Digital generated image of data.
AIOps is about leveraging AI (Artificial Intelligence) and ML (Machine Learning) to automate various parts of an organization’s IT operations. “This provides modern ITOps teams a real-time understanding of any type of issues,” said Venugopala Chalamala, who is the founder and CEO of Atlas. “Traditional IT management solutions can’t keep up with the volume as well as provide real-time insight and predictive analysis.”
The need for AIOps has accelerated because of the growing complexities of IT systems, the explosive growth of data and the sudden increase in remote working arrangements. Gartner forecasts that the exclusive use of tools for this category will go from 5% of large enterprises in 2018 to 30% by 2023.
So then if your organization is looking at AIOps, how do you get started and what are some of the strategies to consider? Well, to see, I have reached reached out to various tech experts to get their advice:
Wilson Pang, the Chief Technology Officer at Appen:
Clearly define the problem you want AIOps to solve. Is the goal to detect anomalies that are hard to find by a human? Or do you want a tool to enable your OPS team to identify root causes quickly when an issue occurs? Or do you want to deploy some automatic recovery mechanism through AI? AIOps can help in many different areas. This means you need to define a clear goal that will help you understand the potential ROI (Return On Investment).
Rosaria Silipo, Ph.D., who is the principal data scientist at KNIME:
You need a good understanding of what is necessary to monitor and store. The more AI models, the more complex the monitoring strategy. Then, you need to define the criteria of acceptable performances by a model or a group of models. Finally, a strategy is needed to retrigger training when performance drops below an acceptance threshold.
Ali Siddiqui, the Chief Product Officer at BMC:
The value of an AIOps tool increases with the broad range of data that you can observe and analyze. It is also important that there is an open approach that can integrate with your existing IT tools and data sources. Once you have your tools, identify the right processes that support agility and collaboration across functions to integrate across Dev, Ops, and security. Finally, organizations have to think about the people–redeploy your most valuable resource to ensure the right tools and processes are in place and you can act on insights.
Muddu Sudhakar, the founder and CEO of Aisera:
The key is to have a good incident management system. You also need to have a very good logging system in place. Also, there should be proactive and predictive management of incidents and outages. You don’t want humans doing this.
Annette Sheppard, the Senior Product Marketing Manager at New Relic:
When it comes to something as transformative as AIOps, start small. Choose a low-scale test case, learn, adapt, tweak, and grow from there. That way, if things go wrong, the consequences won’t be quite so disastrous.
Tej Redkar, the Chief Product Officer at LogicMonitor:
Look for an AIOps platform that can perform automated procedures based on analytics drawn from your pools of data. Oftentimes, this data is already housed within your organization’s monitoring solutions. Then ask if the platform has dynamic thresholds, root cause analysis, forecasting and anomaly detection capabilities.
Eric Tyree, the head of AI and Research at Blue Prism:
AI is easy, Ops is hard: AIOps is all about automation, so make sure you are thinking about the whole automation toolbox. Mature automation programs should look to achieve a formula along the lines of 1/3 systems (AI, BPM, straight through processing), 1/3 human and 1/3 Digital Workers.
Chris Burchett, the Group Vice President of Product Development at Blue Yonder:
AIOps requires lots of logfile data in order to train the Machine Learning to recognize what is an exception and what is a normal operation. Typically many weeks of normal data are needed in addition to specific data when anomalies occur. So a good log aggregation and management practice should be in place in order to adequately use AIOps.
Jim Richberg, the Field Chief Information Security Officer at Fortinet:
For AIOps to be successful, it starts with consolidation and integration. Instead of siloed systems, a unified platform is important to provide rapid response and wider network visibility.
Michael Procopio,Product Manager at Micro Focus:
Before implementing AIOps, it’s important to have buy-in and support from C-suite leaders. To do this, develop a small test project to solve a single pain point and show the C-suite tangible benefits of AIOps. From there, IT professionals can leverage this success to recruit the executive team’s support and alignment to further integrate AIOps into the enterprise. Once you have executive buy-in, observe and incorporate all accessible data from all networks, servers, applications, etc. AI works more efficiently with more data, and the more data you collect the more likely you are to find the source of the problem faster. After businesses have conducted a holistic integration of business data, make sure to take a domain-agnostic approach to AI–that is, ensure you can use AI to diagnose issues across domains. If you’re alerted to an issue in one domain, but the root cause of the problem is in another domain, you need to see this correlation–which is only possible when AI isn’t working in siloes.
Bob Friday, the Vice President and Chief Technology Officer for the AI-Driven Enterprise at Juniper Networks:
Great wine starts with great grapes; similarly, great AIOps starts with great data. The first step on the AIOps journey is data, and a company’s first step should be an audit of their networking equipment and making sure it has the support for telemetry and streaming data back to an AIOps platform. The second step is choosing between a domain agnostic or domain specific AIOps platform. While domain agnostic platforms are more flexible, most companies will find a domain specific platform a quicker path to ROI.
Jeff Hausman, the Vice President and General Manager of Operation Management at ServiceNow:
The best way for an organization to embark on its AIOps journey is to start with a focused approach, and then scale as needed. Organizations should take a look at their IT incidents and identify issues that regularly occur to determine where an initial AIOps deployment would deliver the biggest ROI. For example, we have seen success with customers deploying AI-powered virtual agents to help resolve and reduce the influx of incident reports amid remote working. We also provide customers with a framework that helps them identify the most impactful use cases for their product, ensuring customers will see a positive business impact and are set up for long term success from these new solutions.
By starting focused, IT leaders can showcase their initial AIOps deployment and begin to establish the data-driven cultural mindset that is needed when first deploying solutions like this.
Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps. He also has developed various online courses, such as for the COBOL and Python programming languages.