Articles about AIOps

Modern IT environments are massively distributed, cloud-native, and constantly shifting. But traditional monitoring and AIOps tools rely heavily on fixed rules or siloed models -- they can flag anomalies or correlate alerts, but they don’t understand why something is happening or what to do next.

We spoke to Casey Kindiger, founder and CEO of Grokstream, to discuss new solutions that blend predictive, causal, and generative AI to offer innovative self-healing capabilities to enterprises.

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A recent survey found that only 37 percent of businesses are prepared for AI. This means they risk being left behind as competitors embrace the technology.

We spoke to Richard Tworek, CTO at Riverbed about how organizations can embrace AI and how they can succeed in today's rapidly evolving landscape.

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Artificial intelligence is changing the way that we work with computers and in particular collaborative learning (CL) and conversational intelligence (CI) are set to reshape AI-powered operations.

We talked to Dr. Maitreya Natu, chief data scientist at Digitate, to discover more about what this means both for businesses and for the role of operations professionals.

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Are you ready to unlock the full potential of your enterprise with the transformative power of multi-cloud adoption?

As a cloud architect, you understand the challenges of navigating the vast array of cloud services and moving data and applications to public clouds. But with Multi-Cloud Strategy for Cloud Architects, Second Edition, you'll gain the confidence to tackle these complexities head-on.

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A new study reveals that 97 percent of technology leaders find traditional AIOps models deliver limited value, leaving teams unable to tackle data overload.

The global survey of 1,300 CIOs and technology leaders in large organizations, carried out for Dynatrace, also finds that 88 percent of organizations say the complexity of their technology stack has increased in the past 12 months, and 51 percent say it will continue to increase.

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Businesses are in the early stages of a new era of employee relations as both the workplace and the workforce undergo significant changes. The pandemic accelerated the shift to hybrid work environments, which has, in turn, accelerated the ongoing digital transformations that made hybrid work possible in the first place. Meanwhile, baby boomers are retiring, Millennials are moving into management, and the Gen Z cohorts are just starting their careers.

The digital natives now populating companies have discriminating expectations for how technology works for them. For example, Riverbed’s Global Digital Employee Experience (DEX) Survey found that 68 percent of employees would leave the company if they were unhappy with the DEX. Companies that fail to provide seamless DEX -- which covers the full range of how employees engage with technology at work, from an intranet to email and collaboration platforms to HR systems -- risk frustrating employees when things do not work as expected. This not only increases the chances employees may look for another employer, but a faulty DEX brings losses in productivity and potential damage to a company’s reputation.

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Companies have massively increased their cloud infrastructure investment in the relentless pursuit of innovation. Cloud-native apps, hybrid clouds, microservices, and serverless all enable companies to serve their customers with greater agility -- and at greater scale -- than ever before.

But the rapid adoption of these technologies has also created distributed cloud environments that are immensely difficult to understand and monitor with conventional observability tools.

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When it comes to data analytics, most organizations have historically focused primarily on descriptive and diagnostic capabilities. Descriptive analytics explains what is happening in an IT system and uses analysis levers including analyzing trends, mining patterns, and detecting changes and anomalies. Diagnostic analysis encompasses functions including critical path analysis, bottleneck analysis, fault propagation models, and root-cause analysis to explain why something is happening in the system.

With an increased focus on instrumentation and observability, allied to significant advances in AI, enterprises are now looking beyond simply what happened and why, and seeking to apply advanced intelligence to draw valuable predictive insights from data. IT leaders are looking for insights that can inform them about what is likely to happen in the future and how to prepare for it, for example:

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Organizations in the digital age live and die by their ability to provide customers with 24/7 access to revenue-generating services, including -- but not limited to -- online checkout and reservation portals. If a consumer were to pull back the curtain in front of these daily activities, they would find a dedicated team of site reliability engineers (SREs) and DevOps engineers working in tandem to improve system performance and maintain availability. But why all the fuss about availability?

The answer to that question is hopefully apparent if you are an IT leader. The number of digital buyers in the U.S. has skyrocketed in the past five years. Companies without the digital infrastructure to support this influx of traffic will inevitably lose out to the tune of millions of dollars in revenue per year. Simply put: maintaining availability is mission-critical.

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When constructing large language models (LLMs), developers require immense amounts of training data, often measured in hundreds of terabytes or even petabytes. The challenge lies in obtaining this data without violating copyright laws or using inaccurate information and avoiding potential lawsuits.

Some AI developers have been discovered collecting pirated ebooks, proprietary code, or personal data from online sources without consent. This stems from a competitive push to develop the largest possible models, increasing the likelihood of using copyrighted training data, causing environmental damage, and producing inaccurate results. A more effective approach would be to develop smart language models (SLMs) with a horizontal knowledge base, using ethically-sourced training data and fine-tuning to address specific business challenges.

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Business leaders have relied on monitoring tools since the advent of computing. In an ideal world, these tools help engineers improve their technology’s continuous availability. Modern tools should give teams a real-time understanding of how their digital apps and services are performing, so they can attend to incidents and outages.

There’s another promise of modern monitoring tools. They should free up time for business-motivated  innovation. By providing rapid incident detection, tools should relieve engineers from tedious monitoring activities so they can provide the bigger, better, smarter technologies that make consumers’ lives more convenient and more fun, whilst improving the visibility of what innovation will matter. But are monitoring tools living up to this promise? And are investments paying off?

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Is your business one accidental click away from a major outage? We saw it happen with Atlassian earlier this year. You may already have an incident management strategy and monitoring, but is it adjusted for the ever-changing IT infrastructure and application architectures? Putting appropriate protocols in place ensures that one human code push can't shut down an entire system for three weeks.

Legacy monitoring tools for IT teams were helpful with older, monolithic infrastructures. When we had static infrastructures, finding a direct correlation between the incidents and applications was much easier. Eventually, signals needed even faster processing, but legacy tools couldn’t keep up.

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Technology dominates just about every sphere of modern-day society. If you are like most, you see it in your everyday lives. We increasingly buy online, with U.S. retail e-commerce sales now totaling $768 billion. Likewise, we increasingly work online, with 58 percent of Americans, or 92 million people, now telecommuting at least once a week.

For the most part, online consumers and remote workers take the technology behind their personal and professional activities for granted. We need groceries, so we open a grocery app, fill our virtual carts, check out and -- voilà -- the order is at our door in just a few hours. We apply the same expectations to remote work tools and, well, just about every technology we encounter throughout the day. It should just… work.

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Digital transformation has become ubiquitous throughout every industry, as the world grows more reliant on software-driven services. As this trend continues, customers and end users have increasingly heightened expectations that organizations will deliver better-quality, more efficient, and secure digital services, at greater speed. Multicloud environments, which are built on an average of five different platforms, are at the heart of this transformation. They enhance organizations’ agility, so DevOps teams can accelerate innovation.

However, these Multicloud environments have introduced new challenges given their complexity and scale. Applications span multiple technologies and contain millions of lines of code and generate even more dependencies. It is now beyond human capacity for DevOps teams to manually monitor these environments, piece together and analyze logs to gain the insights they need to deliver seamless digital experiences.

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IT production environments are an essential part of any modern business organization. Today, it’s virtually impossible for an enterprise to function effectively without a defined set of IT solutions. The amount of data managed and needed to run business is growing exponentially, congruent with the amount of data needed to guarantee that these IT environments are always available. These two facts alone create a strong case for the Intelligent Automation (IA) of IT, because data really is the lifeblood of modern business. However, simply generating and managing reams of data is not enough. To derive tangible value from any data, organizations must ensure that the data generated is comprehensive, verifiable, and accurate. Failure here can render data meaningless and lead to poor decision making.

The quality and depth of data can be a game-changer for businesses, and while the human brain is an amazing organ, it can only do so much at once and maintain consistent performance levels. AIOps, the integration of AI in IT operations, on the other hand, leverages the power of machines to enable organizations to accurately comprehend and control the growing complexities of data-driven business ecosystems As more organizations embrace complete operational digital transformation, it’s critical that the data generated is intelligently gathered, organized, analyzed, and optimized. This is where AIOps delivers exponential value through the ability to take data and add context, intelligence and value, driving actionable insights and better-informed decision making. AIOps underpins the drive towards maximized ROI, minimal loss, and delivering complete customer satisfaction.

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