Driving on autopilot is one of the ultimate comforts a car can provide. You do not have to worry about applying accelerator or brake, you could just enjoy the view with some music, while you get to your destination, hassle-free. Enterprises too are increasingly looking to manage their tedious and complex business processes in a hands-free way. Artificial intelligence, embedded analytics and automation together can deliver the ultimate solution enterprises are looking for: Running your company on autopilot. This is made possible by using self-healing systems that have the ability to monitor, detect and correct course of business operations when they are not performing in the intended manner and restore their normal state without any external assistance.
Modern day IT infrastructure are complex with multiple systems and interconnections, all of which need to be functioning seamlessly 24×7 to deliver the experience that users these days demand. But an IT expert knows that it is not possible and sometimes systems do deviate from their normal functionality due to various changes and upgrades. So how can enterprises ensure their processes always run like a well-oiled machine? By investing in self-healing infrastructure.
Why the need for self-healing systems?
The need for self-healing systems, which uses embedded intelligence, has increased exponentially as customers have transitioned to expect digital services by default due to the pandemic. Enterprises on the other hand are still dealing with their day-to-day operations with a hybrid workforce spread across the globe. For companies who have not updated their technology stack, the pandemic has brought forth a new set of challenges like spike in manual errors and security threats. Technology too is becoming more complex as infrastructure and operations span across on-premise and off-premise data centers across multiple generations of technology. A failure in one process can cause cascading effects for many services.
According to a survey of the Fortune 1000 by IDC, the average total cost of unplanned downtime per year amounts to $1.25 billion to $2.5 billion and the average hourly cost of infrastructure failure is $100,000 per hour. End-users expect secure, always-on, uninterrupted services. Investors too do not view service disruptions lightly and may potentially move their investments elsewhere. We live in the time where service disruptions are amplified manyfold by social media, causing the cost to be much higher when we calculate the reputational risk along with lost customers.
For many enterprises, the unfortunate reality is that their service failures are self-inflicted injuries, that could have been well prevented if they had invested in infrastructure and technologies that ensure service reliability. With self-healing capabilities, enterprises can relax while the machines take care of their workflows and processes. i.e., run your company on autopilot.
How does self-healing systems and embedded intelligence work?
The backbone of self-healing systems is embedded intelligence. It is a system that has both predictive and reactive intelligence capabilities that can analyze its own operations and fine-tune its functions automatically without human interference. Using embedded intelligence enhances performance which results in higher user satisfaction. According to a recent State of Embedded Analytics Report, 93% of enterprise stakeholders said it improved their user experience while 96% said it contributes to overall revenue growth and 83% said they will continue their investments in embedded analytics.
When assigned a set of processes, embedded intelligence proactively monitors and predicts potential anomalies and makes adjustments to restore standard operations, thus reducing operational toil and improving service reliability. A self-healing system is the convergence of data analytics, artificial intelligence, machine learning and automation. It generally follows the below three components:
Detect: The embedded analytics self-healing system tracks information from infrastructure, network, application and other sensors across data centers and captures both historical and real-time data to detect changes in the state of system or process. AI/ML help in detecting issues before they create problems.
Decide: If anomalies are detected, it traces the problem to the source using the environment’s topology and triggers alerts. It studies the issue and then validates the necessary auto-remediation required.
Restore: A pre-approved restore protocol is kicked off to ‘heal’ the system of anomalies and bring the operations back to its normal functionality without human assistance. The remediation techniques may range from a simple script to sophisticated bots.
Implementing self-healing systems can have many intangible positive effects on an enterprise, the important one being employee and customer satisfaction. Elimination of system downtime means that companies can deliver consistent, high-quality and reliable services while continuously improving operational performance. It also helps enterprises to focus on their core business instead of worrying about their IT functions. Self-healing infrastructure secures systems effectively, ensures compliance and mitigates cost. Overall, it is a win-win situation for the end-user and the enterprise.
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