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Can Telecoms Inventory Data Provide Fuel for Big Data Analysis Programs?

23 June 2022
Mike Dorland

Trusted by:

Telecom Egypt
BC Hydro


National Grid
Open Fiber
TPX Communications
Ella Link
Red Iris
Surf Net

What does the future for telecoms operators look like? We can’t be certain, but whatever the correct answer turns out to be, it will likely be found in the analysis of the sea of big data being accumulated across the industry. Leveraged correctly, big data analysis and the elements that make up the telecoms inventory are set to become a game-changing combination. Telecoms inventory data should be at the heart of any such analysis program.

Shaping tomorrow’s world

Sure, we all know the standard cliche about big data being the “crude oil” of the future economy. If we’re honest, we’ve probably had enough of hearing it but still, that’s just what big data is. If we mine – and master how we handle – big data now, it teaches us what we should most profitably be doing next. And that’s an outcome we all want.

We can extrapolate from this to suggest that, if the future is going to live up to our expectations (and every innovation, for example 5G, happens because it promises to make our lives better), then telcos had better get on top of their big data now. 

At heart, the telco industry’s plan for the future is to more effectively identify and understand the needs of its customers, then meet them by delivering what the customer wants: better and more targeted services, better quality of service, better overall experience on the network. All of this can and will be driven by big data. Of course, we need to understand where this data will come from – and there’s an existing source that must not be overlooked: the network inventory.

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Master big data; flourish commercially. That’s how it should work

Already, early data-driven Use Cases are proving this assertion. One important example sits at the junction of network inventory and big data analytics: Optimizing Network Capacity. 

Ensuring that the network performs optimally is a basic requirement for both the immediate and long-term success of network operator. Achieving it requires being able to answer questions like where does excess capacity exist and where should bandwidth be re-routed to improve performance? The answers to those and other related questions, of course, lie buried in the big data gathered by network elements from which the necessary information first has to be extracted and then analyzed for beneficial use. Guess what? A lot of this ends up (or should, if you have the right solution) in the inventory.

If this can be done – by tapping into the inventory data and combining it with other sources, for example – network usage analytics becomes the means via which operators can identify areas with excess capacity and reroute bandwidth accordingly, thereby improving performance, reducing waste, increasing customer satisfaction, and more. 

Big data analytics is thus central to not only immediate problem solving but also planning for infrastructure investments and designing new services that meet customer demand. Data-driven network inventory provides a pool of insights to give operators a fast-track to upgrading customer loyalty and reducing revenue loss to competitors. 

And while it may be challenging to build the complex models of relationships between network services and customers required to achieve these and other beneficial outcomes as well as managing the analysis of the data drawn from a broad variety of network sources (call detail records being just the start), the importance of the pairing of Big Data to network optimization is unavoidable.

vc4 big data programs

Analytics delivers benefits across the board

While the focus in the above example is on network inventory, it’s worth noting that for the operator implementing big data analytics the result means being in position access to new use cases is more than one domain.  

Other examples of areas where analytics are impactful include customer churn where data related to service quality, convenience, and other factors, can help predict and then improve overall customer satisfaction and service innovation, where data can yield insights that help telcos design new products and features. As leading analyst firm Gartner once noted, “information is the oil of the 21st century, and analytics is the combustion engine.”

Big data strategy: If only getting it right was simple

The use cases and potential benefits cited above are, however, only the good news part of the story. As with any form of mining, there are also dangers present and to accrue the benefits, operators need to carefully consider the challenges involved with collecting, processing, refining and using data, particularly at scale. 

So, while yes, operators have vast and accessible data repositories at their fingertips that they can use data to detect patterns with the potential to offer new services or quickly detect faults in the network, concerns ranging from privacy to the deployment of new technologies such as AI which have the potential to negatively impact people’s lives if used incorrectly also have to be taken into account. 

That leads to the important question of Data Governance and the need for a standard industry model (a task the TeleManagement Forum is presently tackling). It’s not a question operators can afford to ignore.

The need for governance is, in many ways, recognition of the fact that there’s potentially a fly in the ointment, and that the benefits of data can’t be accrued successfully if the potential threats it poses aren’t addressed first.

Mitigating the perceived threats

The realities above explain why security and privacy of all citizen data has become a political issue around the globe, one prevalent example being the advent of the GDPR regulations in Europe. Handling large data sets raises numerous questions, particularly as not all data has the same value. The most fundamental of all remains, who owns the data? Data bias is also a significant issue for telcos who often have access to the largest datasets available anywhere on the planet and are at the center of peoples “connected” lives.

In this area, operators must find agreed ways to qualify, manage, organize and process data to enable its correct and safe use so that it can be turned into the aforementioned insights and used for the customer’s benefit. That means managing regulatory and legal constraints, as well as adopting any emerging data governance frameworks.

Get the structure right, reap the benefits

Governance challenges or not, as we’ve noted the crude oil of both efficient service delivery and network performance today lies in using the data that’s already available. And telco networks and their attendant supporting systems and components generate a lot of it, from a variety of interfaces and elements within the network that reflect its operational status.  

All these data feeds need to be collected and aggregated, ultimately to build the single and impactful picture required to make decisions that would benefit both the telco itself and its customers. Once again, network inventory is a key source of data for these kinds of applications – a complete record of all network assets (of any kind), their relationships and their locations that can fuel insights.

The inventory isn’t a static model, however. It’s a dynamic model that must be constantly updated – as new connections are made, as services are activated (or turned off) and so on. All of these activities provide a stream of data outputs that can inform other processes. To return to our capacity planning example, inventory data can show us if, say, a leased line is under-utilized or is approaching capacity – in either case, action needs to be taken from an operational point of view, but aggregating all of this data and analyzing it can also highlight trends for future planning.

So, the reality of his situation suggests that there’s an emerging new “dynamic duo” in telecoms; Network Inventory Management platforms coupled with Big Data Analytics. Operating in tandem, they have the potential to re-shape the landscape. A leading example of the former is VC4-IMS, which collects data from all of the network assets, normalizes, and then reconciles it. 

This data is used for operational purposes and, as a result, VC4-IMS delivers a critical, panoramic view all physical and logical resources in the network giving the operator a clear, unified understanding of live network assets, and their utilization and configuration at any given time. At the same time, this data can also be used by other systems via API and other data export feeds – a combined source to discover insights that can drive operational optimization and enhanced efficiency – and to identify new opportunities and areas that need investment.

With access to this secure source of data, Big Data Analytics can work its magic. Network Inventory Management and Big Data analytics are, combined, a key foundation of future commercial success.  Please get in touch If you’d like to find out more.