How CSPs can enable accuracy in network capacity management
The telecom industry is in the midst of an exciting yet turbulent period. 5G is on the horizon and opens new doors of opportunity for communication service providers (CSPs). Investing in 5G is top-of-mind for almost every CSP, with an estimated USD 900 billion forecasted as the total spending on the new technology in the next five years, of which 80% will be towards 5G networks.
The burden of Capex is highest in the telecom industry when compared to other sectors, standing at a grim 12-18% depending on the CSP. 5G and activities around 5G network planning will only add to the Capex strain faced by this industry. This will add even more pressure on CSPs to ensure high returns on their investments, with the network being at the center.
CSPs are also looking to modernize their network to scale to meet rising customer demand and expectations and the associated data volume growth. This endeavor has led to a rise in network complexity stemming from network-wide densification and virtualization. As the 4G network expands further and the 5G network evolves, a CSP’s network will become more prevalent, dynamic, and complex. All this will require more effort. It will become virtually impossible for a human to comprehend and find an optimized solution for day-to-day troubleshooting in such a complex network.
Considering the high Capex intensity and network complexity, it is evident that CSPs have fallen short of achieving this objective, i.e., witnessing optimal returns on their network investments from their network capacity planning efforts. Considering the amount of data CSPs can leverage, generating the right insights to create more accurate and productive network investment plans can certainly help. Of course, this would make data science and advanced analytics, i.e., artificial intelligence (AI), machine learning (ML), and deep learning (DL), imperative for telecom CSPs to succeed. But will the application of AI/ML/DL models suffice?
Why accuracy matters
In an industry struggling to overcome tremendously high Capex and Opex challenges, CSPs need to make the right decisions at the right time. However, globally, a significant amount of Capex and Opex is wasted due to inaccuracies and inefficiencies in the network analytics process, which can no longer be afforded.
Dealing with these challenges is not new to CSPs. However, the legacy tools and processes that CSPs have at their disposal for capacity analytics add a massive, unprecedented burden, making the job a lot more inefficient. What if there was a way CSPs could be fully equipped to deliver exceptional network performance through enhanced agility and accuracy?
How CSPs can enable accurate network planning and optimization activities
The journey towards building accuracy for CSPs begins with laying the foundation in three key areas:
- Ensuring the highest quality of data through intelligent data management
- Bringing in operational efficiency by leveraging advanced automation methods
- Infusing subject matter expertise (SME) to the outcome through domain-driven data science
To understand more on the three strategic pillars of a network analytics solution