combining the expertise of iMerit, the technical knowledge of Enel developers, and the domain knowledge of Enel Business, we were able to define clear guidelines in a tight time frame,” said Mario Larcher, Head of Computer Vision at Enel Group.
Closed Feedback Cycle
Open communication is essential to ensure that the project is progressing as planned. With regular meetings or check-ins to discuss progress, provide feedback, and address any concerns, you can ensure adherence to project guidelines. Another important aspect of having a closed feedback cycle is to have a structured feedback mechanism. Every meeting should have a structured set of questions to be addressed by the client or the partner, outline the expected outcomes of the discussion, and identify upcoming milestones.
Collaborative and Transparent Environment
Access to real-time analytics and insights allows for effective quality control throughout the annotation process. While working with a data annotation partner, you must monitor data labeling and annotation progress to detect and address potential issues or errors. We offer complete transparency to our clients and share regular reports and dashboards to monitor project status.
The demand for highly customized datasets curated and annotated by skilled data experts will remain constant in the upcoming years with the rising adoption of AI solutions across industries. iMerit is a highly experienced data annotation service provider with a diverse clientele spanning industries such as geospatial analysis, agriculture, medical AI, and autonomous mobility.