Self-employed business is close, but parts are still missing

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

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

The purpose and potential of RDA was explored in depth at the recent Robotic Data Automation Fabric and AIOps conference, covering the issues, opportunities and technology needed to achieve an autonomous 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 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, vice president of AI and marketing at CloudFabrix and host of the event, pondered the importance of the advent of AIOps – backed by the data pipeline intelligence that RDA brings – to CIOs and other business leaders. “They are looking to reduce their risk – and that means they need to be able to anticipate and prevent disruptions and security breaches. They want to optimize their operations. They want to improve their productivity through automation. They want to build a combinable business in the face of uncertainty. They want to enable data governance and compliance. They want to build trust in their AI operations. Ultimately, they want to be able to deepen their knowledge of customers and the customer experience,” he explained.

Towards autonomy

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

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

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

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

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

AIOps is important because “most cloud transformation programs don’t achieve the desired results,” said Meenakshi Srinivasan, partner in IBM Consulting’s Global DevSecOps Practice. “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. » In the last 20 years, entering the SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service), IoT (Internet of Things) infrastructure scenario has become complicated. “Complexity has increased, reliability commitments have increased, but manageability has suffered. The challenge is to increase manageability. »

“It’s a journey,” commented Meenakshi Srinivasan. “It won’t happen overnight just because you put some tools in place. Once we have the foundations and the automation layer, we start collecting the data. Defining the right datasets as well as data quality – this plays an important role in AIOps. If you don’t have the right dataset, this journey will take longer. Observing and learning are important to this trip,” he added.

Deploy AIOps

The challenge many companies and IT managers face is that “IT operations never grew in proportion to the amount of complexity that was added to them,” says Sean McDermott, CEO of Windward Consulting Group. “That’s why we need to be continually more efficient. The other objective is to use the data to start making better decisions, particularly when it comes to resource allocation, allocating time, money, and investments, optimizing business processes, business alignment, and detecting bottlenecks. It’s getting harder and harder because we have a lot of data now. »

Sean McDermott recommended developing a vision for AIOps that recognizes it as an important strategy that affects 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 will have a considerable impact on the tools, processes, people and behavior of organizations. One of the pitfalls we see our customers fall into is that they have a very narrow view of their use case, and when they try to move to automation, they haven’t worked upstream with other peer organizations to integrate their data. are 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 meets the need for intelligent data supply chains that can help add value to the organization or monetize data. “Companies have spent millions and billions of dollars collecting data. But after ingesting the data, what do you do next? asks Satya Bajpai, managing director of technology mergers and acquisitions at JMP Bank. “Big technology vendors don’t just see AIOps as a data issue. They find that customers need not just intelligence, not just data management, but actionable data and actions. 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. 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 – powered by the intelligent supply chain that RDA enables – will help companies see the value of the insights provided by AI and IT in advancing companies on the path to self-sufficiency.


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