Director of Information Technology at Turk Telekom

The telecommunications industry is undergoing a transformative shift driven by the convergence of 5G, IoT, AI and cloud Technologies. This convergence is creating a more connected and data-driven world, presenting both opportunities and challenges for telecommunications providers. It opens up new avenues for innovation and service delivery. On the other hand, it introduces complexities in managing and optimizing networks, ensuring data security, and delivering seamless service across multiple touchpoints.

5G unlocks a new era of data-driven innovation. With faster speeds and lower latency, businesses can collect and analyze data in real time, leading to improved decision-making, optimized operations, and the development of entirely new services. As telco technology leaders, we are uniquely positioned to champion this transformation and ensure our organizations become AI-ready.

AI-Ready Data

5G is a transformative technology that will fundamentally change how we interact with data. It's a future where data flows freely.

The 3Ps - Predictive, Proactive, and Preventive operational approaches - will be critical to navigating the challenges and opportunities presented by 5G. By adopting these strategies, organizations can ensure a secure, efficient and optimized 5G ecosystem.

The 5Vs of Big Data (Volume, Velocity, Variety, Veracity, and Value) have become even more critical to manage with the arrival of 5G.

The data makes up 70 percent of the AI pyramid, and the quality and availability of data have a direct impact on the success of AI projects. To ensure the success of AI initiatives, it's essential to make data AI-ready, which involves two critical aspects: data governance and data management.

Understanding the context of data, including how, when, and where it was captured, is vital for making data-driven decisions. We must ensure that data is properly labeled, categorized, and contextualized to derive meaningful insights and drive accurate AI models. ​

  ​Getting one or two AI models into production is very different from operationalizing hundreds of them. 

Investing in data management solutions such as data lake house/data fabric enabling efficient data collection, storage and organization. This ensures the data used in AI models is accurate, complete, and readily accessible to AI teams. The full potential of AI technology can be unlocked and drive transformative outcomes across various domains, including customer experience, network operations and business efficiency.

Scaling AI

The promise of AI is undeniable, but true transformation requires effective scaling of these solutions. Getting one or two AI models into production is very different from operationalizing hundreds of them. And as AI scales, problems can scale, too. To overcome the complexities of managing AI deployments, we adopted a new discipline, ML Ops. ML Ops establishes best practices and tools to facilitate the safe and efficient development and operationalization of AI models.

The future of the telecom industry is undoubtedly AI-driven, by embracing these strategies– building a skilled AI workforce, ensuring AI-ready data, and effectively scaling solutions we, as technology leaders, can empower our organizations to become AI-ready.

Investment Needs

The arrival of 5G promises a revolution in connectivity, enabling advancements like self-driving cars, smart cities, and precision agriculture. These applications will rely on millions of sensors generating data from roads, fields, and potentially even livestock. Managing this data surge will require much more IT capabilities.

Beyond storage capacity, 5G will drive demand for AI-powered analytics (utilizing GPU-equipped machines) and time-series databases for real-time data processing. Open-source solutions like Kafka and Docker will gain traction for virtualization and cloud applications. The need for faster response times will necessitate a shift towards distributed data centers at the network edge rather than centralized facilities.

Network virtualization using software-defined structures will become crucial for efficient resource allocation. Additionally, AI will play a critical role in network optimization, not just in resource management but also in data management. Maybe this time, we create a hypothetical system, Artificial Intestine-AI2, inspired by the human digestive system, a second brain that intelligently filters and discards irrelevant data, mimicking the way our bodies process information.

Bridging the Talent Gap

The digital age demands a surge in skills related to AI, data analytics, and automation. The surveys reveal that while AI and machine learning skills are highly sought after, they are also among the most challenging to find. Turkey boasts a young population with a growing pool of AI enthusiasts who can design, implement and manage complex AI solutions.

New Opportunities

With slicing brought by 5G or new service providers with private 5G services, new service providers will emerge. Looking at the work done by the world's largest companies, network providers are transforming into digital service providers and, especially cloud services are becoming more popular.

In conclusion, with the increasing demand for applications requiring high speed and low latency and the growing data, there will be many opportunities, but that may be the subject of another article.