OSS/BSS + AI + Automation = Artificial Intelligence for IT Operations.
During the last years we have seen CSPs deeply investing in data analysis. Today, they want to go a step further by adding artificial intelligence to improve the user experience, increase agility and improve both efficiency and reliability.
Until today, network monitoring was mainly focused on providing visibility. However, after decades of progress in the field of artificial intelligence, we are facing the challenge of applying accordingly the different OSS data machine learning algorithms to be able to detect potential failures before they occur. For this, these systems must be integrated and later trained with sufficient sources of information such as: Meteorological, alarms, KPIs, logs, incidents registered in ticketing tools, etc.
According to Gartner, the aim of AIOps is to detect anomalies and to react accordingly, taking advantage of proper automation. We can think of AIOps as Continuous Integration and Deployment (CI/CD) for core IT functions.
Satec has been working in the world of OSS for many years, where we have acquired a great level of experience, both in terms of customer needs and in-depth market-specific tools. All this, together with our application development capabilities and our product division alvatross, has allowed us to build a proposal with our own solutions in the NGOSS field.
Our solutions are capable of integrating any type of source, making use of a set of powerful and modern pieces focused on data management and storage, which offers great flexibility and advanced functionalities. To all this, we add data analytics capabilities and machine learning to allow, for example, the detection of anomalies both for supervised and unsupervised models, achieving the so-called Close-Loop process.
Finally, for those of us who have been working in the OSS/BSS area for several years, we have an opportunity to renew ourselves and raise our knowledge. Until now, our role was to provide visibility, but with the arrival of AIOps, we will be able to apply machine learning algorithms to launch automations for both automatic deployment and troubleshooting.