From ITOA to AIOps: 3 Key Differences You Should Know
4 min read
If you think you don’t need Artificial Intelligence for IT Operations (AIOps) because you invested in a lot of IT Operations Analytics tools (ITOA), think again. AIOps and ITOA are not the same.
Let me first explain what AIOps and ITOA is before we dive into the differences.
What is ITOA and AIOps?
Let’s start with ITOA.
ITOA is an approach to IT operations data that allows you to understand and make decisions about your IT landscape. ITOA uses big data across the IT environment so you have a broader context about what's happening. By analyzing all your IT data, you can see the bigger picture and make faster and better decisions. This ITOA approach worked well, for a time, but the analytics techniques that were used tended to be fairly static and did not deal gracefully with dynamic and elastic infrastructure such as cloud and containers.
Alright. Let’s dive into AIOps.
AIOps is Gartner’s next step evolution of ITOA. AIOps platforms are systems that combine big data with machine learning or artificial intelligence functionality to accelerate a broad range of IT Operations siloes including monitoring (observe), service desk (engage) and automation (act).
With AIOps platforms, enterprises will be able to centralize their data in one place and make sense of it immediately. You’ll be able to understand how business services, applications and all underlying infrastructure are related to each other at any moment in time. This information will make IT teams solve problems faster and become more proactive in highly-dynamic environments.
What Are The Differences?
Here are the top three differences you need to know when looking for an AIOps or ITOA solution.
1. Historical Analytics vs. Real-time and Predictive Analytics
Analytical functionality provided by ITOA tools are mostly static and focussed on monitoring data collection to analyse events and problems that occurred in the past.
AIOps platforms applies dynamic and real-time artificial intelligence for highly-dynamic and elastic environments. It delivers proactive insights into problems and recommends as well as automated remedial actions.
2. Data ingestion
ITOA tools are able to blend and correlate multiple data sources. However, most of the ITOA solutions still require customers to slice and dice outcomes of blended analysis to interpret them or present these outcomes in a complex, specialized manner.
An AIOps platform collects data of all types from cloud to on-prem and from legacy to microservices. That may include data from metrics, logs, traces, changes, alerts and tickets. The ability to ingest data from the most diverse data sources is critical. It allows for a precise picture of all the moving parts across (hybrid) IT environments.
3. Static IT vs. Dynamic IT
Today’s infrastructure span multiple environments, and are more dynamic, distributed and modular in nature. Technology teams not only need access to this data, but they also need insight into the web of ever-changing relationships and interdependencies that exist across this data to be able to draw out meaningful conclusion.
ITOA tools couldn’t keep up with the amount of changes that are happening across the IT organization. AIOps solutions are built and designed to tame the complexity of todays architectures.
As you may noticed, AIOps is essentially the evolution of technologies that were previously categorized as IT Operations Analytics. AIOps is attempting to solve the problems which ITOA could not.
Interested and would you like to learn more about AIOps? Download our newest Guide to AIOps to get a better understanding of the market and what to expect from today’s AIOps solutions.
4 min read