When data moves in, operational expenditure moves up

A fully data-driven Telco has been the dream of most executives since the beginning of the 21st century. With a greater degree of self-healing, proactive, predictive, preventive, and self-diagnosing mechanisms, data has brought incredible value to the businesses but in many cases the cost of exploiting data has exploded (for build as well as for run) and risk to continue to escalate with new technologies (AI/ML, Generative AI).

Over the past few years our work with Telcos and their suppliers in the Nordics suggests that a more structured data product approach can significantly reduce the trend of escalating operational expenditure used for financing data driven use cases.

With 5G, IoT, and the introduction of AI/ML, service providers face challenges in transforming vital parts of their operations to achieve economic performance and positive return on investment. While automating a large part of the OSS stack using real-time data, the cost of operating the necessary software and systems is escalating, in some cases eroding a significant portion of the financial benefits brought by the automation.

The key to successfully navigating these complex challenges lies in componentized data software solutions that break the systems into components, which can be independently managed, governed and re-used. In the research realm, the acronym FAIR is often used, which is stands for Findable, Accessible, Interoperable, and Re-usable.

We see that it is possible to create a consolidated internal marketplace of FAIR data products and data assets that work seamlessly together, across the network lifecycle, from planning through deployment to continuous optimization.

In a comparative study we conducted for a Telco client, we found possible savings of up to 70% in a mature setup with 1200 data products overall (with many more data components supporting these products). This is based on a phased adoption of the FAIR data product strategy, encompassing both business and network data, which gradually expands while still using a consistently stable set of data product teams.

Savings are realized in the development phase through the reuse of components and methods, in routine and recurring maintenance. The assembly of interoperable components speeds up processes, reducing time-to-market compared to building software from the ground up. The single version of the truth, which the components represent, leads to lower complexity and further reduction in time spent checking if the input is accurate and of the right quality. It also reinforces the consistency of data analyses across the different organizations within the company.

While still automating operational processes and enabling advanced functions, i.e. running AI-enabled diagnostics and optimizers, we maintain strict cost control. Leveraging insights from previously published data components, we can expedite the rollout of new services and break the relationship between cost and data volume.

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