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Without exaggeration, digital transformation is happening at an incredible pace and judgment it will only move faster. More organizations will move to the cloud, adopt edge computing and use artificial intelligence (AI) for business processes. Gartner.
Powering this fast, wild ride is data, and so for many businesses, data — in various forms — is one of its most valuable assets. As businesses now have more data than ever before, managing it and using it for efficiency has become a major challenge. Foremost among these concerns is the inadequacy of traditional information management frameworks to manage the growing complexities of the digital business environment.
Priorities have changed: Customers are no longer satisfied with static traditional data centers and are now moving to high-powered, on-demand and multi-cloud centers. according to Forrester’s survey 1,039 international software development and delivery professionals, technology practitioners and 60% of decision makers are using multicloud – a figure expected to rise to 81% in the next 12 months. But perhaps most important from the survey is that “90% of responding multicloud users say it helps them achieve their business goals.”
Managing the complexities of multi-cloud data centers
Gartner also reports that enterprise multi-cloud adoption is so widespread that “the 10 largest public cloud providers will control more than half of the total public cloud market” by at least 2023.
It doesn’t stop there – customers are also looking for offsite, private or hybrid multicloud data centers that offer full visibility of the enterprise-wide technology stack and cross-domain correlation of IT infrastructure components. Although basic, these functions come with great complexities.
Typically, layers upon layers of cross-domain configurations characterize a multicloud environment. However, as newer cloud computing functions enter the mainstream, new layers are required – thus complicating an already complex system.
This is further complicated by the rollout of 5G networks and edge data centers to support the growing cloud-based demands of the global post-pandemic climate. According to many, “the new wave data centers,” this restructuring creates greater complexities that put enormous pressure on traditional operating models.
Change is necessary, but enterprise IT teams must accept that they cannot do it alone, given that the smallest change at one of the infrastructure, security, network, or application layers can result in large-scale butterfly effects.
AIops as a solution to multi-cloud complexity
Andy Thurai, VP and Principal Analyst, Constellation Research Inc confirmed this. For him, the secretive nature of managing multiple cloud operations has resulted in increased complexity of IT operations. His solution? AI for IT operations (AIoops), an AI industry category created by a technology research firm Gartner in 2016.
It is officially defined by Gartner as “the fusion of big data and ML”. [machine learning] In automating and improving IT operational processes,” AIops’ detection, monitoring and analytics capabilities enable it to intelligently scan the myriad disparate components of data centers to enable a holistic transformation of their operations.
Data volume will increase by 2030 and cloud adoption will increase as a result is predicted The global AIops market size is $644.96 billion. What this means is that businesses looking to meet the speed and scale demands of rising customer expectations should turn to AIops. Otherwise, they risk poverty data management and resulting decline in business performance.
This need calls for comprehensive and unified operating models for deploying AIops – and that’s it. Cloudfabrix enters.
AIops as a pluggable analytics solution
Inspired to help enterprises facilitate the adoption of a data-first, AI-first, and ubiquitous automation strategy, Cloudfabrix today announced the availability of its new AIops operating model. It is powered by personality-based composable analytics, data and AI/ML observation pipelines, and incident remediation workflow capabilities. The announcement follows the recent launch of what it describes as a “world first”. robotic data automation piece AIops, combining automation and surveillance capability (RDAF) technology.
Defined as the basis for scaling AI, comprable analytics enabling enterprises 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 dashboards and pipelined analytics integration.
Offering 360-degree visualization of different data sources and types, Cloudfabrix’s composable dashboards feature field-configurable persona-based dashboards, centralized visibility for platform teams, and KPI dashboards for business development operations.
Shailesh Manjrekar, VP of AI and Marketing at Cloudfabrix, noted in an article article It was published in Forbes that real-time observation pipelines are the only way AIops can process all types of data to improve quality and gain unique insights. This position is echoed in Cloudfabrix’s adoption of not only patchable pipelines, but also observable pipeline synthetics in incident remediation workflows.
In this synthesis, possible failures are simulated to monitor the behavior of the pipeline and to understand the possible causes and their solutions. The model’s incident remediation workflow also includes a recommendation engine that uses behaviors learned from the operational metastore. NLP analysis recommend clear recovery actions for prioritized alerts.
To give an idea of the scope, Cloudfabrix CEO Raju Datla said the launch of its composable analytics “focuses solely on BizDevOps personas and transforms their user experience and trust in AI operations.”
He added that the launch also “focuses on automation by building confidence in data automation and observable pipelines by seamlessly integrating AIops workflows into your operational model and simulating synthetic errors before going into production.” Some of the operatives for which this model is designed include cloudsbizops, GitOps, finopsdevops, DevSecOpsexecutive, Itops and services.
Founded in 2015, Cloudfabrix specializes in enabling companies to build autonomous enterprises with AI-powered IT solutions. While the California-based software company markets itself as the leading data-centric AIops platform vendor, it’s not without competition — especially with rivals like IBM Watson AIops, Moogsoft, Whimsical and others.
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