Laying the Foundation for AI with High-Quality Network Data

This Industry Viewpoint was authored by David Cottingham, CTO at IQGeo

As the fiber broadband industry evolves, operators face increasing pressure to adopt emerging technologies to stay competitive. McKinsey’s report, “The AI-native telco: Radical transformation to thrive in turbulent times,” underscores the potential of AI and Machine Learning (ML) to drive growth and disruption within the telecom sector. AI promises substantial benefits, including infrastructure that self-heals, touchless customer service, hyper-personalization, and automated marketing. However, its successful implementation hinges on the quality of underlying fiber network data.

The Critical Role of Network Data Quality

At the heart of any telecom business lies the physical network infrastructure. Accurate data about network asset locations, physical connections (such as splice boxes and fiber), and network configurations is essential for operational efficiency and future technological advancements. Inaccurate or incomplete network data creates poor decision-making, operational inefficiencies, and increased costs.

For AI and ML to deliver its promised benefits, it requires a robust foundation built on high-quality network asset data. Without this, the adage of “garbage in, garbage out” will apply. This means that telecom operators would do well to prioritize strategies and technology that is focused on creating a complete and accurate network model, which involves:

1. Comprehensive Asset Management: Precisely documenting where assets are located geospatially and their connectivity architecture.
2. Maintaining Network Documentation: Documenting the location and connectivity model of a network is not a one-time operation. Significant investment is needed to keep up to date.
3. Integrated Systems: Moving away from fragmented data management systems to an integrated solution designed specifically to support the lifecycle of the fiber network.

Prioritizing Network Optimization

At the core of every telecom organization is its physical network. Operators must efficiently plan, design, build, operate, and monetize their networks to accelerate ROI. Early in their modernization journeys, this might mean replacing spreadsheets and CAD drawings with fiber network documentation software. However, many larger established broadband operators are still using traditional GIS systems and a collection of siloed applications, which put them at risk of being outpaced by smaller more agile operations that have deployed integrated lifecycle management solutions optimized for fiber networks.

Investing in an optimized fiber network model, such as a digital twin built specifically for the network, helps avoid creating a complicated and disconnected software infrastructure that is difficult to support, upgrade, and evolve. A fully integrated fiber network management strategy enables operators to lay a solid foundation, so when they choose to invest in emerging AI and ML technologies, they are in a much stronger position to benefit from their full potential.

Actionable Recommendations

To prioritize data quality and prepare for AI and ML, telecom operators should consider focusing on the following steps:

1. Conduct a Data Quality Audit: Assess the current state of network data to identify gaps and inaccuracies.
2. Invest in Integrated Documentation Systems: Transition from disparate data management tools to an integrated network management strategy built for fiber operations.
3. Implement Rigorous Data Management Practices: Establish protocols workflows for continuous data updating and accuracy verification.
4. Select Reliable Technology Partners: Work with software vendors who specialize in solutions built to encourage the capture and management of high-quality network data.
5. Focus on Incremental Improvements: Enhance network data quality through systematic, incremental upgrades and extensions rather than large-scale overhauls that are expensive and risky.

Strategic Vendor Partnerships

Working with a primary technology vendor who understands the operator’s fiber network model and lifecycle requirements can be more efficient than engaging with multiple vendors. Establishing strong relationships with very few key vendors provides a stronger foundation for the long-term lifecycle of their fiber network, ensuring critical software components are integrated tightly and the stakeholders are aligned strategically. This approach simplifies management, offers great ROI, and sets the business up for future success.

By prioritizing network data quality, telecom operators can ensure that the lifecycles of their network are prepared for AI, ML and other advanced technologies. A strong network data foundation will not only improve current operations but also enable operators to harness AI’s full potential when the time is right. Strategic investments in network data quality today pave the way for sustainable growth and future technological advancements.

About the Author

David has held senior Engineering and Product Management roles on product lines worth tens of millions of dollars, most recently as CPTO at an AIM-listed businesses, and previously at Citrix Systems for over a decade. He’s led large, multidisciplinary engineering teams distributed around the world, and worked on operating systems, SaaS, IoT hardware, developer ecosystems, and mobile applications. He holds a Ph.D. in Computer Science from the University of Cambridge.

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Categories: Artificial Intelligence · Industry Viewpoint · Software