Artificial intelligence for IT operations (AIOps) is becoming a critical tool for organizations attempting to bring automation quickly to network management and IT problem resolution. However, many organizations are still unsure how to deploy AIOps for the most value and to net the full, promised results. Perhaps the challenges come from not looking beyond data silos, or perhaps the problems can be attributed to a lack of understanding AIOps’s full potential in the enterprise.
Enzo Signore, chief marketing officer at FixStream, lends credence to the problem of siloed information. “Despite continued investments in system resources, IT cannot holistically see what’s in their physical and virtual infrastructure,” he said. “And without a clear view of the entire IT environment and the ability to make sense of the mountain of data being served up by various system tools, Ops teams can’t figure out what’s wrong or how to fix it.”
In essence, for AIOps to be successful, IT must be able to access that siloed data and then be able to process that data to derive actionable insights, exactly what AIOps is meant to do. Yet, complexity still seems to be one of the major challenges of bringing a holistic approach to managing networks and resolving problems.
“IT teams are bogged down by the sheer complexity of today’s hybrid, dynamic IT environments,” Signore noted. “Along with steep learning curves and lightning-fast changes to new technology, IT departments must cope with soaring consumer expectations, supporting legacy systems, lack of skilled talent, disparate tool sets and opaque physical and virtual IT infrastructures. These factors complicate IT staff’s ability to oversee operations and optimize system performance.”
Naturally, companies such as FixStream have a major stake in the AIOps game; the company offers numerous AI solutions that promise to modernize IT operations and bring AI into the realm of IT operations. However, understanding what AIOps is all about is one of the first hurdles to overcome when contemplating the technology. Gartner defines AIOps as platforms that enhance IT operations through greater insights by combining big data, machine learning and visualization. “IT leaders should initiate AIOps deployment to refine performance analysis today, and as an augment to IT service management and automation within the next two to five years,” advised Pankaj Prasad, senior principal analyst at Gartner.
Gartner defined some best practices that businesses should adopt, which include:
- Deploy AIOps by adopting an incremental approach that starts with historical data and progress to the use of streaming data, aligned with a continuously improving IT operations maturity.
- Select platforms that enable comprehensive insight into past and present states of IT systems by identifying AIOps platforms that are capable of ingesting and providing access to text and metric data.
- Deepen the IT operations team’s analytical skills by selecting tools that support the ability to incrementally deploy the four phases of IT operations-oriented machine learning—descriptive, diagnostic, proactive capabilities and root cause analysis, to help avoid high-severity outages.
Best practices aside, FixStream’s Signore opined that there are some other challenges that must be addressed. “Without the foundational capabilities of accurate discovery, contextualization and full-stack correlation, AIOps can become severely limited,” he said. “The lack of those features have become the big elephant in the room that everyone is not addressing, and it’s holding back the advancement of AIOps and machine learning for many companies.”
Simply put, when AIOps is not fully deployed, important insights may go unnoticed and leads to data remaining in silos. “Companies are getting all this data, but it’s not being properly processed and correlated, and it’s going to waste,” he said.
It is those types of deployment issues that are lending credence to AIOps not being as effective as promised. “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,” said Bishnu Nayak, FixStream CTO.
Businesses discouraged by the initial ROI of AIOps should take a long, hard look at their implementation strategy and take into consideration what their data sources are and how they are applying analytics, Nayak said. “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.”
