AIOps is rapidly evolving to meet the growing needs of digitalizing businesses with major transitions taking place just this year. According to a just-published Gartner’s market report that features Loom Systems, “The Market Guide for AIOps Platforms”, AIOps vendors have begun combining their IT-operations-oriented big data and machine learning projects that in the last few years were separate.
As led by Loom Systems, the goal is to enhance performance monitoring to the extent that classical application performance monitoring (APM) and network performance monitoring and diagnostics (NPMD) tools become obsolete. AIOps vendors want to give digitalizing businesses the best of both worlds: one tool with comprehensive monitoring and analytics that uses AI to automatically scale with business growth.
Gartner predicts AIOps will continue to make great strides in the marketplace. Compared to 5% today that take advantage of AIOps to fully or partially replace their service desk monitoring and process automation, Gartner predicts by 2022 40% of all large enterprises will be on board. For those 35% contemplating an AIOps solutions today, Gartner has a number of recommendations, including:
- Ensuring the success of AIOps deployment by adding functionality incrementally and focusing throughout on historical data.
In our white paper, “Pitfalls to Avoid when Picking an ITOA solution”, we recommend that businesses pay close attention to the requirements that AIOps vendors have for a proof of concept (POC). Specifically, we warn businesses against vendors that require them to pre-define future requirements for an AIOps POC rather than focus on historical data and today’s ad-hoc needs.
Further, we recommend that businesses avoid any AIOps vendor that requires significant upfront investment to prepare projects, hardware, and software for a POC before even one report is run on actual data that’s connected to your own data sources. It’s a tell-tale sign that the vendor is selling archaic technology or misleading them about the capabilities of your analytics tool. A Loom POC, in contrast, will give businesses value by connecting to and delivering insight on historical data right away while demonstrating a roadmap to full functionality.
- Ensure that the AIOps vendor provides comprehensive monitoring that includes ingestion of and access to log data, text data, wire data, metrics, API data and social-media-derived user sentiment data.
In Gartner’s report, they detail the monitoring capabilities of the top AIOps tools, dividing comprehensive monitoring into 11 categories that include historical data management, streaming data management, log data ingestion, wire data ingestion, document text ingestion, automated pattern discovery and prediction, anomaly detection, root cause determination, on-premise delivery, and software as a service.
Of these, Loom is listed as providing all but wire data and document text data. In addition, Loom offers POCs that instantaneously connect to real data sources and deliver value plus, recognizing the hurdles of data overload that too many vendors’ pricing approaches exacerbate rather than resolve, flat pricing without data caps.
- Ensure that the AIOps tool will deepen your IT Ops analytical skills set with four phases of IT-operations-oriented machine learning: visualization and statistical analysis, automated pattern discovery, pattern-based prediction, and root cause analysis.
Here is where Loom Systems excels, reducing analysis, discovery, and prediction to a single dashboard that makes the AI journey modular and accessible to teams that may not have deep AI experience. While other tools are complex to navigate and will end up requiring outside consultation to implement and derive value (also advised against in our “Pitalls to Avoid When Picking an ITOA Solution,” Loom deploys instantaneously, delivers value right away, and scales as teams gain experiences and businesses grow.
Adoption of AIOps vendors will grow in the next decade as businesses increasingly undergo digital transformation. With digitalization, the roles and responsibilities of IT Ops will dramatically expand, and AIOps tools will transition from being a helpful tool that the reduces the mean time to issue resolution (MTTR) to a fundamentally different approach to the complex IT environment.
In addition to reducing MTTR, digitalizing businesses will, according to Gartner, look to AIOps to “improve their engagement with incidents and problems, apply big data and machine learning to ticketing and CMDB functionality, and drive automation at the interface between development and production.” The choice of an AIOps vendor, then, is of the utmost importance as competitive businesses transition to the next generation of AI-powered monitoring.