The Future of Network Management with AIOps

Network management strategy and software have evolved steadily over the course of the 21st century, but a newer development is hinting at a transformative future for network management: artificial intelligence for IT operations, or AIOps. 

Some enterprises are still hesitant to adopt new AIOps technologies and initiatives. But as we’ve seen with the growth of AI and machine learning in other areas, AIOps is likely to grow quickly and become the standard consolidation method for the future of network management.

Also Read: Bringing Hyperautomation to ITOps

AIOps and the Future of Enterprise Management

What is AIOps?

AIOps is the process of incorporating machine learning and big data analytics into network management in order to automate network monitoring, troubleshooting, and other network management goals. Some experts believe the term is a misnomer, as AIOps relies more heavily on machine learning actions than on artificial intelligence-powered human behaviors. The idea is not to replace manpower on the network, but rather to automate network processes so that network administrators can focus on more strategic network tasks. 

AIOps vs. Digital Experience Monitoring

AIOps is growing at a rapid pace, and so is a similar network technology: digital experience monitoring (DEM). Although the two network management approaches are very similar, AIOps goes a step further than DEM. Digital experience monitoring focuses solely on combining application performance monitoring (APM) and end user experience monitoring (EUEM) to avoid data and performance silos. However, AIOps’s use of machine learning and data analytics not only assesses performance, but also automates fixes that would normally have to be handled by a network admin.

Both tools are increasingly replacing other network technology. In a recent report, Gartner predicted that AIOps and digital experience monitoring tools were used as the exclusive network infrastructure tool for around 5% of enterprises, but they expect that number to grow to 30% of enterprises by 2023.

Benefits of AIOps in the Enterprise Network

AIOps offers several key benefits to the enterprise networks that willingly embrace the software and network management practice:

Smart Data for Troubleshooting

It automates network management and monitoring with more intelligent data via big data analytics. This in-depth data analysis and application is particularly helpful for optimized troubleshooting and network security needs.

Improved User Experience

Machine learning and automation helps AIOps software to detect network problems sooner, or perhaps before they become a problem. The speed and thoroughness of this technology improve the overall user experience with limited effort on the part of network admins.

Strategic Growth for Network Administrators

ML-powered automation in AIOps primarily eliminates the need to perform repetitive, time-consuming tasks. This automation frees up time for network administrators to focus on higher-value strategy needs.

Avoiding Communication and Technology Silos

AIOps allows enterprise networks to stream relevant analytics into one space for network monitoring and strategy. This consolidation approach eliminates some of the networking tool sprawl, where so many other network management tools are siloed and focused on one network task. Not only is it quicker, more efficient, and less expensive to consolidate, it also ensures that all teams and network tools have the same information when making changes to the network.

Current AIOps Solutions

Although AIOps is a fairly new field, several software vendors have jumped into the space and are offering comprehensive solutions to enterprises. Most solutions currently handle root cause analysis and problem prediction/solving on the network. Some of the top AIOps solutions currently on the market are: 

  • Dynatrace
  • LogicMonitor
  • Splunk
  • ZIF
  • Moogsoft

More AIOps Solutions to Look Out For: Top AIOps Tools & Platforms of 2021

The Future of AIOps

AIOps is quickly becoming a reality across global industries, but in many ways, AIOps has not truly arrived at the enterprise level. As AIOps becomes a more typical way for enterprises to manage their networks, organizations will need to consider these best practices and changes looming on the horizon for AIOps and the digital transformation that it ushers in.

The Importance of Improving Data Quality

 AIOps tools are only as successful as the data quality that powers them to work. That’s why networks that are looking to move toward AIOps must also consider what tools and data analysis best practices they’ll need to make their data actionable. 

  • Database management systems help organizations to manage huge quantities and types of big data, whether they’re working with unstructured, structured, quantitative, or qualitative data.
  • AIOps isn’t possible if ML-powered technology cannot read an enterprise’s data. Data annotation is the most important data step that enterprises must become familiar with and implement in order to make machine learning possible across their network. 

Improved data quality will be important to AIOps tools across the board, because the automation of so many network skills will use a larger amount of data that other tools may have previously overlooked or considered “useless” to operations. With stronger data practices, enterprise AIOps will be able to improve security practices like anomaly detection in the future.

Integrated Security and Preparation for New Attacks

As AIOps matures and provides stronger data insights over time, experts predict that enterprises will marry AIOps with other existing security software. Because AIOps provides both data visibility and real-time monitoring of how the network is being used, the integration of AIOps with other security tools can create more informed data segmentation and improve alerts when an inappropriate action is taken by a user on the network.

But with machine learning-powered technology like AIOps also comes the possibility for new kinds of malicious network attacks. Adversarial machine learning attacks are not quite commonplace attacks as of yet, but laboratory tests of ML hacks show that these attacks will likely succeed more often in the future. With this knowledge, enterprises must optimize their overall security best practices (both through technology and stringent policies), adjust their ML logarithms over time, and back up their most sensitive data in other locations in case an attack on their AIOps succeeds.

Also Read: Establishing Server Security Best Practices

Shelby Hiter
Shelby Hiter
Shelby Hiter is a marketing content writer with more than five years of experience in writing and editing, focusing on healthcare, technology, data, enterprise IT, and technology marketing. She currently writes for three different digital publications in the technology industry: Datamation, Enterprise Networking Planet, and CIO Insight. When she’s not writing, Shelby loves finding group trivia events with friends, cross stitching decorations for her home, reading too many novels, and turning her puppy into a social media influencer.

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