In this special guest feature, Will Cappelli, Moogsoft EMEA CTO, provides several trends we’ll see shape the way we use AIOps as 2020 unfolds. Will studied math and philosophy at university, has been involved in the IT industry for over 30 years, and for most of his professional life has focused on both AI and IT operations management technology and practices. As an analyst at Gartner, he is widely credited for having been the first to define the AIOps market and is CTO EMEA and Global VP of Product Strategy at Moogsoft. In his spare time, he dabbles in ancient languages.
AIOps has transitioned from a nice-to-have to a need-to-have for combating the data deluge created by the IT complexity of running digital services and apps. As this trend continues, AI becomes more critical to overcoming the volume, variety and velocity of event data produced by this complexity, and to business viability itself. As 2020 unfolds, here are several trends we’ll see shape the way we use AIOps:
Placing More Trust in AIOps
Until now, IT teams have kept a tight leash on AIOps technology — simply relying on it to enhance the work already being done by humans. In 2020, we’ll see IT practitioners place more trust in AIOps systems to operate independently as we realize our cognitive power is the true barrier to faster incident resolution. This trust will allow the technology to meet tightening timeline requirements for the resolution of customer-impacting issues.
Limiting neural network deployment
Neutral networks make up the AIOps systems we know today, but is this approach sustainable? Unfortunately, neural networks have limits that inhibit widespread deployment across ITOps management environments, such as a lack of actionable insights, deficient diagnostic and predictive abilities, and lengthy training periods. And, while both customers and vendors are investing heavily in neural networks, its application to AIOps will be limited as deployment expands. As the AIOps field moves away from a reliance on neural networks, we’ll see a multidimensional approach to data selection, pattern discovery, logical inferences and communication around these findings, and remediation emerge.
Expanding our ability to define data
Today, we focus on machine learning algorithms as a way to understand data. In short, is the data you have telling you what you’re looking at? But, in 2020, we’ll see two other focuses emerge that will help AIOps technology more deeply and accurately define the data it’s inputting: topological data analysis and game theoretic analysis.
In topological data analysis, we define topological shapes rather than probability distributions like with machine learning algorithms. These topological shapes can model everything from events and maps to spatial relationships — models we find especially difficult with probabilistic models, yet are critical for AIOps innovation. In game theoretic analysis, we look at the strategies that dictate games, allowing us to go from dataset to strategies and structures. Particularly, game theoretic analysis has great potential in applying AI to security use cases.
Broadening the global AIOps market
On top of widespread technical innovation, we’ll also see an evolution in the business of AIOps in 2020. This year will see a boom in the global AIOps market, with new AIOps vendors emerging in Israel, India, the UK, Brazil, China and, of course, Silicon Valley. Already, the EU has sparked conversation as they pledged to make further investments in AI. Across the globe, these types of investments will not only further the use of methods like topological data analysis and game theoretic analysis, but also drive interest in new AIOps applications. Most notably, we’ll see a focus on end-user experience monitoring, which will record and track what interactions are inside and outside a business’s infrastructure to define whether they are human or digital.
With 51% of the enterprises surveyed by the AIOps Exchange reporting a million or more IT events each day — and 11% of those reporting 10 million or more — an increasing majority of businesses are realizing the growing need for AIOps to help manage today’s IT environments. As 2020 progresses, we’ll continue to see fast-paced evolution in the AIOps space driven by a growing need to automate management of all this data, and focus teams instead on rapid innovation.
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