AIOPS ARCHITECTURE NETWORK OPERATIONS
Mediaan has assisted a Dutch and German telecom provider with the implementation of an AIOps platform. The AIOps solution covered a big data environment (data lake, data warehouse, data science), in combination with robotic process automation (RPA). Typically, customers are lost in a jungle of network monitoring and alerting systems, which led to very high operational costs. Mediaan was able to support the customer setting up a central Big Data environment that now acts as the foundation for AI/ML-driven monitoring, predictive maintenance applications, and capacity forecasting.
Essentially, Mediaan supported in the following areas:
- The data architecture, roadmap and implementation support of an AIOps platform
Robotic Process Automation (RPA)
Identify and implement relevant use cases for “autonomous” network operations
Convert their existing situation (with a large diversity of tooling) into an integral and more homogeneous AIOps solution
The idea of the project was to extract as much behavioral network data as possible, such as metrics and logging information from network elements. This is usually done by a wide variety of tools so-called “stovepipes” which prevent effective anomaly detection and root cause analysis. By capturing this information centrally in a Big Data Platform and adding AI / ML capabilities to this, an end-to-end monitoring and alerting environment with more intelligence could be created. Intelligence that can be used to identify, classify and even prevent (future) incidents.
Our customer was lost in a jungle of monitoring and alerting systems for their network management. This was resulting in a diversity of problems:
– A very long learning curve for network operators (almost a year)
– It took a very long time to find out the cause of a failure
– It was very labor-intensive to classify network issues by urgency
– All the above was leading to very high operational costs
MEDIAAN IN ACTION
By combining knowledge and experience in the field of data architecture, data engineering, data science, and RPA, we were able to assist the customer step by step to the intelligent automation of their network operations. Our experience in the telecommunications market enabled us to identify and implement relevant use cases for “autonomous” network operations. We also supported our customers to convert their existing situation (having a large diversity of tooling) into an integral and more homogeneous AIOps solution.
Thanks to our knowledge and experience in the field of data architecture, data engineering, data science and RPA, we have supported our client in the development of a vision and the step-by-step implementation of an AIOps solution. This also involved remediation of the jungle for tooling of network operations and, where necessary, the replacement by state of the art technologies. The result of the project is a central Big Data environment that acts as the foundation for AI/ML-driven monitoring, predictive maintenance and capacity forecasting.
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