By Phil Tee, CEO of Moogsoft

It's Time to Democratize AIOps

According to research from Enterprise Management Associates, artificial intelligence for IT Operations, or AIOps, is the most beneficial IT analytics investment, ranking higher than big data stores and customer experience analytics. While AIOps has gained significant traction in large enterprises in recent years, the industry will see the democratization of AIOps as its benefits are extended to organizations of all sizes through 2020 and beyond.

Here are three predictions from the executive team here at Moogsoft, a pioneer and leading provider of AIOps, which all share the common thread of the continued maturity of AI and machine learning technologies for the benefit of ITOps and DevOps.

AIOps Gets Real

2020 will continue the march of Artificial Intelligence and Machine Learning to mainstream acceptance in the greater IT industry. Even larger, typically more conservative enterprises will start to report tangible success with these technologies. As for smaller/midsize organizations, they will witness the weakening of past barriers to entry (e.g. cost, complexity), spurring more widespread AI/ML adoption. As a result of market maturity, proving measurable benefit will become imperative for all AIOps vendor solutions. - Richard Whitehead, Chief Evangelist

Centralized AIOps Platforms

During 2020, deployment of AIOps functionality will migrate to an approach that delivers all algorithmic functions from a logically centralized platform. AIOps can be defined as the sequenced application of algorithms for data selection, pattern discovery, inferencing, communications, and robotics -- applied against varied IT Operations use cases. Up to now, solution delivery has been largely piecemeal and data domain-specific, an extension of existing management technologies and disciplines. But the delivery of AIOps as a logically centralized architecture will come to be seen as a key accelerator, especially as enterprises transition from ITIL3 to ITIL4. - Will Cappelli, CTO, VP Product EMEA

New AIOps Techniques

AI techniques with which we are familiar today -- such as neural networks, event clustering, and regression -- will be joined by less familiar techniques such as topological data analysis (TDA) and generative neural nets. TDA holds promise in commercial applications because data has shape and shape matters. TDA maps the geometric structure of datasets that are large, highly dimensional or noisy to detect patterns and uncover insights. Generative models are trained not only to recognize data but to generate new data just like it. Generative neural nets can learn by recognizing novelty - data that the model has never been trained to recognize. - Phil Tee, CEO

##

About the Author

Phil Tee, CEO

Phil Tee's passion has been IT operational management ever since he co-founded Omnibus Transport Technologies Limited (better known as Micromuse). Having also invented Netcool and built RiverSoft to a successful IPO, Phil now leads the next big revolution in IT event management with Moogsoft, where he maintains a passionate commitment to innovation, including personally leading the company's numerous product functions.