How to Use AIOps to Improve Your Application Performance

Monitoring and managing the performance of your applications is a vital but often undervalued part of any IT professional's role. Being able to spot anomalies quickly, address any problems that might lead to downtime, and optimize systems to ensure they are performing to their full potential are all essential if firms are to be productive.

But this task is getting harder all the time. As businesses' IT environments grow and the number of applications and amount of data available expands exponentially, maintaining control of the resulting sprawl can pose many headaches to IT administrators.

However, there is an emerging technology that looks to tackle these issues, known as AIOps. This is set for a huge rise in popularity over the coming years as more businesses recognize its potential. Indeed, Gartner predicts that by 2022, four out of ten organizations will have implemented such a solution. But what is AIOps, and how can you use it to transform your application performance?

What is AIOps?

AIOps is a combination of artificial intelligence and IT operations, and refers to a series of tools that help businesses monitor and manage their systems much more effectively through automation and machine learning.

According to Gartner, who first coined the term, it aims to support activities including data center monitoring, service desks, and automation processes. It utilizes big data, machine learning and other automation technologies to bring together service management and performance management, and better support IT staff by pinpointing issues much more quickly and accurately than human staff could manage on their own.

The promise of AIOps

It should be clear to see that AIOps has a lot of promise. So, where exactly does the future of this innovative solution lie? There are plenty of possibilities, although some are more practical than others. For example, Gartner believes AI could already be used to reduce delays caused by technical issues.

The firm pointed to two of Australia’s largest supermarket chains, both of which had to close stores due to country-wide technical issues. Because of the large amounts of data IT teams have to deal with, it can take a long time to resolve these types of delays. However, Gartner believes AIOps could be used to cut down on this time.

This is a fairly simple and practical use for the technology. However, some organizations believe AIOps has an even brighter future ahead of it. ScienceLogic - creator of the SL1 AIOps platform - believes it’s the long-awaited solution to the problems posed by an age of big data.

It’s estimated that by 2025, 463 exabytes - or 463,000,000,000 gigabytes - of data will be produced each day. Sifting through that amount of information will prove impossible for humans, and the main barrier to truly being able to use big data is down to people being unable to keep up with its rapid growth. However, AIOps could, in theory, make all this information manageable and useable.

Looking further into the future, Ali Siddiqui - general manager for the AIOps segment at CA Technologies and Forbes Technology Council member - thinks AIOps will transform the way in which we work. He likens this to the difference between individual insects and groups of ants or bees; the latter are working with a “collective intelligence”, enabling them to focus on the group’s overall goals. This is something AIOps could help businesses to achieve.

Applying AIOps to your strategy: practical use cases

But what does this mean in practice? For most businesses, the introduction of AIOps will enable them to monitor the performance of their applications in much greater detail, bringing together disparate sources of data from across the business and analyzing them quickly to cut through the noise and identify the most relevant issues that need to be brought to IT pros' attention.

The power of artificial intelligence and machine learning allows enterprises to effectively deal with the huge quantities of information generated by today's businesses, no matter where across the network such data originates or is processed. In a world of Internet of Things devices, edge computing and cloud, it will be impossible for manual monitoring processes to keep up.

Therefore, there are three key things you can do with AIOPs that would now be achievable using legacy application monitoring techniques:

1. Spotting hidden relationships

Today's IT operations are a complex web of interdependencies, and no one system works in isolation. But being able to understand these relationships is not easy when there is so much data flying around the business. With AIOps, you can more easily compare performance metrics across a wide range of systems to identify the impact your IT applications are having on overall performance and customer satisfaction.

This can be done by first working with business units to identify mission-critical activities for these applications, then gathering data generated during their day-to-day activities, such as orders, transactions, cancellations etc.

AIOps algorithms can then be used to spot patterns or clusters in the combined business and IT data, from which businesses can better understand the relationship and build up a chain of causality that identifies what applications are affecting particular business activities.

2. Forecasting future issues

Another key role of AIOps will be in boosting predictive analytics activities. By closely studying past and current behavior within apps, this technology can extrapolate what the most likely future scenarios will be, allowing businesses to proactively adjust their strategy to best take advantage.

This may, for example, help companies spot changing trends in how users interact with customer-facing apps, which will inform the future direction of software development. Or it could flag up anomalies that are early warning signs of forthcoming failures or business risks. This technology will enable businesses to conduct an in-depth analysis of the root cause of any problems and take steps to mitigate them before they become an issue that affects performance.

3. Making the most of customer and transaction data

AIOps machine learning capabilities can assist in pattern recognition, anomaly detection, classification and extrapolation, all of which are key elements of the big data analytics operations that businesses should be applying to their customer and transaction data. Therefore, turning AIOps' focus to these information sources can greatly help understand how user behavior impacts the wider IT system and vice-versa.

This may make it easier to track the effects any changes you make to your applications will have on business units. Will a certain change here lead to slower response times for customers, for example, and if so, what impact will it have on sales? With AIOps helping to bring together customer and transaction data with internal application monitoring information, businesses can have this information to hand immediately, helping them choose the right path for their applications' future.

Is AIOps worth it?

Of course, setting up AIOps is an expense that might not feel worth it at this time. So, is the technology actually valuable to businesses right now, or is it better to wait before investing? Well, many companies are already seeing major benefits from utilizing AIOps in their current business structures.

Reduce response time

Firms are able to use the technology to reduce their response time to outages and errors. It’s thought that using AIOps can reduce the cost of events like this by between 30% and 40%, which is a significant saving when the average cost of service disruption for many businesses sits at around $300,000 per hour.

Part of this is due to the technology’s ability to see the bigger picture when it comes to data. Businesses often deal with a large range of different systems, each producing huge amounts of information. For example, medium to large companies use an average of 8 different cloud providers. Tools already exist to monitor this data, but these have their flaws.

Connecting silos

Overwhelmingly, the main issue is how siloed companies can be, with an astonishing 91% of IT leaders saying their monitoring tools only provide them with information about their own specific areas of responsibility.

AIOps has the potential to deal with this issue, using machine learning and data analytics to oversee thousands of information streams, spotting problems that would be extremely difficult to identify using a siloed approach.

This isn’t just a theory; Brazilian telecoms provider Nextel has already utilized AIOps to monitor over 25,000 network elements. This has enabled the firm to reduce the average time it takes to respond to incidents across its network from 30 minutes to just five.

While there is definitely a strong future ahead of AIOps, the technology is already being used to make significant cost savings for many businesses. In most cases, this means the solution is worth investing in.