As IT departments are overwhelmed with the overwhelming amount of data now being collected on network health and performance, it’s easy to see why the enthusiasm around AIOps is brewing.
AIOps uses artificial intelligence and machine learning to digest and analyze large volumes of data from the computing environment and apply this intelligence to automate network monitoring and management tasks.
With AIOps, network administrators can tackle tasks where there is too much data for a human to process, help reduce response times and downtime, and streamline government IT operations.
Let’s take a look at some general areas where government agencies can leverage AIOps and how they can position themselves to seize this next big wave of network modernization.
AIOps-based observability: predicting the unpredictable
Imagine a network capable of predicting a security problem or threat before it happens. This is what AIOps provides. A comprehensive AIOps approach to network management will automate the collection and analysis of event data on the network. With this information, operations teams can anticipate network problems, detect anomalies, get the context they need to remedy them, and take action before performance is impacted.
Because AIOps is built on machine learning, it will continuously improve over time, becoming more familiar with the agency’s IT environment and providing more information on probable root causes and recommending remedies. actions so that network teams can continuously optimize the performance, security and reliability of the network infrastructure.
Then, while network administrators work to resolve incidents, AIOps will observe the corrective actions taken. When similar incidents arise in the future, it will use these observations to inform and trigger automated mitigation workflows, all with minimal user effort. This relieves teams of the initial triage, helps them meet SLAs, and ensures a more resilient, self-sufficient network that gets smarter over time.
Taking SD-WAN to the Next Level
Agencies have been exploring software-defined wide area networks for some time. Its many benefits include simplified network provisioning and management, improved network agility, and built-in security. SD-WAN brings secure networking to the furthest edge of the network.
However, as the needs of the agency become more complex over time, the manual configurations associated with SD-WANs can become cumbersome to set up and manage. This is where AIOps comes in.
AIOps introduces a level of autonomy, allowing agencies to automate many of the heavy tasks required to manage an SD-WAN network and focus on strategic initiatives.
AI-based analytics are also integrated with SD-WAN solutions to optimize security. For example, AIOps can help agencies predict the impact of certain events on network security posture and proactively mitigate the risk of any network and security related changes.
With more data flowing through networks, the cloud, and software-as-a-service applications, AIOps-based intelligent SD-WANs are the smart grids of the future. They provide a unified, real-time view of network and application performance and agency security status, and help network administrators move from a reactive to a proactive approach to network management.
Master data at scale
There is a caveat regarding the opportunities offered by AIOps: the need to overcome data silos, especially in hybrid environments.
Data generated by government networks is skyrocketing. This is a good thing. After all, the more data an AIOps solution ingests, the more meaningful information it can extract for effective action.
But networking data is often siled between multiple monitoring tools. For AIOps to learn and make contextual decisions, precise and structured data must be available in real time and from a single source. It’s a future that state agencies should start planning for now. This means finding ways to break down data silos and consolidate or interleave data sources across hybrid infrastructures (on-premises, remote networks, and in the cloud) into a single data model.
The good news is that modern hybrid network and data management approaches can aggregate this data at scale, including system logs, metrics and traces, as well as topological data and relationships. When this happens, AIOps can combine and interface with all data simultaneously so that it can learn, analyze and act, giving agencies the mission advantage of automated network environments, self-healing and secure.