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Without exaggeration, digital transformation is moving at breakneck speed, and the verdict is that it will only move faster. More organizations will migrate to the cloud, adopt edge computing and leverage artificial intelligence (AI) for business processes, according to Gartner.
Fueling this fast, wild ride is data, and this is 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 leveraging it for efficiency has become a top concern. Primary among those concerns is the inadequacy of traditional data management frameworks to handle the increasing complexities of a digital-forward business climate.
The priorities have changed: Customers are no longer satisfied with immobile traditional 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 practitioners and decision-makers are using multicloud — a number 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 that it’s helping them achieve their business goals.”
Managing the complexities of multicloud data centers
Gartner also reports that enterprise multicloud deployment has become so pervasive that until at least 2023, “the 10 biggest public cloud providers will command more than half of the total public cloud market.”
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But that’s not where it ends — customers are also on the hunt for edge, private or hybrid multicloud data centers that offer full visibility of enterprise-wide technology stack and cross-domain correlation of IT infrastructure components. While justified, these functionalities come with great complexities.
Typically, layers upon layers of cross-domain configurations characterize the multicloud environment. However, as newer cloud computing functionalities enter into the mainstream, new layers are required — thus complicating an already-complex system.
This is made even more intricate with the rollout of the 5G network and edge data centers to support the increasing cloud-based demands of a global post-pandemic climate. Ushering in what many have called “a new wave of data centers,” this reconstruction creates even greater complexities that place enormous pressure on traditional operational models.
Change is necessary, but considering that the slightest change in one of the infrastructure, security, networking or application layers could result in large-scale butterfly effects, enterprise IT teams must come to terms with the fact that they cannot do it alone.
AIops as a solution to multicloud complexity
Andy Thurai, VP and principal analyst at Constellation Research Inc., also confirmed this. For him, the siloed nature of multicloud operations management has resulted in the increasing complexity of IT operations. His solution? AI for IT operations (AIops), an AI industry category coined by tech 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 operation processes,” the detection, monitoring and analytic capabilities of AIops allow it to intelligently comb through countless disparate components of data centers to provide a holistic transformation of its operations.
By 2030, the rise in data volumes and its resulting increase in cloud adoption will have contributed to a projected $644.96 billion global AIops market size. What this means is that enterprises that expect to meet the speed and scale requirements of growing customer expectations must resort to AIops. Else, they run the risk of poor data management and a consequent fall in business performance.
This need creates a demand for comprehensive and holistic operating models for the deployment of AIops — and that is where 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 model. It is equipped with persona-based composable analytics, data and AI/ML observability pipelines and incident-remediation workflow capabilities. The announcement comes on the heels of its recent release of what it describes as “the world-first robotic data automation fabric (RDAF) technology that unifies AIops, automation and observability.”
Identified as key to scaling AI, composable analytics give enterprises the opportunity to organize their IT infrastructure by creating subcomponents that can be accessed and delivered to remote machines at will. Featured in Cloudfabrix’s new AIops operating model is a composable analytics integration with composable dashboards and pipelines.
Offering a 360-degree visualization of disparate 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 could process all data types to improve their quality and glean unique insights is through real-time observability pipelines. This stance is reiterated in Cloudfabrix’s adoption of not just composable pipelines, but also observability pipeline synthetics in its incident-remediation workflows.
In this synthesis, likely malfunctions are simulated to monitor the behavior of the pipeline and understand the probable causes and their solutions. Also included in the incident-remediation workflow of the model is the recommendation engine, which leverages learned behavior from the operational metastore and NLP analysis to recommend clear remediation actions for prioritized alerts.
To give a sense of the scope, Cloudfabrix’s CEO, Raju Datla, said the launch of its composable analytics is “solely focused on the BizDevOps personas 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 in your operating model and building trust in data automation and observability pipelines through simulating synthetic errors before launching in production.” Some of those operational personas for whom this model has been designed include cloudops, bizops, GitOps, finops, devops, DevSecOps, Exec, ITops and serviceops.
Founded in 2015, Cloudfabrix specializes in enabling businesses to build autonomous enterprises with AI-powered IT solutions. Although the California-based software company markets itself as a foremost data-centric AIops platform vendor, it’s not without competition — especially with contenders like IBM’s Watson AIops, Moogsoft, Splunk and others.
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Author: Kolawole Samuel Adebayo