The Point Solution Trap

A towerco identifies a specific operational need. Maybe it is AC metering — they need to understand power consumption at their sites to manage energy costs. So they deploy an AC metering solution, likely with its own modem connection. 

A year later, the same towerco needs IP cameras at some remote sites for security, or a diesel fuel probe for accurate diesel fuel measurements, or rectifier information.   They might get a camera solution from a security vendor, an IoT solution for fuel monitoriing, and an element management software from a particular rectifier vendor.

A year after that, a major tenant negotiates for energy billing — they want to pay for what they consume rather than a flat rate. The towerco works to deploy a billing method.

Maybe the towerco recognizes the downsides to using single point solutions, and an attempt is made to consolidate point solutions using hardware-based RMS (Remote Monitoring Systems), RTU, or other broad-based software solutions.   These might be better, at least an intermediate step at reducing the number of modem connections to the site, but have various problems, fundamentally in what they don't solve.

These solutions are how data silos are built, not through bad decisions, but through a series of locally reasonable ones. Maybe each of the systems described above solved a real problem, but the collective result is fragmentation that makes every subsequent problem harder to solve, because each new need now has to navigate around the silos that already exist.   Every tower company and telecom operator without the luxury of building a network from scratch suffers from this problem.

It is like accumulated debt.  Ultimately the goal is standardization, and this debt will have to be paid sooner or later if operators really want to solve the operational problems in their networks.  

What a Data Strategy Changes

Unlike point solutions, a towerco data strategy doesn't start with an immediate operational problem. It starts with different questions: what mix of equipment and data is already available in my network, what point solutions have I tried, and do I have site network connectivity?

For a grass-and-steel towerco, this data strategy might initially need to answer only a handful of questions: grid power status, basic site availability, anomalies that warrant attention. Simple obligations, simple data needs, but the foundation should still be built to expand, because the obligations won't stay simple forever.

For a managed infrastructure towerco, the data foundation needs to support more: power delivery metrics, backup performance tracking, cooling status, potentially renewable energy contribution and management. These are the inputs to the KPI calculations — Power Availability, Power Backup, Cooling Availability — that define SLA compliance in this model. The specific KPIs that matter depend on what the towerco has committed to its tenants. The data foundation that makes them measurable is the same regardless.

The point solutions a towerco would otherwise purchase to address each of these needs individually become applications of the data foundation rather than separate systems creating separate datasets.  Standardized site data can be combined together in different ways or with external data like energy tariffs to deliver many additional functions, like energy billing.  Each additional use is made possible by the SAME underlying data created as part of your overall site data strategy.

Why It Matters Now

The argument for a data strategy over point solutions has always been sound. What has changed in 2026 is the availability of open-source tools that can create standardized data cost effectively, and the arrival of AI that will only operate on standardized data.

Every point solution deployed today creates a data silo that is, by design, inaccessible to AI systems that were not part of that vendor's ecosystem. An AC metering platform that does not expose its data in a standardized format is not just a monitoring limitation, it is a barrier to every future optimization algorithm, anomaly detection model, and predictive maintenance system that will want to consume that data.

Towercos purchasing point solutions today are not just creating integration debt for their current operations. They are making a bet about the future of their data: either that they will never want AI systems to have access to it, or that their current vendors are more suited to manage any AI usage on their data than they are themselves. That is an increasingly difficult bet to justify.

A data strategy built on open, normalized, vendor-agnostic telemetry that is owned by the customer has essentially unlimited upside as AI tooling matures. You could expect to gather 50-100 energy and other parameters per minute from every tower location in your network, and this data has value.   A massive, standardized, growing, historical dataset grows more valuable over time, not less, and if you have a large head start on your competitors, the value is even larger.

The Practical Path Forward

The data strategy approach does not require replacing equipment that already exists in your network. In most towerco environments, the equipment that can supply the right data is already deployed, including all the point solutions mentioned above, as well as modern rectifiers, battery management systems, environmental sensors, and power distribution equipment that has been capable of producing granular telemetry for years.  New open-source tools (and knowledge of how to use them) standardize that data without asking the towerco to rip out and replace anything.

A proper data strategy is additive, not disruptive. Whatever alarm and monitoring infrastructure a towerco has invested in, whatever NOC processes and ticketing workflows have been built around existing systems that are working well, those stay in place. The data layer operates alongside them, providing the foundation that existing systems were never designed to deliver, and could even supply data back into these same systems.  Personnel in your organization are able to help work with this data.  It is a path to your organization and personnel building AI-related skills and advantages.  A provider of a data strategy and standardized data, like ClaritechIO, is not a gatekeeper to the use of the data.   The data is ultimately yours.   

You benefit from the ability to answer new questions from a single, expanding foundation, without deploying a new silo every time a new question arises.  If you want to deploy cameras, then the answer is to just examine how video fits into this data strategy.  If you want to buy PV gear, generators, rectifiers, or cooling equipment, you can purchase the equipment from whatever vendor you believe gives you the best value, as you aren't tied to any particular equipment's management interface.   The data strategy is fundamentally built on an ability to work with any equipment. 

You don't cede control of key elements of managing your network to vendors who benefit from the siloing of your data as a form of vendor lock-in. 

That is the practical difference between a data strategy and another proprietary monitoring purchase.  These benefits will compound in favor of the operators who make it early, and it is somewhat ironic that pursuing this data strategy path will cost less than continuing to buy point solutions that are never going to deliver what you're hoping for due to the data silo problems mentioned above.   It is a much better solution at a lower cost.