Breaking Telco Data Silos In Telecoms

Why Unified Data Layers Are The Foundation for Future-ready Telcos

In 2020, siloed data resulted in USD 3.1 trillion in annual losses for US businesses, according to a study by IBM.¹ This problem isn’t limited to the US, as many telcos around the world today sit on vast amounts of data, but much of it is fragmented and siloed within different business functions or vendors. 

Data could be locked within BSS, OSS, marketing, customer support, or network analytics, preventing data analysts from getting a full picture of the telco’s situation. These data silos result in slow decision-making, poor customer visibility, limited scalability, and AI and machine learning models that can’t deliver good returns on investment.

This brief report will cover the costs that data silos have on businesses and telcos before discussing how unified data layers can help to break these silos down.

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Data Silos Cost Billions While Slowing Down Employees

In telecoms, data silos can result in inefficiencies that translate into slower product launches, higher churn, and missed cross-sell opportunities. 80 percent of organizations in general still operate with data silos, and poor data quality continues costing companies millions.² In an industry competing on customer experience, speed, and cost efficiency, fragmented data has become a structural disadvantage.

Customer Experience

Imagine receiving a warning about an upcoming telco service outage in your area before receiving upsell promotional messages from that same telco. That’s what one telco operator found its customers were facing, where its various units were sending customers up to five messages a week, with some customers receiving that exact outage information and cross-sell promotions within the same hour.³

Data silos result in a poor understanding of customers. Different teams can’t see the same subscriber across billing, marketing, and support tickets, resulting in embarrassing situations like the one above. Meanwhile, it becomes harder for telcos to find at-risk customers who are thinking of churning or identify high-value customers they can upsell to. That means lower customer lifetime value and lost upselling opportunities.

Untrustworthy Data and Wasted Resources

When data is in silos, it is hard to know if all the relevant information has been included, which leads to untrustworthy data. There could be reporting inaccuracies, and telcos could be making decisions based on incomplete data.¹ 

Meanwhile, data silos also cost employees a lot of time, as workers can lose up to 12 hours a week searching for information trapped in silos. Part of why this happens is because different departments and different companies store data using their own formats without making it easy to use or access for users outside their domain.

This is also known as ‘accidental challenges,’ where inefficiencies are created by people attempting to make use of the data. In this case, data analysts from different departments make copies of other departments’ data and transform them into formats they can use in order to develop their reports.⁴

On top of inefficient manpower usage, it also leads to many duplicates of similar data across multiple functions across the telco, leading to wasted server space and slower processes. Poor quality data led to at least USD 12.9 billion in extra costs, according to Gartner in 2020.²

Slowing Down AI Adoption and Slower Decision Making

As a result of messy data and data silos, telcos don't have access to real-time information, meaning that they also lose out on making proactive strategic decisions. All these manual processes needed to find siloed data leads to slower reports that rely on data teams to manage, while messy custom data pipelines need to be built by tech teams that rely on custom metrics, which slows down the data collection process; hence no instant diagnostics. 

If the data is hard to use by human data analysts and existing machine learning models, this data won’t be useful to AI models either. This results in the infamous ‘garbage in, garbage out’ problem, where models that are trained on incomplete or inconsistent data deliver useless predictions and hallucinations, eliminating any return on AI investment.

Governance Risk

Duplicated and siloed data also makes compliance and audits more complex, such as GDPR and PDPA audits. Fragmented datasets and a lack of visibility into information mean that telco teams don’t know where data is stored. When teams don’t know where data is, it’s hard to safeguard it.

The Changing Expectations of Data 

For older telcos, data silos are a result of changing expectations of data. Telcos previously were focused on ensuring that service lines were up, and customer data storage was a secondary concern. 

Eventually, in the digital age, the business world’s expectations of data changed. Data is needed to support real-time decision-making, which means that the same data is usable by all departments as soon as it’s available, with minimal processes in between that could slow down when the data becomes usable. 

Telcos now also need to gather and use data beyond individual functions and even collect non-telco data as well, such as user locations and their web-viewing habits, to power initiatives like marketing campaigns. Meanwhile, the number of vendors they need to coordinate with continues to balloon, such as software partners, cloud providers, app partners, AI vendors, and more.

Unfortunately, telcos who had been prioritizing service uptime and keeping legacy systems around as they were proven to work now struggle with data silos. 

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Root Causes of Data Silos

Legacy BSS/OSS Stacks That Were Not Meant for Integration

Legacy telcos prioritized service uptime over data management, relying on stable monolithic structures that were less prone to failure. As long as the software worked, they would keep it or ‘if it isn’t broken, don’t fix it.’

This mindset led to decades of patchwork updates with vendor-specific software and custom integrations and interfaces to add new functionality to the existing system. Every new custom pipeline would slow down the data flow, and the semantics, or ‘language,’ of the data meant that it would need to be cleaned up or reformatted to be usable by other business units.

Fragmented Data Ownership Within the Telco

In the older days, it was sufficient for different departments to gather and use data that only their function would need without worrying about how other teams might want to use it. 

According to McKinsey, 80 percent of organizations responding to their survey reported that some of their divisions operate in silos, each with their own data management requirements, practices, source systems, and consumption behaviors.⁵

That means that sales and marketing could maintain client data in their own customer relationship management (CRM system), while the customer support team uses their own software like Zendesk to manage tickets. Mergers and acquisitions would add to that, as the newer telco would have its own data silos and data management standards.

Data is isolated by function and the different architectures they used, which would require custom pipelines to make usable for anyone else. With no single source of truth or governance layer, there is now a lot of duplicated work and duplicated data. 

Fragmented Vendor Ecosystem

Meanwhile, data was increasingly kept with third-party vendors and in different systems such as multi-cloud and on-premises hybrid approaches. These arrangements would complicate governance, access, and integration with many platforms not designed for cross-environment data management as a first principle.⁴ Vendors would also protect their environments to retain clients, which can slow down integrations further.

Technical Debt from Ad-Hoc Data Management

On top of that, an ad hoc approach to building data pipelines between departments makes data transfers slow and fragile. These tactical patches would accumulate and eventually add to the ‘spaghetti’ of technical debt. 

Each new pipeline is a separate process to extract, transform, and load (ETL) data, so new pipelines would slow the system down further, creating massive technical debt that would be difficult to unravel short of changing the software completely.

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The Strategic Solution - Using Unified Data Layers

Data silo issues can be solved in a number of ways, including moving to a system that uses a unified data layer. 

A unified data layer gathers all the company’s data into a single, central nervous system with consistent data governance and data collection standards. Governance, security, and access rules are also managed centrally by one data team or platform owner. 

Having a single governed access layer for all business departments means that usable data can be pulled from the unified data layer with less duplication and additional transformation. Different departments can pull data from the same place in a consistent and standardized way, removing the need for redundant custom pipelines and saving man-hours and speeding up the system. 

This leads to faster launch cycles and sets the stage for real-time insights which power better AI and predictive analytics. Faster launch cycles mean faster time-to-market for new products and innovations powered by a 360° customer view while also enabling proactive churn prevention. One organization achieved a 30 percent reduction in costs after upgrading its data management processes and enabling advanced analytics.⁶

In short, a unified data layer simplifies access and integration, ensures consistency and data quality and also prepares your telcos data to be AI-ready and cloud-compatible.

Leading telcos are already moving in this direction:

But building a new unified data layer from scratch won’t eliminate existing legacy problems. At times like this, working with a strategic partner who can provide an AI-enabled full-stack solution built with a unified data layer can save large amounts of time and money.

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Work With The Right Partners and Leapfrog Ahead Of The Competition

There are currently no clear winners in the telco digital transformation and AI adoption race yet, but an AI maturity gap between leaders and laggards is emerging. Working with the right partners who can provide you a telco-specific, full-stack solution with a unified data layer is a start. Working with one who has firsthand experience running its own telco alongside a proven data migration service is better.

Built by Operators, for Operators

Circles’ AI stack is designed by operators for operators, ensuring relevance, speed, and scalability. Validated through deployments with global leaders such as Telkomsel, AT&T, and KDDI, the platform leverages multi-market learnings to deliver proven results. 

At its core lies unified data intelligence, providing a single source of truth across acquisition, operations, and engagement. Additionally, Circles offers AI-as-a-Service, supported by digital advisory support services (DASS) consultants, giving telcos on-demand expertise and accelerating transformation journeys.

Data silos are sapping telcos’ competitiveness, and dealing with them is a strategic imperative. A unified data layer is the foundation for becoming an AI-enabled anticipatory techco, meaning a telco that doesn’t just react to customer needs but can predict and fulfill them ahead of the competition.

Circles has the experience of running its own MVNO brand while building its telco SaaS solutions from its own experience. Having won multiple awards and trusted by major telcos in the Americas and Asia, your telco will benefit from tried and tested software, proven data migration, and a unified data platform fit for today’s demanding customer expectations.

To see a demo of our platform or find out more, contact us below!