Two of Australia’s largest supermarket chains had to close their stores last year due to technical issues they suffered nationwide. This resulted in a huge loss of revenue, not to mention the high level of frustration faced by customers. It could have however been avoided.
The truth is that IT teams are dealing with a huge amount of data using tools and techniques that are often causing delays in identifying and resolving issues. What they need is a robust AIOps strategy. When leveraged well, AIOps will have a transformative effect on IT.
As Senior Director Analyst Padraig Byrne at Gartner rightly points out, “IT operations are challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analysed and acted on. Coupled with the reality that IT operations teams often work in disconnected silos, this makes it challenging to ensure that the most urgent incident at any given time is being addressed.”
The immediate need is to prevent, identify and resolve high-severity outages and other related problems that pose challenges for the Operations teams.
The answer? Artificial Intelligence for IT operations (AIOps). What they need then is a roadmap that’s robust and effective. Here’s how we can create the perfect AIOps tools strategy.
Traditional tools and AIOps
According to Gartner, the exclusive use of AIOps to monitor applications and infrastructure in large enterprises will rise to 30% in 2023 as compared to 5% in 2018. In our previous blog, we had discussed the many components of AIOps.
It’s time we understood how we can take the AIOps plan forward. An emerging trend as identified by Gartner suggests that the traditional tools and processes are not suited for dealing with the challenges faced by modern digital enterprises due to the humungous amounts of data and agility.
Gartner believes that organizations need a big data platform that allows the merging and coexistence of IT Service Management (ITSM), IT Operations Management (ITOM), and IT Automation at the data layer.
The platform should support real-time analytics that is managed by machine learning that processes and supports supervised as well as unsupervised data and also has answers to deep historical queries.
Tools in IT silos will remain sovereign, which means, Service Management will still handle incidents, requests, etc. while Performance Management will manage metrics, events, logs, etc. but the data will be connected and analysed in a way that enterprises would be able to make faster decisions while speeding up process and task automation.
The goal of AIOps tools strategy
The ultimate goal for having an AIOps tool strategy is to ensure that the data flows freely from multiple IT data sources into the platform. And then it is analysed and processed, automated workflows are triggered. And the entire system works in a way that it adapts and responds to the changing data volumes. The response should be automatically adjusted as per the data and its sensitivity and concerned administrators should be duly informed.
Use cases must be identified early on. The focus should be on questioning the ‘why’ of desired outcomes, prioritizing use cases, and identifying the gaps between capabilities, tools, skills, and processes.
With time, technologies will change, priorities will shift and new use cases will keep coming up, and accordingly, your desired outcomes will change too. Your AIOps tools strategy, therefore, should be able to address these challenges and open up a whole new world of possibilities.
Assess your data streaming capabilities to help with AIOps
The whole crux or the intent of the strategy would be to ensure the free flow of data from disparate tools into the big data platform. You, therefore, need to assess the ease and frequency with which data flows so that you can receive and send data in real-time.
Not all IT monitoring and service desk tools support outbound data streaming. While they may support programmatic interaction in the latest versions using REST API, they may not support streaming in the case of traditional relational databases like Oracle or SQL even if they have a programmatic interface. Due to this lack of support, the performance impact will not be as desired. You need to have clear answers to questions like:
- How and what kind of data do I get from existing tools?
- How often can I use it?
- Will I be able to do so programmatically?
Once you have pertinent answers, you will be in a position to tweak your data consolidation strategy and replace your IT tools for effective data streaming in real-time. Assessment of data streaming capabilities, therefore, should be treated as a high-priority task when you decide to develop an AIOps tools strategy.
Establish mutually agreeable data sharing practices for better management
It is important that the IT Operations team and the IT Service Management team come together to review the data jointly. For the same, it is crucial to have clearly defined roles and responsibilities.
While they don’t need to analyse the entire volume of data, they would still require assessing of data that tells them what’s happening in their environment, what actions need to be taken and accordingly make decisions that are tracked for effectiveness.
Teams should agree on the following:
- Deciding what data is required
- Where it can be stored
- Create joint access for sharing and review
With DevOps teams using Jira to log defects and enhancements, it has become even more important for enterprises to identify challenges and work unanimously to arrive at a plan to collate and review data together. Parkar NextGen Platform, for instance, comes with dashboards to help filter data for specific uses of varied IT audiences.
Automation is key to AIOps
While everyone understands the importance of automation, we have a long way to go before everyone embraces it completely. In an environment where data moves and grows beyond the human scale, it is critical to automate all tasks and orchestrate processes.
DevOps teams are now moving at lightning speeds to automate and orchestrate things and plug into the CI/CD toolchain. With the right processes and teams, you will be able to know who owns the code, what is its impact on production, identify developer backlog and measure productivity effectively. All you need to do is automate and orchestrate the things they do across siloed tools.
Parkar NextGen Platform
The steps mentioned above identify and iterate just a few of the key elements of developing an effective AIOps tools strategy. Alternatively, you can leverage Parkar’s robust platform to get a better grip on your IT functions and align them with your business goals.
Broad business benefits:
- Enriched AIOps data
- The clarity to prioritize issues
- Automate service assurance through a model-driven approach
- Excellent algorithmic correlation
- Cognitive insights to process data more efficiently
Those who have used the platform have experienced incredible results. Primary Operational Benefits include:
- Reduction in tedious manual tasks: 74%
- Faster MTTR: 67%
- Anomaly Detection: 58%
- Casualty Determination: 48%
- Alert co-relation and inferencing: 49%
- Data insights: 73%
- Noise Reduction: 28%
- Root Cause Analysis: 68%
AIOps adoption is critical for successful digital transformation. It’s time you realized the full potential of AIOps and see how it can put you on the road to success with machine learning, big data, and analytics. Request a demo or call us today and we would be happy to take you on a tour of amazing possibilities. What we promise is greater efficiency. The question is- Are you ready to embrace AIOps?
Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.