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Without exaggeration, digital transformation is moving at breakneck speed, and the verdict is that it will only get faster. More organizations will migrate to the cloud, adopt edge computing and leverage artificial intelligence (AI) for business processes, according to Gartner.
Driving this fast, wild ride is data, which is why for many businesses, data – in its various forms – is one of their most valuable assets. As businesses now have more data than ever before, managing and leveraging it for efficiency has become a major concern. Primary among these concerns is the inadequacy of traditional data management frameworks to manage the increasing complexity of a digital advanced business climate.
Priorities have changed: Customers are no longer satisfied with immobile traditional data centers and are now migrating to powerful, on-demand and multicloud. According to Forrester’s survey of 1,039 international application development and delivery professionals, 60% of technology practitioners and decision makers are using multicloud – a figure expected to rise to 81% in the next 12 months. But perhaps the most important takeaway from the survey is that “90% of black multicloud users say it helps them achieve their business goals.”
Managing the complexity of multicloud data centers
Gartner also reports that enterprise multicloud deployments have become so widespread that until at least 2023, “the 10 largest public cloud vendors will control more than half of the total public cloud market.”
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But it doesn’t stop there – customers are also looking for edge, private or hybrid multicloud data centers that offer full enterprise-wide technology stack visibility and cross-domain correlation of IT infrastructure components. While justified, these features come with a lot of complexity.
Typically, layer upon layer of cross-domain configurations characterize the multicloud environment. However, as newer cloud computing features enter the mainstream, new layers are required – thus complicating an already complex system.
This becomes even more complicated with the rollout of the 5G network and edge data centers to support the growing cloud-based demands of a global post-pandemic climate. Ushering in what many have called “a new wave of data centers,” this reconstruction is creating even greater complexity that is putting tremendous pressure on traditional operating models.
Change is necessary, but considering that the smallest change in one of the infrastructure, security, network or application layers can result in large butterfly effects, enterprise IT teams must realize that they cannot do it alone.
AIops as a solution to complexity in multiple clouds
Andy Thurai, VP and principal analyst at Constellation Research Inc., also confirmed this. For him, the multicloud operations management has led to the increasing complexity of IT operations. His solution? AI for IT operations (AIops), an AI industry category created by 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”, the detection, monitoring and analysis capabilities of AIops allow it to intelligently comb through myriad disparate components in data centers to deliver a holistic transformation of 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. What this means is that businesses that expect to meet the speed and scale demands of increasing customer expectations must turn to AIops. Otherwise, they risk poor data handling and a subsequent drop in business performance.
This need creates a demand for comprehensive and comprehensive operating models for the deployment of AIops – and this is where Cloudfabrix comes in.
AIops as a composable analysis solution
Inspired to help businesses make it easier to adopt a data-first, AI-first and automate-everywhere strategy, Cloudfabrix today announced the availability of its new AIops operating model. It is equipped with person-based composable analytics, data and AI/ML observability pipelines, and workflow capabilities for incident remediation. The announcement comes on the heels of the recent release of what it describes as “the world’s first robotic data automation fabric (RDAF) technology that unites AIops, automation and observability.”
Identified as the key to scaling AI, composable analytics gives companies the ability to organize their IT infrastructure by creating sub-components that can be accessed and delivered to remote machines at will. Embedded in Cloudfabrix’s new AIops operating model is a composable analytics integration with composable dashboards and pipelines.
Offering a 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 published on Forbes that the only way AIops can process all data types to improve quality and gain unique insights is through real-time observability pipelines. This attitude is echoed in Cloudfabrix’s use of not only composable pipelines, but also synthetic pipelines for observability in incident remediation workflows.
In this synthesis, probable fault functions are simulated to monitor the behavior of the pipeline and understand the probable causes and their solutions. Also included in the model’s incident remediation workflow is the recommendation engine, which leverages learned behavior from the operational metastore and NLP analytics to recommend clear remedial actions for priority alerts.
To give a sense of the scope, Cloudfabrix CEO Raju Datla said the launch of its composable analytics is “exclusively focused on the BizDevOps people in mind and transforming their user experience and trust in AI operations.”
He added that the launch also “focuses on automation, by seamlessly integrating AIops workflows into your operating model and building confidence in data automation and observability pipelines by simulating synthetic failures before launching into production.” Some of the operational personas for which this model is designed 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. Although the California-based software company markets itself as a premier data-centric AIops platform provider, it’s not without competition — especially with challengers like IBM’s Watson AIops, Moogsoft, Splunk and others.
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