According to Gartner, digital transformation is happening at an incredible speed, and the jury is that it will only go faster. More organizations will migrate to the cloud, adopt edge computing, and leverage artificial intelligence (AI) for business processes.
Data is at the core of this fast-moving journey, and this is why for many businesses, data — in its many 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 these concerns is the inadequacy of traditional data management frameworks to deal with the evolving complexities of a digital-forward business environment.
Customers are no longer satisfied with immobile traditional data centers; they are switching to high-powered, on-demand, and multicloud services. According to Forrester's survey of 1,039 international application development and delivery professionals, 60% of technology practitioners and decision-makers are using multicloud in the next 12 months. But perhaps the most significant finding is that "90% of responding multicloud users believe it's assisting them meet their business goals."
Managing the challenges of multicloud data centers
Enterprise multicloud deployment has become so widespread that until at least 2023, the top 10 public cloud providers will account for more than half of the total public cloud market, according to Gartner.
Customers are also looking for edge, private, or hybrid multicloud data centers that offer full visibility of the enterprise's technology stack and cross-domain correlation of IT infrastructure components, although justified.
The multicloud environment is typically characterized by layers upon layers of cross-domain configurations. However, as newer cloud computing capabilities enter the mainstream, additional layers are required, complicating an already complex system.
With the introduction of the 5G network and edge data centers to support the growing cloud-based demands of a post-pandemic climate, this reconstruction creates even greater challenges that exert enormous pressure on established operational models.
Change is required, but enterprise IT teams must accept that even the slightest change in one of the infrastructure, security, networking, or application layers may result in significant butterfly effects.
AIops as a multicloud solution
Andy Thurai, the vice president and principal analyst at Constellation Research Inc., has also confirmed this. For him, multicloud operations management has resulted in an increasing level of IT operations. His approach is AI for IT operations (AIops), a category created by tech research firm Gartner in 2016.
AIops are able to spot, monitor, and analyze millions of data structures to transform their whole operations, according to Gartner.
The rise in data volumes and the subsequent rise in cloud adoption are expected to contribute to a $644.96 billion global AIops market size by 2030. What this implies is that businesses that want to meet the speed and scale demands of growing customer expectations must resort to AIops. Otherwise, they risk poor data management and a consequent decline in business performance.
This demand for comprehensive and integrated operating strategies for the deployment of AIops arises, and this is where Cloudfabrix comes in.
AIops as a composable analytics solution
Cloudfabrix has announced the availability of its new AIops operating model, which includes persona-based compooable analytics, data and AI/ML observability pipelines, and incident-remediation workflow capabilities. The publication follows the release of "the world-first robotic data automation fabric" that combines AIops, automation, and visibility.
Composable analytics are seen as an essential component of any AI stack, allowing businesses to organize their IT infrastructure by constructing 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.
Cloudfabrix's composable dashboards include field-configurable persona-based dashboards, centralized visibility for platform teams, and KPI dashboards for business-development operations.
Shailesh Manjrekar, Cloudfabrix's vice president of AI and marketing, wrote an article on Forbes that the only way AIops might enhance their quality and extract unique insights is through real-time observability pipelines. This position is reiterated in Cloudfabrix's use of not just composable pipelines, but also observability pipeline synthetics in its incident-remediation processes.
To monitor the pipeline's behavior and identify probable causes and their solutions, various components are simulated. The recommendation engine is also included in the model's incident-remediation workflow, which leverages learned behavior from the operational metastore and NLP analysis to recommend clear remediation actions for prioritized alerts.
Raju Datla, Cloudfabrix' CEO, 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."
Cloudops, bizops, GitOps, finops, devops, DevSecOps, Exec, ITops, and serviceops are some of the operational personas for whom this model has been developed.
Cloudfabrix, a California-based software company founded in 2015, is dedicated to enabling businesses to build autonomous businesses using AI-powered IT solutions. However, the company is not without competitors, particularly with IBM's Watson AIops, Moogsoft, Splunk, and others.