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Without exaggeration, the digital transformation is happening at breakneck speed, and the verdict is that it will only accelerate. According to Gartner, more organizations will migrate to the cloud, adopt edge computing and use artificial intelligence (AI) for business processes.
Powering this fast, wild ride is data, and here’s why for many enterprises data – in its various forms – is one of its most valuable assets. As businesses now have more data than ever before, managing and using it for efficiency has become a top priority. Chief among those concerns is the inadequacy of traditional data management frameworks to handle the increasing complexity of a digitally-advanced business environment.
The priorities have changed: customers are no longer satisfied with immobile traditional data centers and are now migrating to high-performance, on-demand and multi-cloud data centers. According to Forrester’s survey of the 1,039 international application development and delivery professionals, 60% of technology practitioners and decision makers use multicloud – a number expected to rise to 81% in the next 12 months. But perhaps most important of the research, “90% of responsive multicloud users say it helps them achieve their business goals.”
Managing the complexity of multicloud data centers
Gartner also reports that enterprise multicloud deployment has become so widespread that until at least 2023, “the 10 largest public cloud providers will occupy 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 provide complete visibility into the enterprise-wide technology stack and cross-domain correlation of IT infrastructure components. Although justified, these functionalities entail great complexities.
Typically, layers upon layers of cross-domain configurations characterize the multicloud environment. However, as newer cloud computing functionalities enter the mainstream, new layers are required, complicating an already complex system.
This is further complicated with the rollout of the 5G network and edge data centers to support the increasing cloud-based demands of a global post-pandemic climate. This reconstruction is leading to what many call “a new wave of data centers” and creating even greater complexities that put enormous pressure on traditional operating models.
Change is necessary, but since the slightest change to any of the infrastructure, security, network or application layers can lead to large-scale butterfly effects, enterprise IT teams must come to terms with the fact that they cannot do it alone.
AIops as a solution for multicloud complexity
Andy Thurai, VP and principal analyst at Constellation Research Inc., also confirmed this one. For him, the silo nature of multicloud operations management has resulted in the increasing complexity of IT operations. His solution? AI for IT operations (AIops), a category of the AI industry created by a technical research firm Gartner in 2016.
Officially defined by Gartner as “the combination of big data and ML” [machine learning] in the automation and improvement of IT operations processes,” AIops’ detection, monitoring, and analytics capabilities enable it to intelligently comb through myriad disparate components of data centers to provide 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 $644.96 Billion Global AIops Market Size. This means that enterprises expecting to meet the speed and scale requirements of growing customer expectations must turn to AIops. Otherwise, they run the risk of poor data management and a consequent drop in business performance.
This need creates a demand for comprehensive and holistic business models for deploying AIops – and it’s true Cloudfabrix comes in.
AIops as a composable analytics solution
Inspired to help enterprises ease their adoption of a data-first, AI-first and automate-everywhere strategy, Cloudfabrix today announced the availability of its new AIops operating system. It is equipped with persona-based composable analytics, data and AI/ML observation pipelines and workflow capabilities for incident resolution. The announcement follows the recent release of what it describes as “the world’s first robotic data automation fabric (RDAF) technology that unites AIops, automation, and observability.”
Composable analytics is identified as the key to scaling AI, empowering enterprises to organize their IT infrastructure by creating sub-components that can be accessed and delivered to remote machines at will. Cloudfabrix’s new AIops control model includes composable analytics integration with composable dashboards and pipelines.
Cloudfabrix’s configurable dashboards provide a 360-degree visualization of disparate data sources and types and include field-configurable persona-based dashboards, centralized visibility for platform teams, and KPI dashboards for business development activities.
Shailesh Manjrekar, VP of AI and Marketing at Cloudfabrix, commented in a article published on Forbes that the only way AIops can process all data types to improve their quality and gain unique insights is through real-time observation pipelines. This view is echoed in Cloudfabrix’s adoption of not only composable pipelines, but also observable pipeline synthetic components in its incident resolution workflows.
In this synthesis, probable failures are simulated in order to monitor the behavior of the pipeline and understand the possible causes and their solutions. Also included in the model’s incident resolution workflow is the recommendation engine, which uses learned behaviors from the operational metastore and NLP analytics to recommend clear remediation actions for prioritized alerts.
To give an idea of its scope, Cloudfabrix CEO Raju Datla said the launch of its composable analytics “will focus solely on the BizDevOps personas in mind and transforming their user experience and trust in AI capabilities.” operations.”
He added that the launch also “focuses on automation, by seamlessly integrating AIops workflows into your business model and building trust in data automation and observability pipelines by simulating synthetic errors before going into production.” Some of the operations people this model is designed for are cloudops, bizops, GitOps, finops, devops, DevSecOps, Exec, ITops, and serviceops.
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 a premier data-centric AIops platform provider, it’s not without competition — especially with contenders like IBM’s Watson AIops, Moogsoft, Splunk, and others.
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