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Not All AIOps Solutions are Created Equal

As our market evolves, a number of AIOps solutions have entered the market. It is hard to discern the differences between the offerings. This article helps you understand the core features that you should look for when evaluating these types of solutions. This article was previous published in DZone.

As enterprises accelerate the digital transformation of their business, they’ve increased their dependency on always-on, high-performing business processes. As such, it’s critical that these mission-critical applications perform optimally and are always available to users. To improve system availability and aid troubleshooting, organizations have turned to AIOps (Artificial Intelligence for IT Operations), a technology based on AI and Machine Learning, to automate the identification and remediation of numerous IT issues and automate day to day IT operations activities.

AIOps is helping organizations cope with the challenges of digital business transformation, siloed IT operations and the exponential growth of operational data such as logs, alerts, network faults and performance data generated by the typical digital enterprise. AIOps can use that data to understand dependencies between IT entities across domains, observe the health of critical IT assets, understand business impacts and improve visibility into root cause of system outages and slowdowns.

Harness the Power of Big Data with AIOps

Using big data analytics and machine learning algorithms, AIOps solutions can ingest and aggregate multiple streams of metrics, events and logs to filter through noise and uncover problems. They can provide IT departments with valuable insights into complex cloud-based and virtualized environments, system problems, and business-impacting issues. But the success of the AIOps platform depends on its access to cleanse the disparate data gathered from all across the hybrid IT ecosystem, using various data lineage and relationships. Without the complete set of data and the data relationships, the AIOps system can’t analyze and learn from it, and its success is limited.

Why is this? At its core, AIOps is data-driven, so it requires access to all relevant operations data, including unstructured machine data such as logs, metrics, streaming data, API outputs, and device data, and structured data such as databases. In order to eliminate false positives and accurately identify cause vs impacts, anomalies across related entities, AIOps solutions must utilize relationships across the entities in the machine learning algorithms.   

AIOps technology learns from the input data source to identify trends and patterns to provide an early warning whenever it discovers anomalies or reoccurrence of a known pattern indicating business impacting incident. By correlating and analyzing data from multiple enterprise application and infrastructure domains, the right AIOps solution can reveal trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues. It can also uncover patterns to show what has occurred, a boon to system diagnostics and predictive analytics.

Not all AIOps are created equal

AIOps is a multi-layered platform whose capabilities should include efficient data collection at big data scale, correlation across the collected data, machine learning, analytics and visualization. Most AIOps vendors focus on capabilities of data ingestion, and machine learning for noise reduction and root-cause failure analysis. However, when shopping for an AIOps platform, it helps to remember that AIOps solutions come with varying functionality and ability to manage data sources.                

Cross-Domain Data correlation provides valuable insight

Successful AIOps platforms need to be able to collect data from the entire multi-vendor and multi-domain environment, including network and storage solutions, containers, and public cloud. So, it’s important to select an AIOps platform that can ingest, correlate, and provide access to a broad range of historical and streaming data types. This will enable a broader analysis of trends and issues within the distributed hybrid IT ecosystem and avoid blind spots.

The AIOps solution’s value lies in its ability to ingest and correlate data across the siloed hybrid environment, helping organizations deal with the high volume, variety and velocity of data generated by today’s complex IT operations. Within the data, the AIOps platform can uncover patterns, which can then be used historically to identify the root causes of specific system issues in real time, or proactively to predict potential problems.

Data correlation capabilities have other benefits as well, such as revealing application dependencies and what specific resources each application requires. The AIOps platform may also be able to unite the power of machine learning and big data with domain knowledge to identify a multitude of data relationships and interdependencies. This insight can help IT managers better allocate resources, plan migrations, and purchase only what their cloud-based applications really need.

Enhance digital business operations with AIOps

Today’s modular, dynamic and distributed IT systems require a new multi-perspective approach to fully understand how they’re operating. One approach is to implement AIOps solutions capable of ingesting, correlating, and analyzing data from a number of sources and across IT siloes. This ability to consolidate and analyze system information gives IT teams the ability to more quickly diagnose system issues and resolve proactively before it impacts business

Business applications are the lifeblood of every digital enterprise. With more mission-critical applications based in the cloud, managers need better tools to understand and track how those applications are performing. Done right, an AIOps implementation can lead to a decreased mean time to remediation and better proactive problem solving. By increasing system availability and responsiveness, AIOps can enhance your digital business operations and improve profitability.

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