The DevOps to AIOps journey and its future
The demand for DevOps continues to grow. As IT infrastructures become more complex, the resources to efficiently manage them grow increasingly important. What’s the next stop on its journey? The next evolution for addressing IT challenges is AIOps—the application of machine learning (ML) and data science to solve IT operational problems.
Selling products and services online continues to be an essential strategy for businesses of all sizes. Beginning in the mid-1990s with the launch of Amazon, now eBay, and Alibaba, e-commerce sites across the globe have been the fastest growing business segment.
Technology has increasingly provided consumers with easy access to these websites and the ability to purchase virtually anything from the comfort of their home, a computer, smartphone, or mobile device. Consumers can usually find the items they want on multiple websites and can evaluate them based on quality, price, and peer reviews.
For enterprises, this means a brick-and-mortar store is no longer required; customers across the globe can purchase items online 24/7. But implementing technology that supports an on-premises or a hybrid, public, or private cloud IT environment that drives e-commerce activities is challenging. The infrastructure must be agile and adaptable to enable a rapid response when inevitable performance problems arise. Failure to do so can be costly: unplanned application downtime costs Fortune 1000 companies up to $2.5 billion each year, according to research from IDC.
IT operations and software development join forces
The solutions for addressing IT performance challenges can be found in DevOps, defined as the merger of IT operations and software development. DevOps focuses on speeding up product delivery to better serve customer requirements; among the many responsibilities DevOps teams have is ensuring websites are updated frequently with efficient code.
Increasingly, IT teams turn to DevOps to find the root cause of performance problems. Resolutions can take hours, costing large enterprises an estimated $5,600 per minute in lost revenue and opportunities, not to mention the damage to an organization’s reputation. It also varies by industry and organization; the loss of revenue for commerce giant Amazon, for example, is cited as an astronomical $220,000 per minute.
AIOps: The next evolution
The next evolution for addressing IT challenges is AIOps—the application of machine learning (ML) and data science to solve IT operational problems. Implementing AIOps with DevOps decreases the mean time to resolve (MTTR) issues and impacts other key resolution metrics by analyzing log events and alerts, pinpointing the root cause of IT issues faster, and performing resolution automation that reduces the need for manual intervention.
As IT infrastructures become more complex, the resources to efficiently manage them grow increasingly important. More than 80 percent of enterprises today have a hybrid cloud plan in place. Hybrid cloud environments are cost-effective and can keep legacy data private, along with many other benefits. But businesses must also be prepared to address maintenance complexities and troubleshoot issues to ensure an optimal customer experience.
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Working hand-in-hand, AIOps helps DevOps teams manage hybrid clouds as well as on-premises, public, and private cloud environments by delivering real-time, end-to-end performance baselining, automated root cause analysis, anomaly detections, and predictive insights.
The convergence of DevOps with AIOps is already proving to be transformative for business. AIOps is changing the software development process, helping enterprises continue to deliver the e-commerce experiences their customers expect and introducing new services to attract the next generation of customers.