Predictive Maintenance
The intelligent data-driven strategy implementation of equipment to track and evaluate their performance using real-time data is known as predictive maintenance of tower infrastructure. Predictive maintenance is proactive instead of reactive, involves shorter periods of scheduled downtime, and may be carried out to examine the information and raise red flags to address any tower-related problems in its early stages whereas preventive maintenance depends on best practices and based on past data.
An improved technological architecture that can smoothly integrate with a centralized data collecting and dissemination system for enhanced decision-making is required to carry out successful predictive maintenance. Predictive Maintenance uses the vast amount of data that towercos have on the operation of their holdings, combined with the AI and machine learning expertise of the telecom asset management software that the towerco is using, to produce reliable forecasting schedules for maintenance. Predictive Maintenance is typically performed for assets such as power, cooling, housing, etc.
Towercos can investigate the cross-pollination of information from other relevant operations to make sure the AI has just enough previous data to be accurate. The data from the contract administration documents, for instance, can be supported by data on the overall profit and present situation of telecom assets, which in turn could help towercos review SLAs and bargain improved service conditions with the MNOs. Similarly, the servicing and breakdown data can be supported by backup electricity production and fuel usage data. By resolving difficulties in real-time, integrating several data sources renders the system extremely centralized and contributes to service continuity. Having such a system in place aids in meeting the information demands of the towerco, as well as the MNOs and outside service providers.

Digital Twins in Tower Management
To advance data-backed tower management, it is vital to strike a balance between data complexity and usability. Tower management is all about eradicating data silos, maximizing the data at hand, and transforming it into meaningful insights to maximize both operational and maintenance effectiveness.
All of the asset-related data is located at the site; thus, it is crucial but also time-consuming to examine the tower and other site-related equipment to collect this information. The whole telecom sector is presently focused on developing digital twins to collect this information. These "digital twins"—which are produced using software for drone mapping and photogrammetry—are virtual 3D models and maps of the objects being investigated. In order to build a comprehensive representation of the place or item under inspection, the drones capture hundreds of overlapping photographs.
Before the invention of digital twins, data was gathered by hand. Taking manual photos has its own limits and cannot give the precise angles or the amount of data that an aerial assessment can. Furthermore, aerial assessment spares the field operators an exhausting climb and the frequency of inspection trips to the site. Hence, with the primitive methods, the resulting data collection required a lot of effort and was inconsistent and inaccurate.

The asset's size and orientation may be determined using software now available in the market, and rust and other defects can be found on the asset as well. The information gathered in this way may be shared and used, making it possible to gather and access asset information instantly. In order to establish predictive maintenance procedures to address any current or anticipated defects and chances for site development, which is where the actual value of tower management resides, management at towercos may now study the data that is now accessible to them. The advantages of a digital twin system, however, are insufficient without the foundation of an all-encompassing site management platform that stores site and resource data.