AIOps — Operations Analytics Redefined
“Data is a precious thing and will last longer than the systems themselves.” — Sir Timothy John Berners-Lee
We’re living in exciting times when it comes to harnessing the power of data, in copious amounts with the help of Artificial Intelligence. From AI being the next big thing for transforming your business processes, to the soul of customer engagement — the opportunities are endless. IT operations is currently one of the most promising areas where AI can prove itself practical.
We’ve seen some of the biggest technology companies in the world gradually shift towards Artificial Intelligence solutions. With customers and employees demanding faster response times, more reliable service and IT operations teams being increasingly asked to deliver custom reports or dashboards quickly as a way to track KPIs — it makes sense for these enterprises to put aside traditional thinking and start embracing AI. So let’s take a look at the ways in which we can use Artificial Intelligence for IT operations to improve business’ performance.
What is AIOps?
The concept originally known as Algorithmic IT Operations was later coined as AIOps by Gartner in 2016 as an industry category. Gartner’s official description of the AIOps is :
“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”
In short, AIOps is a way to better monitor, analyse and improve system performance with the help of mathematical algorithms and models, providing real-time awareness of the entire platform with accurate engagement methods, thereby increasing the efficiency of IT Operations.
AIOps and DevOps
IT systems have been undergoing vast expansion in the recent times in response to the societal and technological transitions. A huge side effect of this acceleration tends to be the rapid rate in which these solutions have to be developed and deployed, and also the colossal amounts of data that is then produced by these systems. The traditional IT development and operations methodologies were simply no match to deal with this scenario, which eventually led project deadlines to slip. It’s this primordial soup of tech issues that gave birth to DevOps.
DevOps can be defined as a combination of software development and IT operations to speed up the development process. The benefits of DevOps methodology is such that it ensured faster, more effective processes, along with an improved end-user experience. The operating environment tends to be more stable with the help of improved communication and collaboration. DevOps provided the needed dexterity to the entire workflow, which made it a favourite of business entities across the globe. More and more enterprises are now adopting cloud-native applications with the help of containers and microservices to achieve better scalability and fault tolerance. This in-turn have made the job of the DevOps team more complex. They now need a more robust solution capable of identifying and eliminating issues with better efficiency.
This is where AIOps comes into picture, to assist DevOps teams in handling routine IT tasks. By applying AI and machine learning algorithms to the monitoring data, AIOps can learn an environment’s behaviours and generate alerts accordingly. Based on the anomalies detected, AIOps will be able to correlate important alerts from all sources into actionable, contextual insights, thus enabling DevOps automation. A comprehensive AIOps solution will also be able to identify root causes and impacts, and prescribes potential solutions based on previous resolution steps and feedback.
How does it work?
Data Source
AIOps works with traditional IT monitoring data sources such as logs, metrics, wire data and so on. The data from all these sources can then be further analysed to identify consequential events using algorithms, which would otherwise require arduous manual effort due to the volume and complexity of the incoming data streams.
Big Data
To assist with real-time processing of IT streaming data, a Big Data platform such as Hadoop, is what feeds the Machine Learning/AI algorithms.
Algorithms/Machine Learning
The AIOps platform will need to make to use of different algorithms to make the best use of the ingested data. This will include:
- Analysing system performance using various KPIs
- Identifying patterns upon which an action might need to be taken
- Correlating the relationships between different datasets and group them as required
- Notifying and empowering the right people to take the right action.
All these algorithms are based on Machine Learning, which injects the Artificial Intelligence into the system and gives it the ability to ingest and adapt to new information on its own. In IT operations, machine learning is what sets AIOps apart from legacy ITOps. Without machine learning, an AIOps platform offers only an incremental improvement over traditional ITOps.
Automation
The actions that are yielded as a result of AI analysis can then be automated to provide a more quick and precise response to an incident or a scenario, thereby increasing the overall system performance.
The features established with the help of AIOps algorithms along with its automation constitutes the five primary use cases of an AIOps platform — Performance Analysis, Anomaly Detection, Event correlation and analysis, IT service management and Automation.
Who are the users?
AIOps is a platform that can be used by organizations of all types and sizes for different scenarios.
Cloud-Native SMEs
Cloud Native infrastructure is something that AIOps is best suited for as the applications would be deployed as microservices within containers and it would be ideal for Small and Medium Enterprises to entirely opt for such an infrastructure which would be more cost effective and efficient at that scale.
Even though applications within a cloud native environment is easier to develop and maintain, it makes inter service communication much more complicated. The number of deployments too will be numerous, all together making IT operations that much difficult. AIOps will be a much valuable addition to DevOps here.
Large Enterprises with Complex Environments
Large enterprises would deal with systems spanning different technology types, consuming vast amounts of data. Maintaining such systems, providing efficient analysis will be a job best suited for AIOps.
Enterprises with Hybrid Environments
Having workloads based in cloud have its own benefits but so is the case with on premise infrastructure, especially when dealing with data that is highly confidential. Hence the reason organisations at times opt to have a hybrid infrastructure, utilizing best of both worlds.
But this might create headaches for IT ops to keep track of everything. AIOps helps teams to maintain control over such infrastructure set-ups.
Enterprises undergoing Digital Transformation
Digital transformation is the process of replacing non-digital or manual processes with digital processes thus transforming the business or associated services. This may also be an upgrade of an existing digital service with a newer technology platform. Digital solutions may enable new innovation and creativity on top of performance enhancements.
AIOps will be greatly helpful in such transformation projects where precise analysis will be required.
My take
AIOps is an integral part of the new digital transformation wave wherein the entire industry is moving towards a cloud-native infrastructure.
Long gone are the days of monolithic systems, as enterprises are adapting modular architectures for better agility with the help of DevOps methodologies, which on the other hand increases the complexity of those systems drastically.
AIOps provides the much needed relief for the IT operations personnel, who seems to be struggling to accommodate the massive demands of such an infrastructure.
