Were you unable to participate in Transform 2022? Check out all the Summit sessions in our on-demand library now! Look here.
Without exaggerating, digital transformation is moving at breakneck speed and the verdict is that it will only move faster. More and more organizations will migrate to the cloud, adopt edge computing, and leverage artificial intelligence (AI) for business processes, according to Gartner.
Powering this fast and wild race is data, which is why data, in its various forms, is one of the most valuable assets for many companies. As businesses now have more data than ever, managing and leveraging it for efficiency has become a major concern. Chief among these concerns is the inadequacy of traditional data management frameworks to handle the growing complexities of a digital-oriented business climate.
Priorities have changed: Customers are no longer satisfied with traditional real estate data centers and are now migrating to high-powered, on-demand and multicloud ones. According to Forrester’s survey of 1,039 international application development and delivery professionals, 60% of technology professionals and decision makers are using multicloud, a number that is expected to rise to 81% in the next 12 months. But perhaps the most important thing about the survey is that “90% of multicloud users who respond say it is helping them achieve their business goals.”
Manage the complexities of multicloud data centers
Gartner also reports that enterprise multicloud deployment has become so pervasive that until at least 2023, “the 10 largest public cloud providers will control more than half of the total public cloud market.”
MetaBeat will bring together thought leaders to provide insight into how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, California.
But that’s not where it ends: Customers are also looking for peripheral, private, or hybrid multicloud data centers that offer comprehensive visibility of the technology stack across the enterprise and domain correlation of IT infrastructure components. While justified, these features have great complexity.
Typically, tier upon tier of cross-domain configurations characterize the multicloud environment. However, as new cloud computing capabilities enter the mainstream, new levels are needed, thus complicating an already complex system.
This is made even more complex with the rollout of the 5G network and peripheral data centers to support the growing cloud-based demands of a post-pandemic global climate. Heralding what many have called “a new wave of data centers,” this reconstruction creates even greater complexities that put enormous pressure on traditional operating models.
Change is necessary, but considering that the slightest change in one of the layers of infrastructure, security, network, or application could cause large-scale butterfly effects, corporate IT teams have to contend with the fact that they can’t do it alone.
AIops as a solution to multicloud complexity
This was also confirmed by Andy Thurai, vice president and principal analyst of Constellation Research Inc .. For him, the isolated nature of managing multicloud operations has led to the increasing complexity of IT operations. Its solution? AI for IT operations (AIops), a category of the AI industry coined by the technology research firm Gartner in 2016.
Officially defined by Gartner as “the combination of big data and ML [machine learning] in automating and improving IT operational processes, “AIops’ detection, monitoring and analytics capabilities enable it to intelligently analyze countless disparate data center components to deliver a holistic transformation of its operations.
By 2030, the increase in data volumes and the resulting increase in cloud adoption will have contributed to a projected global AIops market size of $ 644.96 billion. This means that companies that expect to meet the speed and scalability requirements of growing customer expectations must turn to AIops. Otherwise, they run the risk of data mismanagement and consequent decline in business performance.
This need creates a demand for comprehensive and holistic operational models for implementing AIops, and this is where Cloudfabrix comes in.
AIops as a modular analysis solution
Inspired by helping companies facilitate the adoption of a data-driven, artificial intelligence and automated strategy everywhere, Cloudfabrix today announced the availability of its new AIops operating model. It features composable person-based analytics, data observability and AI / ML pipelines, and workflow capabilities for resolving incidents. The announcement comes on the heels of its recent release of what it describes as “the world’s first robotic data automation fabric (RDAF) technology that unifies AIops, automation and observability.”
Identified as the key to AI scalability, composable analytics offers companies the opportunity to organize their IT infrastructure by creating sub-components that can be accessed and delivered to remote machines at will. Featured in Cloudfabrix’s new AIops operating model is composable analytics integration with composable dashboards and pipelines.
Offering a 360-degree view of disparate data sources and types, Cloudfabrix’s composable dashboards include person-based dashboards that can be configured in the field, centralized visibility for platform teams, and KPI dashboards for business development operations.
Shailesh Manjrekar, VP of AI and Marketing at Cloudfabrix, noted in a Forbes article that the only way AIops can process all types of data to improve their quality and gain unique insights is through real-time observability pipelines. . This position is reiterated in Cloudfabrix’s adoption of not only composable pipelines, but also synthetic observability pipelines in its incident correction workflows.
In this summary, probable malfunctions are simulated to monitor the behavior of the pipeline and understand the probable causes and their solutions. Also included in the model’s Incident Correction workflow is the Recommendation Engine, which leverages behavior learned from the operational metastore and NLP analysis to recommend clear remediation actions for prioritized alerts.
To give an idea of the scope, Cloudfabrix CEO Raju Datla said the launch of his composable analytics is “focused solely on BizDevOps people in mind and transforming their user experience and trust in AI operations.” .
He added that the launch “also focuses on automation, seamlessly integrating AIops workflows into the operational model and building confidence in data automation and observability pipelines by simulating synthetic errors prior to production launch.” Some of those operational characters this model was designed for include cloudops, bizops, GitOps, finops, devops, DevSecOps, Exec, ITops, and serviceops.
Founded in 2015, Cloudfabrix specializes in enabling businesses to build autonomous businesses with AI-powered IT solutions. While the California-based software company presents itself as a leading provider of data-centric AIops platforms, it is not without competition, especially with competitors like IBM’s Watson AIops, Moogsoft, Splunk, and others.
VentureBeat’s mission it must be a digital town square for technical decision makers to gain insight into transformative business technology and transactions. Discover our briefings.