What is AIOps, and what can it do for businesses?
“AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination” – Gartner
AIOps platforms, by definition, are robust enough to absorb and analyze massive data volumes, no matter how diverse, with speed – thus releasing HR from the burden. As a result, IT management has been the first to embrace AIOps, appreciating it as a groundbreaking solution for businesses of all sizes, yielding ROI-centric metrics.
When do you think the penny drops? When’s the first-time organizations in financial services, banking, life sciences, pharma, hospitality, retail, media & entertainment know that they want AIOps in their business?
It’s the minute the stakeholders accept that machine learning (ML) and data science principles have a role in the future of their IT operations. In other words, when they realize that AI practicalities can push traditional IT operations out of the picture. That, or at least, partially replace them. Many still take some convincing, but it’s getting a lot easier. The real question is, how much easier?
AIOps is big and growing by the day
The trend is motoring at a rapid pace. Gartner predicts that enterprise-class companies will be heavily into ML methodologies in their applications and networks by 2023 (i.e., 30% deployment versus 5% back in 2018). Indeed, the expected shift is so impressive that it’s moving into the realm of IT transformation. As its capabilities emerge and new application possibilities pop up, AIOps will gain traction and exceed even the most optimistic thinking.
Launching an AIOps initiative in your IT operations:
1. “AIOps is only for mega-size businesses, right?” Wrong!
As AIOps usage gallops forward, the providers scale up and reduce the cost, sometimes dramatically, making it affordable to a broader group. Many products are at that stage already, benefiting from open-source developments around low-cost ML software.
The trick is knowing how to find them and evaluate their worth to your operations with an accurate cost/benefits overview. So, our advice is not to hesitate to learn what AI and ML can deliver to your IT operations, the vocabulary behind them, and where it’s working for others in your industry.
You may not have a project to take on the AI challenge now, but you’ll have a running start the day you want to give it a go. Also, look for professional advice from entities like Atlas Systems on which products suit your business and budget.
2. Start small; learn as you go along
Transform into the AIOps space at your pace. In that way, you won’t feel overwhelmed or out of control. Remember, IT processes are a means to an end, not the end themselves. Sometimes we lose sight of that, resulting in confusion. So, test and retest until you feel comfortable with this new automation opportunity as an integral part of your workforce.
3. Address employee fear
Facing the prospect of a robot or machine-like device replacing one’s job is likely to be super scary to staff unless you show them the bigger picture. So, let them see it for what it truly is. Therefore, build a positive AIOps image within the team ranks by explaining the methodologies to everyone. Go so far as to take them through the different ML techniques. Finally, encourage staff – at the right moment – to identify the areas AIOps will help them in their careers by taking routine activities off their plates.
4. Set the AIOps stage in the IT division
AIOps thrive on a consistent IT architecture, infrastructure code (such as IaC), and immutable infrastructure patterns. Setting things up in this way, even prematurely, won’t interfere with the current system. On the contrary, it will enable you to accommodate AIOps when the time comes without disrupting anything else. So, the mantra is: aim for standardization in your IT planning and modernize internally wherever it’s feasible.
5. AIOps won’t stand still for you
Remember, the pace of IT sophistication in parallel with ML innovation makes this a highly dynamic environment. As a result, what many companies may consider as state-of-the-art today can fade away in a short time.
So, once you have AIOps in the system working for you, evaluate platforms that provide for scaling up and other adjustments. It’s wise to develop new capabilities internally on existing software instead of buying new applications every time things change.
6. The gold may be buried already in your garden
Data analytics extends to other departments, and the skills and technologies may already be driving other aspects of your business and easily adaptable. Exercise due diligence that includes a data processing audit to uncover hidden resources already energized by AIOps software outside of IT. If found, there are many ways to extend ML capabilities to benefit your IT strategies.