How agencies are benefitting from AIOps
GettyImages/piranka
Connecting state and local government leaders
With AIOps, network administrators can tackle jobs where there’s too much data for any human to process, help reduce response time and downtime and streamline government IT operations.
As IT departments get buried under the overwhelming amount of data now being collected about network health and performance, it’s easy to see why excitement around AIOps is brewing.
AIOps is the use of artificial intelligence and machine learning to digest and analyze large volumes of data from across the IT environment and apply this intelligence to automate network monitoring and management tasks.
With AIOps, network administrators can tackle jobs where there’s too much data for any human to process, help reduce response time and downtime and streamline government IT operations.
Let’s look at some broad areas where government agencies can leverage AIOps and how they can position themselves to catch this next big wave in network modernization.
AIOps-powered observability: Predicting the unpredictable
Imagine a network capable of predicting a problem or a security threat before it arises. This is what AIOps delivers. A comprehensive AIOps approach to network management will automate the collection and analysis of event data across the network. With this insight, operations teams can anticipate network issues, detect anomalies, gain the context they need to remediate and act before performance is impacted.
Because AIOps relies on machine learning, it will continuously improve over time, learning about the agency’s IT environment and providing more insight into probable root causes and recommending actions so network teams can continually optimize the performance, security and reliability of the network infrastructure.
Then, as network administrators work to resolve incidents, AIOps will observe the remedial steps 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 from initial triage, helps them meet service-level agreements and ensures a more resilient, autonomous network that gets smarter over time.
Taking SD-WAN to the next level
Agencies have been exploring software-defined wide-area networking 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 remotest edge of the network.
However, as the needs of the agency become more complex over time, the manual configurations associated with SD-WANs could get cumbersome to set up and manage. This is where AIOps comes in.
AIOps introduces a level of autonomy, enabling agencies to automate away much of the heavy lifting needed to manage an SD-WAN network and freeing them to focus on strategic initiatives.
AI-based analytics are also being integrated into SD-WAN solutions to optimize security. For instance, AIOps can help agencies predict how certain events will impact the network’s security posture and proactively mitigate the risk of any network- and security-related changes.
With more data traversing networks, cloud, and software-as-a-service apps, smart AIOps-infused SD-WANs are the intelligent networks of the future. They provide a real-time, unified view into agencies’ network and application performance and security posture and help network administrators move from a reactive to a proactive approach to network management.
Get a grip on data at scale
There’s one caveat to the opportunities AIOps delivers: the need to overcome data silos, especially in hybrid environments.
The 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 derive for efficient actioning.
But networking data is often siloed across multiple monitoring tools. For AIOps to learn and make contextual decisions, accurate, structured data must be available in real time and from a single source. It’s a future state agencies should start planning for now. This means finding ways to break down data silos and consolidate or interlace data sources across hybrid infrastructures—on-premises, remote networks and in the cloud—into a single data model.
The good news is modern approaches to hybrid network and data management can aggregate such 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 it can learn, analyze and act, giving agencies the mission advantage of automated, self-healing and secure network environments.