Advanced analytics for telecoms: how to unlock AI and ML capabilities
With more data than ever before, advanced analytics forms the base for AI and ML capabilities to play an essential role into the day-to-day operations of major telcos enabling smarter, more agile networks.
The value of advanced analytics for telcos
The explosion of sensors and the increasing comfort of executives to make data-informed decisions is driving new opportunities for telcos to utilize machine learning (ML) and artificial intelligence (AI). While 99% of operators surveyed in a recent STL report wanted to leverage these emerging technologies to increase network efficiency, different operators were at different points along their data analytics journey, with nearly all encountering obstacles.
However, in order to unlock the full potential of AI and ML in the long term, telecoms operators should focus on investing into developing their advanced analytics capabilities today, to form a strong foundation and base for future AI and ML skills and capabilities down the line.
Key opportunities for advanced analytics
STL Partners identified six types of problems that A3 (automation, advanced analytics and AI) can help with and conducted a modelling exercise to assess the value of A3 into a telco’s processes. Our analysis predicted that 60% of the value from A3 will come from the network domain.
One key problem that telcos face is making sense of complex data, where using advanced analytics and machine learning can help to identify patterns, diagnose problems and predict/prescript resolutions.
Within this domain, there are two key use cases where we report the largest potential financial benefit (i.e. more than $50mn in yearly financial benefit):
1. Network (resource) management
2. Service assurance
Among the most important opportunities identified is network (resource) management, which includes network planning, deployment and maintenance, as well as the management of network capacity and resources. This is no particular surprise given that the management of physical and virtual network infrastructure accounts for a significant proportion of operators’ capital and operational expenditure. A3, including advanced analytics in particular, can play an important role in enabling operators to leverage network insights and data to make better informed decisions around new network investments. Predictive maintenance has also singled out by operators as an important use case that will enable them to more efficiently allocate resources to repair their networks, substantially lowering OpEx.
This is another key area where advanced analytics (and eventually ML and AI) can play a significant role, particularly in the short term. This is also heavily intertwined with the virtualisation and cloudification of networks (and network functions) whereby new service assurance products will be required in the next 5-10 years to support an increasingly multi-vendor, disaggregated environment as well as new types of edge computing services and maturing IoT use cases. That is not to say that the value of advanced analytics is primarily in the “new” stuff, our research has found that there is still value that has not been realised and can be derived from A3 in service assurance for 3G and 4G LTE, for example with the introduction of more predictive algorithms. Advanced analytics in service assurance will set up greater possibilities with the use of machine learning further down the line for more proactive root cause analysis.
Next steps: preparing for greater AI and ML opportunities
Ensuring that data is clean and unified
The majority of operators cited that data collection and management is still a key issue and challenge, therefore operators should focus on improving this to ensure that their data is clean, complete and unified, and that their data lakes are up-to-date and accurate.
Building analytics skills and capabilities
Telecoms operators should look to build the skills internally to make full use of their data, whether that be through hiring more data scientists or upskilling existing employees. Data analytics should not be seen purely as a capability to leverage internally, for example within the network, but also seen as one that others can leverage (i.e. moving to charging customers for outcomes and insights from analytics).
Creating a data-centric culture
To create a data-centric culture telcos should consider creating a Chief Data Officer that takes ownership of data collection, aggregation, cleaning, and data analysis. This helps to create a data-centric culture by having an advocate for analysts on the board. Talent acquisition and retention of individuals with key analytics skills should also be prioritized.
ML/AI provide telcos with exciting new opportunities, but it will take time, especially for lower-tier operators, to see them bear fruit.
Author: Ciarán Mulqueen is a Consultant at STL Partners, specialising in telco-cloud