Your AI Journey: Finding the Use Cases That Matter
Improving operational efficiency with AI is non-negotiable in telecommunications. Discover how targeted AI use cases can deliver immediate value.
Blog
16 December 2025
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How can your Customer Experience Index be used in Digital Twins to enhance Network Assurance and predict the impact of change?
Blog
16 December, 2025
Author
Building an effective CEI depends on acquiring, ingesting and processing data from a wide range of sources. In turn, this demands an effective data fabric, enabled by DataOps processing. Once in place, this also unlocks the possibility of using Digital Twins to model the impact of changes on CEI.
CEI and CX Are Crucial Benchmarks to Measure
Customer Experience is, of course, fundamental. It’s table stakes for Mobile Network Operators (MNOs) that need to retain and attract customers – and an ever-moving target, as new services are launched and as customer expectations grow.
Measuring, maintaining and enhancing it, then, is a critical activity – and one that is fundamentally dynamic in nature. Not only does the network performance change during operations, each change and service update can have repercussions that impact the experience enjoyed by customers.
We try to capture the experiences of customers through metrics like a Customer Experience Index (sometimes known as CX), which seeks to summarize the experience of an individual customer. Advances in AI help us to understand these and the behaviors that can result as this metric moves up or down a scale.
Effective Data Acquisition, from Multiple Sources, Is Critical to Your CEI
However, while these offer much promise, we first have to capture the data needed to calculate CEI scores. The problem here is that CEI depends on a range of information from different sources. It’s a value that is inferred by taking different data points and calculating the overall score. So, if data is lacking, then a less complete picture can be captured – reducing the accuracy of the resulting CEI.
Our industry recognizes this. For example, in Recommendation ITU-T M.3389 (03/2025), the ITU notes that customer experience management (and also the creation of CEIs) depends on “data acquisition” – the collection of:
“relevant data that can reflect customer experience from the diverse telecom service data. This data should serve as the object of monitoring, the input of customer experience evaluation and the basis of anomaly analysis.”
And, should cover inputs such as:
- Network performance
- Service quality
- Configuration and service operation data
From different sources, like:
- The network
- Network management systems
- Customer Relationship Management systems
- Work order systems
- Customer service systems
- And more
This data is available in your networks, but it is presented in different formats – so the first challenge to building a reliable CEI is to secure the ability to collect, process and normalize these different data sources, so that they can be analyzed — at scale and at the requisite speed. The problem for many operators is that the data is currently available only through separate silos and discrete platforms.
Build the Necessary Data Fabric with DataOps
So, the first challenge is to build a data fabric that can ingest the sources of data that contribute to building your CEI and to consolidate different data types so they can be efficiently analyzed through a single pane of glass.
DataOps provides the answer to this challenge, allowing us to capture data from different sources and platforms and to create a harmonized way to ingest data and to make it available for analytics and other processes that leverage the unified data set.
So far, so good. This solid data foundation is essential – but the CEI metrics captured reflect the current state of the network. What about changes to the network that could negatively (or positively) impact CEI scores?
Leverage This to Enable Digital Twins for Experimentation
Well, one area that is attracting considerable attention across multiple sectors is that of Digital Twins. The Economist has noted how Digital Twins are helping diverse sectors such as manufacturing, aerospace and Formula 1, to name a few examples:
“Rolls-Royce, along with its two big American rivals, General Electric and Pratt & Whitney, which also competed for the contract, were among the first to start using digital twins to monitor the performance of their engines.”
The Economist: Digital twins are speeding up manufacturing
And:
“…digital twins will redefine what it means to run a company. Instead of co-ordinating disparate islands of automation, as is the case today, bosses will manage a constantly churning “flywheel” fuelled by data.
With access to information from all over the company’s operations, as well as from its customers and suppliers, a corporate twin will not just help managers make better plans. It will also implement them, learn from the outcomes and optimise itself to achieve certain corporate objectives - over and over again…”
The Economist: Digital twins are making companies more efficient
Put together, that sounds an awful lot like the sort of network assurance we want to deliver: digital twins can help provide models to enable us to discover new insights, predict issues and pre-emptively drive corrective actions. All of this requires data, as the articles note.
Now, if operators want to leverage innovations like these to assure their operations, then they need a way of obtaining the necessary data in a way that can be used in Digital Twins and any other future application that can support our transformation and automation goals.
DataOps enables us to acquire and ingest the necessary information, providing the data-driven fabric we need. But building complete Digital Twins will take time. As Arthur D Little notes in a recent paper, “significant technical, data-related, and economic barriers still need to be overcome before widespread adoption [of Digital Twins] becomes possible .”
However, even if we do not yet have all-encompassing Digital Twins that span the entire network and operations, we can also learn from the experience of Rolls Royce and others – as The Economist noted, they are using Digital Twins to elements of their overall systems, in this case the engines.
This is the third article in our series about Network Assurance. Read the previous posts:
How Can Data Enhance Coverage Optimization and Extension for Mobile Network Operators?
Location and User Validation Through Enriched Data Acquisition and Analytics
The fourth and final article will pull back the curtain on the ultimate prize: monetization.
