To enhance customer experience, many Service Providers are in the early phases of evolving their networks to support newer technologies such as SDN, NFV and 5G. One of the core drivers for this transformation is to distinguish themselves in a saturated market by being more efficient with their operating processes and to provide faster and higher quality new service offerings – all of which translates to delivering a better customer experience.
Because transformation is complex, a phased approach to minimize risk and to get some ‘quick wins’ by implementing small changes to achieve results faster and use fewer resources over time is a good approach. Digital transformation can be accelerated in a number of ways, but one of fastest is to make the best use of your data by analyzing it using Artificial Intelligence, specifically machine learning, and using the results of this analysis to drive automation. Another ‘quick win’ is to review your existing product set to see if you have the right tools for the right jobs. Some simple and low risk changes can often result in some big value gains.
Artificial intelligence (AI) can yield fresh insights for business operations and service management. Many organizations have already embraced AI for tasks like optimization, security, anomaly detection, chatbots and traffic identification. In the area of network and operations management, there are additional opportunities that can address pain points and drive more business value. For example, the skills of highly trained network engineers are not easily turned into a set of simple instructions to pass on to new hires, nor can they be converted to an algorithm. This is particularly problematic, as it’s predicted that 70% of all network operations staff will retire by 2024. However, a cognitive engine, an advanced form of AI, can learn from how people make decisions on complex data sets in order to create its own rules. Currently, most businesses only utilize narrow AI to optimize single, specific tasks. But general machine learning is paving the way for knowledge transfer. By learning from human operators, AI tools can provide the best remediation processes and automatically suggest or implement changes the next time a similar issue recurs, thereby increasing the accuracy of the analysis ahead of triggering automation and resulting in streamlining operations.
Another significant pain point for operators is the dependency on legacy tools to manage and process huge data sets – many of these tools were not built for this purpose. However, applying machine learning to these data sets is possible and can deliver new insights into the data. With many legacy systems, the concept of filtering or de-duplicating events is common. Twenty years ago, this was the only way to ‘manage’ the number of events being displayed to an operator from rapidly growing networks and infrastructure. However, when you are looking for patterns in data, having ALL of the data available is critical for machine learning to work effectively. Luckily, with the arrival low-cost compute and storage resources, we now don’t have the challenges of collecting, storing and processing massive amounts of data in real-time. However, we still need to ensure that the data is as ‘pure’ as possible which means no pre-processing, filtering, sorting, de-duplicating etc. and this is sometimes a challenge when legacy systems are still involved in the end-to-end operational process.
Network Tool Consolidation
As mentioned above, the functionality of legacy tools may impact your ability to use AI and Machine Learning. In addition to this, the number of management and monitoring tools being used in an organization is also a serious consideration when it comes to driving digital transformation. While skipping straight to the implementation of automation and artificial intelligence may be possible in some cases, you’ll deliver greater business value in the short term by streamlining the basics of your operations beforehand. Look first to perfect network management tools and systems as a stepping stone to larger transformation.
Tool consolidation is a proven method for creating greater efficiencies in the operations center. According to participants in a recent webinar, nearly 83% of attendees estimated that their network uses five or more different types of service assurance tools. However, many of our customers tell us that on average they have more than 20 tools deployed in their environment – many of which are not being updated or maintained. Too many tools create an unnecessarily complex infrastructure that leads to operational friction from shifting back and forth between applications. In addition, such an environment can often present security risks, is costly and difficult to maintain. Introducing newer technologies such as 5G, SDN and virtualization into your environment only adds layers of complexity.
Whether you are looking to modernize your legacy systems, consolidate existing tools or implement AI and machine learning to make best use of your data, Federos’ unified platform will help you accelerate your digital transformation.
At the end of the day, what makes accelerating digital transformation worthwhile is the need to carve out innovation for growing business. Any company that focuses on solely retaining their current customers will not make it in the long term. Leveraging transformation needs to be a priority for organizations in order to improve customer engagement, bring new service offerings to market, create an attractive environment for recruitment and develop agile operations.