Autonomous businesses closed, but there are still missing pieces

Creating and supporting the AI ​​infrastructure that runs the business is not an easy task. Behind-the-scenes applications, data and networks need to work as seamlessly and in real time as possible. The good news is that AI itself can be employed to alleviate stressful IT teams. AIOps – Artificial Intelligence for IT Operations – paves the way for autonomous operations of critical business systems. However, AIOps AI has an Achilles heel: in order to function properly, it needs reliable and quality data.

There is a new approach, Robotic Data Automation (RDA), which promises to establish the intelligent data supply chain needed for AI to work. While RDA has the potential to supercharge AI in all its forms and purposes, the early stages are focused on computing optimization, focusing on high-performance AIOps, which is the next challenge down the road. To do fully automated computing.

The recent Robotic Data Automation Fabric and AIOps Conference explored the purpose and potential of RDA in depth, covering the issues, opportunities and technologies needed to achieve autonomous initiatives.

Smart data supply chain

Everyone wants to be digital, and everyone relies on IT to make that vision a reality.

That is why now is the time to create an intelligent data supply chain, moving data from raw supplies to data customers in the final, refined product. The RDA paves the way for an intelligent data supply chain, which involves automated data pipelines, including “databots”. Data-related tasks that can be automated using RDA include data collection, data integration, data verification, data cleansing, data normalization, metadata enrichment, and data extraction from structured or unorganized data.

All of this can be automated. The goal is to free IT teams to be bold in their technology initiatives.

During the conference, Shailesh Manjrekar, VP of CloudFabrics and VP of Marketing and host of the event, thought about the significance of the emergence of AIOps – the RDA that is supported by Data Pipeline Smart – for CIOs and other business leaders. “They want to reduce their risks – and that means they need to be able to predict and prevent disruptions and security breaches. They want to optimize their operations. They want to increase their productivity through automation. They want to build a cohesive business in the face of uncertainty.” “They want to be able to enable data governance and compliance. They want to build confidence in their AI operations. Ultimately, they want to be able to deepen their knowledge of customers and customer experience,” he explained.

Towards autonomy

Shailesh Manjrekar describes four steps that companies take towards digital autonomy:

1. Discovery “The first level is really a descriptive stage where you list all your IT, applications and business assets,” he says. “It’s about inventory.”

2. Predictive Autonomy : This is where “if you do” looks at these resources, looks at trends and analyzes inconsistencies. A

3. Prescriptive Autonomy “The third level of autonomy is prescriptive, where after analyzing your simulation, you can decide what steps you are going to take.”

4. Cognitive autonomy According to Shailesh Manjrekar, “all that intelligence becomes part of your information system.”

AIOps are important because “most cloud transformation programs fail to achieve the desired results,” says Meenakshi Srinivasan, Global DevSecops Practice Partner at IBM Consulting. “The reasons are that they lose control over how they react to events, as well as their inability to reduce unplanned downtime, which costs them a lot of money. Over the past 20 years, the infrastructural landscape has become more complex for SaaS, Pass, IoT access. “Complications have increased, promises of reliability have increased, but management has been affected. The challenge is to increase management. A

“It’s a journey,” commented Meenakshi Srinivasan. “It’s not going to happen overnight because you have a few tools in place. Once our foundation and automation layers are fixed and we start collecting data. Defining the right dataset, as well as data quality – it plays a major role in AIOps. Without the right data set, the journey is going to take longer গুরুত্বপূর্ণ observation and learning are important for this journey, ”he added.

Install AIOps

Shawn McDermott, CEO of Windward Consulting Group, said the challenge facing many businesses and IT managers is that “IT activities have never grown consistently with the amount of complexity added to it.” “So we have to be constantly more efficient. Another goal is to start making better decisions using data, especially in asset allocation, time, money and investment allocation, optimization of business processes, business alignment and barrier identification. It’s getting harder and harder because we have so much information right now. A

Sean McDermott recommends developing an approach around AIOps that recognizes it as an important strategy that affects all IT-related functions. “It’s about a strategy,” he said. “It’s not a product, it’s not an algorithm. It’s a strategy and it will have a significant impact on the organization’s equipment, processes, people and behavior. Trying to move towards automation, they did not work upstream with other peer organizations to consolidate their data, and they are facing a lot of resistance. Improves our work and makes the organization more efficient.

From a broader perspective, the market addresses the need for smart data supply chains that can either bring company value or help with data monetization. “Companies have spent millions and billions of dollars collecting data. But once you ingest the data, what do you do next? Satya Bajpayee, managing director of JMP Bank’s Tech M&A, asked “Big tech vendors” don’t see AIOps as just a data issue. They see that customers need not only intelligence, but also data management, functional data and action. We’re moving to more acquisitions and funding agencies or using it in cases where it’s not just AI that detects a problem. If you solve the problem. AI smart. We all know that machine learning is useful, but how do you turn it into a real benefit for an organization? How much money are you saving? What values ​​are you creating for your customers? A

AIOps – driven by intelligent supply chains that enable RDA – will help companies see the value of the insights provided by AI, as well as help IT move their businesses towards autonomous ventures.


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