Understanding CSPs’ objectives and requirements for network analytics solutions
Network analytics is a key enabler
Network analytics solutions provide CSPs with insights required to address their top business challenges. According to Ovum’s 2019 ICT Enterprise Insight survey of over 300 senior telecom executives across the business, CSPs’ top challenges this year are launching digital services and personalizing customer experience.
The increasing consumption of digital services such as video, IoT and other OTT services is driving a massive increase in the traffic going through CSPs’ networks. The ongoing transformation within CSPs’ networks on the other hand is increasing in complexity in terms of network management; leading to growing operational costs.
Unfortunately, pricing pressures from competitors limits how well CSPs can monetize new services and therefore grow revenues. Alongside this, customers are demanding improved experiences in line with those obtained from web scale internet service providers, like Google and Amazon. These prevailing conditions make it difficult for CSPs to tackle their top business challenges.
For CSPs to address these challenges they require deep visibility into their networks and operations, to assess performance and identify optimal decisions to take to meet business priorities. Network analytics tools are therefore critical as they enable the analysis of network data and related data sources; providing insights into network and service performance, and customer behavior. With these insights, CSPs can make smarter decisions, drive automated operations and personalize engagements with customers. The application of AI techniques to network analytics tools provides CSPs the opportunity to derive more value from these tools.
Use cases are in abundance, however challenges exist which can be addressed
There are numerous network analytics use cases, and these can be categorized based on CSPs’ top business priorities; operations efficiency, improving customer experience and enabling revenue growth.
Figure 1. Network analytics use cases
The use cases are varied but can be achieved using a similar approach. A range of data sets relating to a use case needs to be collected, stored or immediately analyzed (depending on how quickly insights are required). These data sets could be network and non-network related. Non-network related data sets include CRM data and device data. Data sets need to be cleaned and standardized and then passed on to the analytics tools for analysis. Results generated can be passed on to dashboards to support further decision-making processes or integrated with other systems to take immediate actions.
The application of AI techniques like machine learning can enable CSPs predict events before they occur and assess how best to address them, for example using automation. With this capability, CSPs can save millions of dollars in costs and drive operational efficiencies. Operators like AT&T, Comcast, Hutchison 3 Indonesia, NTT Docomo, Telefonica UK, T-Mobile and SK Telecom are deriving benefits from the application of AI techniques to predict network issues and identify the next best action to resolve them.
Implementing network analytics use cases contributes to both top and bottom line performance for CSPs. There are however, barriers that CSPs must address to successfully deploy any network analytics use case and these include:
The siloed approach to network management in the CSP environment
Understanding the data sources required to implement each use case and how to get access to them
Ensuring data is of high quality
Getting the buy in of the business leaders to ensure smooth deployment of these tools
Having the required data science skills
Addressing these challenges requires CSPs to make changes to internal operations, as well as work with vendors that provide expertise in developing, delivering and managing network analytics use cases.
Key requirements CSPs should identify when selecting network analytics vendors
The depth of insights required to address CSPs’ current challenges cannot be supported by traditional network analytics tools. Traditional tools operate in siloed network domains, focus mainly on monitoring and analyzing network infrastructure and do not operate in real time. Consequently, there are several key capabilities that today’s network analytics vendors must be able to offer to satisfy CSPs requirements.
Figure 2. Key requirements when selecting network analytics vendors
Clearly, meeting all these requirements is no small feat for network analytics vendors. Vendors leading in this space will have a strategy that enables fast development and deployment of analytics tools. They must build the right partnerships, deliver professional services, develop a roadmap that fosters continuous innovation, and offer an open platform that enables ease of integration and fosters closed loop automation. For more information on the vendors that are leaders in this space, see Ovum’s latest research on network analytics solutions: Ovum Decision Matrix: Selecting a network analytics solution for CSPs.
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