All posts by michaelallen

How to prevent AIOps from becoming just another cog in the machine

Over the past few years, companies have been rapidly transitioning to dynamic, hybrid cloud environments in a bid to keep up with the constant demand to deliver something new. However, while the cloud provides the agility businesses crave, the ever-changing nature of these environments has generated unprecedented levels of complexity for IT teams to tackle.

Traditional performance management strategies have been stretched to breaking point, as IT struggles to piece together often conflicting insights from countless monitoring tools and dashboards.

These tools collect a multitude of metrics and raise alerts when problems arise, but provide very few answers as to what actually went wrong. They’re firing out thousands of alerts every day, creating a data storm that makes it easy for problems to be missed or worsen as IT works to identify which are urgent, which are duplicates, and which are false alarms – and that’s before they’ve even gotten onto the task of understanding and resolving the underlying issue.

Hope on the horizon

Over the last two years, IT teams have identified hope on the horizon in the form of the emerging market of AIOps tools. This new breed of solution uses artificial intelligence to analyse and triage monitoring data faster than humans ever could, helping IT teams to make sense of the endless barrage of alerts by eliminating false positives and identifying which problems need to be prioritised.

The global AIOps platform market is expected to grow from $2.5bn in 2018 to $11bn by 2023, and Gartner predicts that 25% of enterprises will have an AIOps platform supporting two or more major IT operations by the end of the year. This demonstrates that there’s a substantial appetite for AIOps capabilities. However, AIOps is not a silver bullet, and there’s a risk that enterprises will fail to realise its potential if it simply becomes just another cog in the machine alongside the array of monitoring tools they already rely on.

Part of the wider solution

AIOps tools are only as good as the data they’re fed, and to radically change the game in operations, they need the ability to provide precise root case determination, rather than just surfacing up alerts that need looking into. It’s therefore critical for AIOps to have a holistic view of the IT environment so it can pull in any pertinent data and contextualise alerts using performance metrics from the entire IT stack. Integration with other monitoring capabilities is therefore key when adopting AIOps, ensuring there are no gaps in visibility and issues can be understood and resolved faster.

While IT teams would almost certainly see a reduction in alert noise when taking the ‘bolt-on’ approach to AIOps, other tools would still be required to drill down and identify the solution to a problem, taking time and manual effort. For AIOps to truly deliver on its promise and make life easier for IT teams, it needs to be part of a holistic approach to performance management.

Taking this more integrated approach will enable IT teams to not only automatically find and triage issues, but create true software intelligence that can surface answers to those problems in real-time.

Looking to the autonomous future

It’s this potential for simplifying IT operations and delivering a more efficient organisation that should be the end goal of AIOps. When done right, the software intelligence enabled by AIOps can be used to drive true efficiencies, through automated business processes, auto-remediation and self-healing.

Ultimately, this can enable the transition towards autonomous cloud operations, where hybrid cloud environments can dynamically adapt in real-time to optimise performance for end-users, without the need for human intervention. As a result, problems can be resolved before users even realise there’s been a glitch.

This AI-driven automation will fuel the next wave of digitalisation and truly transform IT operations. However, reaching this nirvana can’t be achieved by cobbling together a mixed bag of monitoring tools and an AIOps solution into a Frankenstein’s monster for IT. Companies need a new, holistic approach to performance management that combines application insights and cloud infrastructure visibility with digital experience management and AIOps capabilities.

Taking this approach will help to deliver the true promise of AIOps, providing IT with answers as opposed to just more data. As a result, IT teams will be freed up to invest more time in innovation projects that set the business apart from competitors, instead of focussing their efforts on keeping the lights on.

Read more: How to solve visibility issues from AIOps: A guide in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

How AI can help IT teams see through the clouds of complexity

Businesses understand the importance of providing seamless customer journeys, but we’ve seen a growing spate of digital service outages and software performance problems in recent months. There have been online banking outages that have left customers unable to pay bills on time, while problems with major payment systems have left shoppers unable to use their bank cards at the checkouts.

These problems seriously disrupt peoples’ ability to live their day-to-day lives, so they’re becoming a growing concern for businesses and consumers alike. So, if businesses understand the importance of providing seamless customer journeys, why are outages happening more often?

The complexity conundrum

The soaring complexity of technology ecosystems is the biggest contributor to the rise in software performance problems. Modern digital services reside in complex hybrid multi-cloud environments, spanning multiple platforms and technologies. They’re powered by applications running in dynamic microservices and containers, creating constant change. A single web or mobile transaction now crosses an average of 35 different technology systems or components, compared to 22 just five years ago. With digital transactions crossing such a diversity of components in a dynamic technology stack, it’s gone beyond human capability to manage performance effectively. They struggle to maintain visibility into everything that’s happening in their environment, and to find the root cause of any performance problems that arise quickly.

Unfortunately, this trend is showing no signs of slowing. Digital ecosystems are becoming even more complex, and IT teams are under more pressure than ever to quickly identify and resolve the root cause of any problems before customers feel any impact. If they fail to do so, the spate of digital performance problems and service outages that we’ve seen recently will only occur more often.

Taking the guesswork out of performance

There’s a number of reasons why it’s become impossible for businesses to manage the complexity of their digital ecosystems manually. Firstly, new technologies, infrastructure and platforms are constantly being layered onto IT stacks, requiring more monitoring tools to provide visibility and enable IT teams to manage performance. However, the digital ecosystems that have arisen around these IT stacks are highly dynamic. Whilst this creates the agility that businesses need to thrive, it also makes it impossible for humans to stay on top of performance using traditional monitoring tools.

On top of this, traditional monitoring tools are bombarding teams with alerts, most of which are just white noise. Given that it’s impossible for humans to overcome this challenge manually, organisations need to be able to automate as many IT operations processes as possible. They need the ability to automatically detect issues in real-time and, most importantly, use AI to pinpoint the root cause with precision. These capabilities can help organisations onto the path of auto-remediation, so their monitoring system can detect problems and apply fixes to prevent or resolve the issue before it escalates into a full-blown outage.

No turning back

While moving to the cloud has made businesses far more agile, it’s added exponential complexity to their digital ecosystems. This has had a huge impact on organisations’ ability to successfully monitor performance and rectify any issues quickly and efficiently. AI is crucial to combatting the problem. It can make the process of detecting and rectifying software performance problems much faster and more effective. Ultimately, this will enable IT teams to provide more consistent and positive user experiences, relegating the nightmare of major outages to the past. in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.