In this blog, we will walk through the scenario of sending or E-bonding ServiceNow incidents to 5 destinations simultaneously, using Robotic Data Automation and AIOps Studio. E-bonding refers to a scenario where data is delivered (one-way) or synchronized (two-way) between two or different systems, which are typically under different administrative boundaries. E-Bonding term originally appeared in Service Provider and Telco space (see: ATT E-Bonding).

Implementing any IT project requires time, planning, and effort and AIOps probably requires even more planning and stakeholder involvement, because of the breadth of coverage and potential to bring profits to multiple IT domains/functions (ex: ITOps/ITSM/NOCOps). Customers have high expectations from AIOps, but, even after taking such major projects, most AIOps vendors are only able to support a few core AIOps use cases, which severely limits the utility and potential of AIOps.

Lately, I have seen fewer call outs from our field teams to our solution engineering team, and I was wondering what could be the reason? Sometimes, our field engineers approach our solution engineering team with advanced requests for data analysis, running what-if scenarios and assessing the quality of data and what new value can be gleaned by combining related datasets.

RDA enables enterprises to operationalize machine data at scale to drive AI & analytics driven decisions. RDA automates repetitive data integration, preparation and transformation activities using bots that are invoked in “no-code” data workflows or pipelines. RDA helps to move data in and out of AIOps systems thereby simplifying and accelerating AIOps implementations that otherwise would depend numerous manual data integrations and professional services activities.

Gartner predicts that large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. And this prediction is soon turning into a reality. AIOps is showing promising business value as it impacts measurable metrics such as mean time to detect (MTTD), mean time to acknowledge (MTTA), mean time to restore/resolve (MTTR), service Availability, percentage of automated versus manual resolution, and so on.

Medical devices, subway car doors, severe weather warnings, heavy machinery, car alarms, software security alerts. They all notify you to indicate that something is wrong so that you can take action to prevent harm. Hospital monitors can detect a wide range of issues, from an incorrect dose of medication to an irregular heartbeat and beyond. They can quite literally save a life. The same goes for severe weather alerts that warn of impending tornadoes or hurricanes.

First things first. Observability is inherent as a principle to a system and not something that is instilled. Here, we are addressing observability as an open source based solution in the context of insightful monitoring within the ITOM landscape. ITOM is now in the middle of addressing the needs of the expanding and dynamic nature of IT infrastructure as a function. It is no longer about being a monolithic computing stack. It is now beyond monitoring discrete infrastructure elements.

Artificial Intelligence for ITOps (AIOps) can help accelerate incident response with all the incident context, impact assessment, triage data and collaboration & automation tools at one place.

Are your current expensive, traditional or legacy monitoring tool implementations holding you back? It may be time to look into Observability architectures.