Recently there has been a paradigm shift in IT operations and the diagnosis of application health. Up until now, IT operations have focused on quickly detecting and fixing problems as they arise. However, in the last few years a new model has emerged; one of self-healing whereby digital enterprises prevent problems before they ever occur. The self-healing model benefits the user as they see fewer service disruptions, and also IT operations teams and businesses themselves as a higher uptime makes their product more attractive than the competition.
The Break and Fix Model
The trouble with the break and fix model is that most of the time it’s the customer who finds the problem. They may be doing an online booking when the form crashes and all of their data, that they’ve spent the last ten minutes inputting, is lost. This can be very frustrating and seriously erode customer retention.
It’s true that IT teams have a myriad of tools at their disposal to attempt to identify problems before customers experience a service disruption. However, these tools are often designed to work in the break and fix model and they feature fixed indicators such as performance level or fault alarms. When the alarm does come there can be a triage of problems, many of which are difficult to address owing to the IT stack’s complexity. Most stacks have a web of relations between distributed applications and infrastructure running on the cloud, on-premise or on edge. Given the complexity of the stacks, finding the problem can be like looking for a needle in a haystack.
In a nutshell, the goal of the break and fix model is risk mitigation and containment. Enterprises throw money at the problem and hope to avoid outages by over deploying resources. That can include paying for excess capacity to ensure redundancy as well as assigning valuable development teams to fix problems. All of that works OK, however, there is a better option.
The Predict and Prevent Model
With predict and prevent the world is a better place for digital enterprises. End users will rarely encounter any problems as a majority of potential issues are eliminated before they cause an outage. This ensures better customer experience and higher retention rates.
Another benefit is that IT teams don’t have to be tasked with discovering the problem as the bugs never arise in the first place. A preventative AI program automatically detects anomaly signals and finds the source so that problems can be fixed before they affect the user. An AI can either help an operator to implement a patch or take remedial action and fix the problem itself. With AI running the show the operations team can spend their time maintaining the service level, not sifting through an avalanche of alarms and alerts in order to diagnose a problem.
The final advantage of predictive systems is that they give business leaders a view of the future. Predictive systems can analyze business growth data in order to model future states of the ecosystem and determine where the capacity bottlenecks are. With this level of precision resource deployments can be optimized, reducing both capital and operating costs.
A Better System
Predict and prevent systems are altogether better at maintaining network uptime and offering the best possible user experience. They fix problems before they occur, better manage resources and free up IT teams to focus on more pressing issues. Digital enterprises that adopt predict and prevent systems are finding it easier to scale their business and outcompete the competition.