Telecom network operators face several data challenges, including as-built backlogs, data silos, and legacy system limitations. Manual data entry and cumbersome workflows contribute to these issues, often resulting in poor data quality and missing information. Addressing these challenges involves not only improving data collection processes but also implementing robust data management practices to ensure data integrity and accessibility.
Like many others, the telecom industry is gradually moving from manual operations to fully automated and autonomous processes. This progression is often mapped using a framework that includes levels from zero (completely manual) to five (fully autonomous). For instance, Level 0 represents manual operations, akin to driving a car manually. As we advance, each level incorporates more automation, leading to Level 4, where processes operate with minimal human intervention, and Level 5, where systems can function entirely independently.
However, for these technologies to be truly transformative, the accuracy of data is crucial. Inaccurate data can lead to flawed automation, which in turn affects the overall efficiency and reliability of operations. Let’s look into three key areas—data capture, data migration, and data consolidation— where automation and AI can be effective in promoting data integrity while also replacing manual processes.
Data capture
Data capture is the process of collecting information and converting it into a digital format for processing, storage, and analysis. AI-powered tools like computer vision and machine learning are transforming data capture processes by automatically identifying and recording details from the field. These technologies significantly reduce the need for manual data entry, thus improving the accuracy and reliability of the collected data.
Tools like SSP Vision can capture real-time data, including the asset type, by leveraging mobile devices and augmented reality. The integration of voice recognition allows for labeling and identifying specific items in the field, improving usability and efficiency. Furthermore, AI ensures that the captured data is validated against predefined rules, reducing the chances of duplicate or incorrect data entries.
Data migration
Data migration is the process of transferring data between different storage systems, formats, or computing environments. Automating data migration processes involves deciding on a preferred working format; determining tools, software, and libraries needed; developing and deploying processes; and designating processing frequency. AI can suggest field mappings and transformations, reducing the time and cost associated with these tasks. However, human oversight is required to validate automated output during all facets of the migration process, especially as it is refined.
Data consolidation
Data consolidation involves integrating data from multiple sources into a single system of record. Creating a continuous integration process that can handle a variety of data, including user-entered data and third-party system data, is essential for maintaining up-to-date and accurate information in a consolidated system. Given the volume and variety of data, automation becomes critical in making the consolidation process sustainable and efficient.
As AI and automation technologies continue to evolve, their applications in data management will expand. The symbiotic relationship between AI and high-quality data will drive more sophisticated and reliable automation processes, ultimately leading to more efficient and effective network operations. As we progress, leveraging AI and automation will evolve from being a trend to becoming essential. By prioritizing automation and AI now, telcos can lay the foundation for a more efficient and intelligent future.
This post is based on our recent webinar, Leveraging AI and automation to streamline data collection and management. For additional information and insights, check out the full session on-demand here.
