A staggering 80% of companies in the US are in various stages of their digital transformation journey. This means that over time, the vast majority of operational activities will come under the ambit of IT. This meteoric projection is reflected on the global scale as well with the market for digital transformation expected to reach $1 trillion by 2026!
However, this dependence on IT also comes with its own side of disadvantages. The chief among them and the focus of this discussion being IT downtime.
Various estimates peg the cost of IT downtime at ~$5500 per minute. For some firms engaged in high–value transactions, a downtime could result in losses of $1-1.5 MM per hour. The reasons for downtimes can be various and difficult to solve. But what can be changed is how IT teams respond to this niggling problem.
The traditional strategy of waiting for something to go wrong and then taking action can result in months of intermittent disruptions. Lloyd’s Bank found this out the hard way when an IT outage locked their ~2 million customers out of their accounts for weeks.
Pivoting To Predictive Analytics
As more operations are moved to the IT domain, relying on automated processes and algorithms to complete tasks, the chances for a faults & breakdowns arise proportionally. Eventually, enterprises will reach a stage in the automation when humans are ill-equipped to proactively monitor and fix systems.
The solution is to rely on AI-powered predictive analytics.
Tools built upon predictive analytics algorithms are capable of preventing problems from materializing in the first place.
Seebo, a predictive analytics solution provider, recently implemented their solution at a global biotech manufacturing firm. Using a combination of data analytics & ML, the solution identified the correlation between variables that were causing a blockage in the firm’s reactor to centrifuge pipeline. The result was an 83% reduction in downtime and 72% savings in downtime costs.
AI Systems for IT Operations or AIOps, are capable of monitoring IT systems in real-time and checking for early signs of potential failures. ML algorithms use past data to learn how problems typically develop and enables stakeholders to step in before anything unfortunate occurs.
It’s not just in detecting anomalies, AIOps can significantly reduce the number of false alarms and even warm operators when IT systems are not running at full efficiency.
A Plethora Of Use Cases
While AIOps is still a niche field, ~30% of global enterprises are expected to embrace it fully by 2023.
AIOps use cases include:
- Intelligent alerting – AI-powered alerting systems collect data from logs, events & sensors to calibrate alert thresholds manually. Pattern-based alerting is being used by network traffic switchers to handle overloads and divert packets intelligently. Over time, ML models learn optimal network pathways and perform switching with minimal manual intervention.
- Cross-domain situational understanding – AIOps aggregates data over a cross section of information domains and draws cause/effect maps that be used to extract actionable insights.
- Real-time performance monitoring – By taking control over the application performance environment, AIOps provides complete overview of real-time performance metrics and raises alerts based on pre-trained ML models. This decreases the mean time to response. AIOps is increasingly leveraged in the emerging field of Deep Medicine.
Our signals strongly indicate that service providers who can hit the ground running in delivering solutions for the above use-cases will be in high-demand post-COVID.
Draup tracks business intentions and digital themes across 30+ industries to proactively highlight AIOps use cases. Our proprietary Signals Cast app delivers real-time insights that vendors can utilize to explore trending use cases and win deals.
