Autonomous business is close, but there are still missing pieces

Building and supporting the AI ​​infrastructure that drives businesses is no easy task. The applications, data and networks behind the scenes need to work as flawlessly as possible and in real time. The good news is that AI itself can be employed to relieve stressed IT teams. L’AIOps (artificial intelligence for IT operations) paves the way for the autonomous operation of business-critical systems. However, AIOps AI has an Achilles heel: to work properly, it needs reliable and quality data.

There is a new approach, robotic data automation (RDA), that promises to establish the intelligent data supply chain necessary for AI to work. Although RDA has the potential to supercharge AI in all its forms and for all its purposes, the early stages are concentrated in the area of ​​computing optimization, with a focus on high-performance AIOps, which which is the next challenge on the road to fully automated computing.

The purpose and potential of RDA was explored in depth during the recent robotic data automation structure and AIOps conferencecovering the issues, opportunities, and technology needed to achieve self-sustaining enterprise.

Smart Data Supply Chain

Everyone wants to go digital, and everyone depends on IT to make that vision a reality.

That’s why now is the time to build an intelligent data supply chain, moving data from raw supply to the final, refined product in the hands of data consumers. RDA paves the way for an intelligent data supply chain, which essentially involves automating data pipelines with “databots”. Data-related tasks that can be automated using RDA include data collection, data integration, data validation, data cleansing, data normalization, metadata enrichment, and data extraction from structured or unstructured data.

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

During the conference, Shailesh Manjrekar, VP of AI and Marketing at CloudFabrix and host of the event, pondered the significance of the advent of AIOps – backed by the data pipeline intelligence that RDA brings – for CIOs and other business leaders. “They’re looking to reduce their risk – and what that means is they need to be able to predict and prevent outages and security breaches. They want to optimize their operations. They want to improve their productivity through automation. They want to build a composable business in the face of uncertainty. They want to be able to enable data governance and compliance. They want to build trust in their AI operations. Finally, they want to be able to deepen their knowledge of customers and the customer experience,” he explained.

Towards autonomy

Shailesh Manjrekar outlines four stages companies go through on the path to digital empowerment:

1. Discovery. “The first level is really a descriptive phase where you take inventory of all your IT, application and business assets,” he says. “It’s about taking inventory. »

2. Predictive autonomy. It’s “where you do ‘what if’ analyzes by looking at these assets, looking at trends and predicting anomalies. »

3. Prescriptive autonomy. “The third level of autonomy is prescriptive, where after your simulation analysis, you can decide what action you are going to take. »

4. Cognitive autonomy. “All that intelligence becomes part of your information systems,” according to Shailesh Manjrekar.

AIOps is important because “the majority of cloud transformation programs don’t achieve the desired results,” said Meenakshi Srinivasan, partner in the Global DevSecOps Practice at IBM Consulting. “The reasons are that they lose control over how they respond to incidents, as well as their inability to minimize unplanned downtime, which costs them a lot of money. » Over the past 20 years, to enter the SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service), IoT (internet of things), infrastructure landscape got complicated. “Complexity has increased, reliability commitments have increased, but manageability has taken a hit. The challenge is to increase manageability. »

“It’s a journey,” commented Meenakshi Srinivasan. “It’s not going to happen overnight just because you put a few tools in place. Once we have the foundations and the automation layer in place, and we start collecting the data. Defining the right datasets, as well as data quality – this plays a major role in AIOps. If you don’t have the right data set, this journey is going to take longer. Observing and learning are important for this trip,” she added.

Deploy AIOps

The challenge facing many businesses and IT managers is that “IT operations have never grown proportionately to the amount of complexity that has been added to it,” says Sean McDermott, CEO of Windward Consulting Group. “So we continually need to be more efficient. The other goal is to use the data to start making better decisions, especially with regard to the allocation of resources, the allocation of time, money and investments, the optimization of the processes of business, business alignment and bottleneck detection. It’s getting harder and harder because we have so much data now. »

Sean McDermott recommended developing a vision around AIOps that recognizes it as an important strategy affecting all IT-related functions. “It’s a strategy,” he says. “It’s not a product, it’s not an algorithm. It is a strategy and it will have a considerable impact on the tools, processes, people and behavior of organizations. One of the pitfalls that we see our customers fall into is that they have a very narrow view of their use case and when they try to move towards automation, they haven’t done working upstream with other peer organizations to integrate their data, and they’re meeting a lot of resistance. Develop a vision of how to deploy AIOps – bringing people together, demonstrating that integrating our data 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 help either bring value to the organization or monetize data. “Companies have spent millions and billions of dollars collecting data. But once you’ve ingested the data, what do you do next? asks Satya Bajpai, managing director of tech M&A for JMP Bank. “Big technology vendors don’t see AIOps as just a data problem. They find that customers need not just intelligence, not just data management, but also actionable data and action. We’re seeing more acquisitions and funding going to companies or use cases where it’s not just AI that detects a problem. If you solve the problem. The AI ​​is smart. We all know that machine learning is useful, but how do you convert it into a tangible benefit for an organization? How much money are you saving? What value are you creating for your customers? »

AIOps – boosted by the intelligent supply chain that RDA enables – will help companies see the value of insights provided by AI, and IT in moving businesses forward on the path to self-reliance.

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Autonomous business is close, but there are still missing pieces

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