Data quality in telecom and utility networks can make or break operational efficiency and profitability. Without a foundation of network data accuracy, operators won’t be able to deploy reliable AI-driven automation or embark on their ambitious vision for a fully autonomous network.
This article sheds light on the mounting risks poor field data quality poses for telecom and utility networks as operators race to manage, grow and upgrade their networks. Beyond discussing the immediate benefits of improving data quality data for greater network visibility, this post will also highlight why leaders need to prioritize systems that enhance network data accuracy today as a first step towards the automated operations that will enable them to pull ahead tomorrow.
Telecom and utility operators typically rely heavily on photos and forms to document their physical network. Once collected, that data often gets trapped in disconnected software systems, shared drives and spreadsheets. It’s not centralized, structured or searchable, which means it can’t be readily brought into monitoring and analysis software or accessed by field and back office teams who need it to make decisions.
Many operators have already made significant investments in apps and system of record (SOR) solutions to bring field documentation online and into the cloud. The reality is that adopting these tools introduced a new issue: data silos.
A complete picture of the network is often fragmented across many software systems, which can include geographic information systems (GIS), AutoCAD, asset and inventory management software or enterprise resource planning (ERP) tools. And despite these digital solutions, teams also continue to load data into spreadsheets and share documents in cloud-based drives and via email.
When data is scattered across disconnected systems, you can’t clearly link it to specific assets, locations, workflows, contractors or individuals. Without unified data, teams simply can’t access insights. Field workers have to be on site to verify key details about an asset: its condition, exact location, configuration or the last time it was maintenanced. At best, technicians have to commit these details to memory or spend extra time searching for the right information before starting work, stalling the job. At worst, this lack of visibility puts workers in danger[MK2.1]. Crews may unknowingly work on energized equipment, excavate near unrecorded underground infrastructure, or follow outdated network configurations that no longer reflect field reality.
Data silos prevent operators from reliably tracking network and asset performance over time, which is critical for adequate utility network and telecom quality control processes (QC), quality assurance (QA), outage detection and conducting cost-saving preventative maintenance.
Paper-based systems and inaccessible digital records also promote a culture where knowledge resides with individuals rather than the organization. When technicians can’t rely on their systems, they lean on years of experience or shared knowledge of the assets they’re working on. As older field workers reach retirement en masse, operators will lose vital information workers have been using in the field to keep the network up and running.
Manual field data validation and QA/QC processes simply can’t keep up with the rapid growth and increasing network complexity that operators are optimizing for today, causing utility network and telecom data quality issues that have a domino effect on network visibility.
The reality is that operators deal with two layers of quality control: they need to check whether field work is done correctly, but they also need to check whether that work has been documented accurately. This effectively doubles the work for human QA/QC teams. If a technician completes a job impeccably but takes a blurry, poorly-lit photo of the work or forgets to fill out a form field, the back office still can’t tell if the job was done right or not.
In most cases, teams don’t have time or resources to get someone back to the site to rectify low-quality documentation. That useless data sits in the SOR until the next back- office team member or technician has to deal with it when the asset is serviced again.
These twofold manual field data validation and quality control falls apart as networks become increasingly complex. Traditional human QA/QC teams can only check between 10-15% of the work done. More specifically, they can only thoroughly check jobs that have been properly documented. This means up to 90% of jobs aren’t assessed or validated, leaving operators in the dark about errors that can lead to outages, premature degradation and safety issues. These gaps create a critical lack of visibility that only snowballs at scale.
Utility and telecom network data quality issues aren’t just an irritating inconvenience for people in the organization, whether they are field workers or back office teams , it creates risk over the entire network lifecycle, from build to connection and throughout ongoing operations.
Build
Poor data quality in telecom and utility networks slows construction and project delivery. Manual redlining and outdated or inaccurate as-builts create barriers to communication. Crews arrive on site only to discover the plans they have no longer match reality and they have to spend (expensive) time figuring out how things were completed. This stretches project timelines, sometimes significantly. Swisscom, for example, experienced a six-month delay in its fiber rollout due in part to a manual quality assurance process. These delays postpone revenue realization and can negatively impact relationships with governments and municipalities commissioning the projects, as well as customers waiting to get connected.
When it’s time to validate the work that’s been done to close the job and pay contractors, operators can’t be sure that the documents they’ve received reflect what was done. On the contractor’s side, this stalls billing and can lead to disputes.
Connect
Long project completion timelines and time-consuming manual field data validation processes ultimately prevent customers from receiving their services. Adequate documentation becomes a customer-facing issue when crews are working on last-mile projects and installing assets in homes and businesses. Technicians need to be in and out quickly and provide excellent customer service while maintaining high safety standards. They need accurate information about their location and the network as well as clear instructions to get the job done right the first time.
The faster operators can activate services, the faster they can begin billing the customer. The better their customer service and the more reliable the services are out of the gate, the less likely new customers are to churn for competitors.
Operate
Field data quality directly impacts network quality. When completed work, materials used and asset condition are not documented and tracked, network performance cannot be effectively monitored over time. Static records quickly fall out of alignment with the physical network. Manual field data validation creates blind spots that can easily hide low-quality work and mistakes that impact the entire system.
When a truckroll can cost upwards of $200 USD, it pays to ensure that every job is done correctly the first time and that the work is accurately documented. If technicians can access the information they need in real time, they can do their work more effectively. Trustworthy documentation also offers boots-on-the-ground visibility to the back office so that they can make more informed decisions and perform thorough asset audits, when necessary, without sending anyone into the field.
Improving data collection and quality control directly contributes to exponential cost savings that can then be allocated to further expansion and optimization.
Trustworthy, structured field data is the prerequisite for automation. Without a unified source of clean field data, operators can’t initiate AI and ML projects to streamline operations, improve decision-making and drive growth and profitability.
Implementing expensive AI and automation initiatives without airtight data collection and validation procedures is a recipe for failure. AI-driven systems amplify the consequences of bad data. Automation errors lead to delayed (or uninformed) decision-making, costly or dangerous mistakes in the field and even more rework. Errors also cause users to lose trust in the system, undermining hard-won buy-in and making it more difficult to get teams to reengage with their tools after the fact.
The downstream effects of bad data derail the vision for intelligent, automation-optimized operations and autonomous networks and put operators back at square one: dealing with clunky processes that yield an incomplete picture of the network.
Weak field data capture and documentation processes create vulnerabilities in telecom and utility network operations. Accurately documenting field work as it’s done allows operators to leverage trustworthy network insights to make more effective decisions, move faster and proactively adapt to climate challenges, workforce gaps and increasing demand.
Operators that want to prepare for the future of autonomous networks to realize greater efficiency and cost savings need to improve their field data systems today by eliminating data silos and moving away from manual field data validation. Leaders must prioritize data as an asset and focus on increasing not only the quantity, but the quality of the data they collect from the field or risk being left behind by competitors who are already implementing next-generation automation.
As the telecom and utility industry moves toward intelligent and even autonomous operations, keeping network records continuously aligned with field reality is becoming a critical requirement. To help address this challenge, IQGeo recently announced an industry-first capability that automatically detects discrepancies between telecom network records and completed field work, enabling operators to identify and resolve data quality issues before they impact planning, operations or future automation initiatives.