The Foundation for AIOps Is Data
The more work an organisation does, the more data they generate. In addition to collecting data in their primary ITSM tool, other IT tools also generate data. The combination of data volume and data silos multiplies the challenge of gaining meaningful insights from this data.
Business intelligence (BI) teams and company leadership try to make informed decisions. But how do you make informed decisions when the amount of data is overwhelming and seemingly impenetrable?
Increasingly, companies are turning to AIOps.
What Is AIOps?
AIOps combines big data and machine learning to improve the operations of IT. Specifically, machine learning makes sense of the aggregated data to anticipate, prevent, and resolve incidents. It lets IT both become more proactive and reduce resolution time.
Gartner foresees a significant rise in the use of AIOps.
“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.”– Susan Moore
How Does AIOps Work?
Enterprises generate massive volumes of data in ServiceNow and other platforms and apps. This independently produced data gets sent to a data lake or data warehouse.
An AIOps tool then uses machine learning to act on all this data, cutting through the clutter to identify patterns and causal conditions. The marketplace for AIOps platforms is thriving and includes vendors such as Dynatrace, Splunk, and AppDynamics.
What Can AIOps Do for IT?
By employing the analytics of machine learning, IT operations can better monitor performance and correlate events. Among the “noise” of data, it identifies “signals.”
AIOps can even identify root causes, either using integrated tools to resolve them automatically or sending information about them to IT to resolve quickly. With anomalies detected, slowdowns and outages receive rapid attention. Companies not only improve mean time to resolution (MTTR) – they also reduce the number of incidents that they encounter in the first place.
Through it all, IT operations improve visibility, helping them better understand the status and health of IT within their organisation.
What Does AIOps Rely On? Data
Any AIOps initiative is going to be only as good as the data that it uses. The data is foundational to the work of the AIOps platform, so the success of AIOps is dependent on both historical and current data.
This data can include incidents, system logs, event data, and many other kinds. The beauty of machine learning is that IT does not need to determine what data is relevant. It is the job of machine learning and analytics to identify patterns – to discern the signal from the noise.
IT just needs to ensure the real-time, secure delivery of once-siloed data to an external location so that machine learning and analytics can act on it.
Perspectium DataSync replicates enormous quantities of ServiceNow data to external data storage – all without impacting the performance of ServiceNow.
Running natively within ServiceNow, Perspectium’s end-to-end encryption keeps data secure, while its message queuing prevents data loss. Enterprises such as Accenture and ServiceNow themselves take advantage of Perspectium DataSync’s scalability. They each move millions of ServiceNow records – every day – to external databases.