AIOps is a hot topic across the IT industry, IT leaders are enchanted by the possibility of capturing more value from their rapidly growing supplies of data with the use of machine learning and analytics.
Of course, data collection and processing relies on the underlying network, and the network itself is a target for AIOps-based management and automation. Network analytics are, therefore, an essential part of AIOps. Here are just five contributions a unified network analytics solution can make:
#1 Ensure quality of data
We’ve all heard the phrase, “garbage in, garbage out,” often shortened to GIGO. The data fed to machine learning tools must be high grade, appropriate, and relevant or, well, strange outcomes happen. Bad data results in inaccurate pattern identification and poor predictions, and with the “black box” nature of much of the AI field, it can be nearly impossible to assess or correct these shortcomings after the fact.
In particular, network data harvested using separate tools or from separate silos may be inconsistent, incomplete, or out of date. The best network analytics solutions, however, are unified packages that avoid silos to create a single, reliable compilation of data from across the network. Drawing on such systems, enterprise AIOps can see the end of GIGO and benefit from QIQO—quality in, quality out.
#2 Improve infrastructure management
To note that IT infrastructure has grown more complex is a vast understatement. IT teams today frequently have difficulty even visualizing the topography of their increasingly hybrid infrastructures, especially if hampered by older, less robust toolsets. Organic growth and acquisitions can also lead enterprises to “glue on” unfamiliar networks and sometimes execute networking changes and expansions in an ad hoc manner just to keep up.
Fortunately, a holistic network analytics solution can put the entire network and all the management functions on a single screen. Analytics are run on everything—cloud, multi-cloud, hybrid, and on-premises environments—all from one central resource. This not provides instant visibility of the network infrastructure and also produces consistent data sets across the full scope of IT operations, without swivel chair management. It’s a necessary foundation for comprehensive, effective AIOps.
#3 Maintain network performance
IT teams have more data about their networks’ status than ever before, but they are also being confronted with an overwhelming number of alerts on network management systems. The sheer time devoted to sifting through and evaluating alerts to identify actionable items often delays response and compromises network performance.
Network analytics comes to the rescue here. Machine learning applied to the network layer enables identification of true network errors in real time, so they can be immediately addressed. When combined with intelligent event suppression that prevents alert overload, network analytics solutions help deal with incidents before they affect business services.
Reducing false alarms is a key benefit companies are seeking from AIOps, and the right network analytics solution sets the stage and informs an overall AIOps process.
#4 Generate continuous insight
Without high-quality network analytics, the network becomes a blind spot where operational impacts can’t be easily traced. This is problematic, since the network is the core of all IT operations and the business as a whole.
End-to-end visibility and monitoring tools help enhance and maintain network performance, but adding in network analytics integrations delivers greater insight into the AIOps process. With a constant flow of powerful insights, better root cause analysis of incidents, quicker response, and greater efficiency soon follow.
#5 Create more value for end users
The more a business knows about its customers, the more value it can provide. Much of the data required to understand customer needs, usage patterns, etc., derives from the network layer. Thus network analytics empowers IT teams to leverage a comprehensive view of the customer and the full revenue potential of the network. Supported by network analytics, AIOps can do a better job learning and adapting to user data to enhance performance and that be-all, end-all—the customer experience.
(Authored by Paul Mercina, Director of Product Management, Park Place Technologies)