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It’s no longer news that the enterprise landscape is essentially a vast lake of data. While many experts agree with British mathematician, Clive Humby, that data is the new oil, extracting optimal value from data remains a challenge. According to Gartner analyst, Donald Feinberg, several companies often watch their pristine new data lakes turn into murky, muddy data swamps. 

As Humby noted, like oil, data must be refined, broken down and analyzed for it to have value. With more organizations embracing digital transformation and moving to the cloud, building an agile, resilient and accurate artificial intelligence for IT operations (AIops) data pipeline is key to effective decision-making processes.

AIops needs data to function, but challenges along the AIops data pipeline mean that AIops doesn’t often produce the right results. Data observability is one way to solve some of those challenges, but it’s not enough. 

While data observability seeks to understand what happens within systems through their external output and gives critical insight into data health, Shailesh Manjrekar, VP of AI and marketing at CloudFabrix notes in an article published in Forbes that digital-first businesses need more to deliver their expected business outcomes. Manjrekar said observability needs to be augmented by AIops to contextualize, automate, scale and instantly remediate challenges along the AIops data pipeline.

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Cloudfabrix, a California-based software company, says it’s built the world’s first robotic data automation fabric (RDAF) technology to unify observability, AIops and automation — turning noisy IT data into action and prediction.

Defining observability, AIops and automation

Gartner defines AIops as the combination of big data and machine learning (ML) to automate IT operations processes, including event correlation, anomaly detection and causality determination. In Data Done Right for AIops, Andy Thurai, VP and principal analyst at Constellation Research Inc., explains that AIops is simply about using AI and ML to improve IT operations. With enterprises moving to the cloud and many others adopting hybrid models, IT operations have become more sophisticated. The objective of AIops is to uncomplicate an increasingly sophisticated enterprise IT ecosystem.

Raju Datla, founder and CEO at Cloudfabrix, told VentureBeat two major ways enterprises attempt to simplify IT operations are monitoring and observability. While monitoring gives IT teams insights into what happens within IT systems, Datla noted that observability enables them to do more. He said observability enables IT teams not only to see into IT systems, but to know why changes occur in those systems. Observability allows IT teams to understand the underlying reasons behind alterations in things like metrics, events, logs and traces.

The unification of observability and AIops is what leads to hyper-automation — a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many businesses and IT processes as possible, according to Gartner. One thing is central in all these: data. The fulcrum of all conversations surrounding AIops, observability and hyper-automation is data. It’s why Thurai said in a Forbes article that infusing AI into IT operations is more about data than AI itself. 

The need to unify observability and AIops

A recent report published in Molecula showed many AI and ML  projects fail because of data pipeline problems. The report further revealed that 84% of businesses have a major problem with non-optimized data warehouses, high storage costs of data because of too much data, outdated IT infrastructure and finally manual or slow processes that do not meet business needs. Another research by Gartner showed that 53% of AI projects make it from prototype to production.

As Thurai noted, data operations are still a big problem with enterprises. Several challenges including missing information, inconsistent information and biases in datasets are hindering AI and ML  projects from reaching the acme of their capabilities. This is where robotic data automation (RDA) comes in.

RDA is a set of robotic processes used for solving data pipeline issues, simplifying all data handling processes required for the success of AI and ML  projects. Thurai said RDA automates the manual, cumbersome and expensive data pipeline process with bots. 

Unifying observability and AIops will ensure more AI and ML projects are successful and Cloudfabrix says its RDAF technology allows AIops and observability to work in tandem, enabling a “data-driven autonomous enterprise journey” for organizations.

How robotic data automation fabric works

The term “data fabric” has been gaining momentum in the data analytics space in recent times. Gartner listed “data fabric” as one of the top 10 data and analytics trends for 2022, with predictions that “as data becomes increasingly complex and digital business accelerates, data fabric is the architecture that will support composable data, analytics and its various components.”

The research firm further said data fabric reduces the time for integration design by 30%, deployment by 30% and maintenance by 70% because its designs draw on the ability to use/reuse and combine different data integration styles.

Cloudfabrix’s RDAF technology offers a multi-tenant environment to design, explore, test, experiment, collaborate, reiterate, observe and deploy pipelines in production, said Datla.

“RDAF consolidates disparate data sources, converges on the root cause by applying dynamic AI and ML pipelines and concludes by remediating with intelligent automation,” said Manjrekar.

He added that data-driven organizations should explore, evaluate and implement RDAF for faster innovation, faster time to value, meeting SLAs and SLOs and excelling at customer experiences.

With $17 million raised in total funding to date, Cloudfabrix claims its RDAF technology helps organizations “move beyond traditional monitoring into observability pipelines and AIops.” The industry seems to agree on the vision and strategy for RDA, with thought leaders from IBM, Cisco, Google, Microsoft, SAP, T-Systems and others coming together to share their RDAF vision at Cloudfabrix’s Global Big Data Conference this month.

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