Why it's time to replace the “break and fix” model with “predict and prevent”

George Thangadurai, CEO, Heal Software Inc.
George Thangadurai, CEO, <a href='https://healsoftware.ai/' rel='nofollow' target='_blank' style='color:blue !important'>Heal Software Inc.</a>

George Thangadurai, CEO, Heal Software Inc.

Disruption of end users or operations is often the first noted signal of an information technology (IT) problem, leaving enterprises to rely on an antiquated break-and-fix model. Compounding this problem, IT departments are plagued with tens of thousands of alerts each week, causing alarm fatigue and making it hard for them to prioritize which problems need immediate attention. This can result in significant financial, productivity and reputational losses. In fact, service outages can cost thousands of dollars per minute according to research by the Digital Enterprise Journal.

The old-fashioned “break and fix” model

Most artificial intelligence for IT operations (AIOps)tools on the market claim to use machine learning (ML) models and artificial intelligence (AI) algorithms to detect and flag incidents, perform correlation between seemingly unrelated events across monitoring silos and provide variants of a potential root cause. However, any remedial actions are always after the fact; and none of these tools are effective at eliminating downtime.

While the “break and fix” model has been the status quo, it no longer has to be the reality. The recent paradigm shift in IT operations and the diagnosis of application health has changed the focus of IT operations from fast detection and problem fixing to preventive healing whereby digital enterprises prevent problems before they ever occur.

How “predict and prevent” is changing the game

Preventive healing is a new category of monitoring and AIOps software. Using AI and ML, it preempts any possible outage by acting before it occurs. Detection of any situation where an outage or issue is imminent becomes all important in these cases, allowing teams to “predict and prevent” versus wait for something to “break and fix.” Shifting to the “predict and prevent” model is not only beneficial for the internal team, but also the customer or end-user experience.

• Provides valuable business insights:

Predictive systems give business leaders a view of the future. This technology 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. Moreover, the ML model can be trained and refined further with these additional insights.

In addition, the traditional “breakandfix” model is focused on risk mitigation and containment, much like applying a band-aid to a deep wound. This results in enterprises throwing money at the problem and hoping to avoid outages by over-deploying resources. This can include paying for excess capacity to ensure redundancy, as well as assigning valuable development teams to fix problems. Replacing this model with “predict and prevent” can help businesses make smarter decisions and save valuable resources.

• Simplifies internal intervention:

Alarm fatigue is real in the IT space. When the alarm arrives, there can be a triage of problems, many of which are difficult to address due to the level of burden already on the IT teams’p late. Even more so, preempting an outage or issue is more complex and requires detailed algorithms and 24/7 monitoring, which is well-beyond the scope of even the best IT professionals. Relying on manpower to cross-analyze all the systems can make finding a problem like looking for a needle in a haystack. Preventive healing with AI technology can automatically detect anomaly signals and find the source sothat a problem can be fixed before it occurs. If it cannot fix the problem, it can identify the root cause for the IT professionals, minimizing time and energy wasted on discovering issues. Early identification not only helps eliminate customer disruptions but can free the IT team up to focus on other pressing items.

• Improves customer experience:

When errors or outages occur under the “break and fix” model oftentimes customers are the first to flag the problem. Because traditional reactive models cannot identify and warn against unnatural patterns of behavior before they result in issues, by the time they are detected it is often too late. This can be very frustrating and seriously erode customer retention. With the preventive approach, end-users rarely encounter any problems as most potential issues are flagged and eliminated before they cause outage or performance degradation. This ensures a better customer experience and can improve retention rates.

HEAL is the first preventive healing software for IT operations that makes this possibility a reality. HEAL uses unsupervised and supervised ML models to learn how a system works under normal circumstances and creates a dynamic baseline for the entire system and workload behavior, thereby precisely predicting and preventing problems. Enterprises that have switched to HEAL’s proprietary software have benefitted from four key capabilities:

1. Predictive and Preventive

Due to clustering and regression models which can predict the potential behavior for a workload mix, HEAL is in the unique position to intelligently detect anomalies and leverage healing actions and remedial workflows to bring system parameters back to normal before an issue occurs.

2. Collective Knowledge

HEAL is not just preventive healing – it also provides a full-stack infrastructure and business activity monitoring solution. It comes equipped with its own agents to collect workload, behavior, configuration and log data, and is comprised of a suite of APIs and connectors to integrate with most APM vendors and content formats.

3. Situational Awareness

HEAL produces precise predictions by using contextual data at the time of the anomaly – including forensic data capturing the state of the processes/queries running on the system at the time. This data is used to determine causation and ensure that responses are coherent and complete.

4. Remedial and Autonomous

HEAL provides remedial actions in two scenarios: by scaling up to handle the workload and triggering autonomous correction of underlying issues that cause anomalies. HEAL’s intelligent ML engine leverages patented techniques to ensure it always delivers the best response to the problem. Leveraging these patented techniques, enterprises can feel confident that they are receiving the best response.

As IT continues to move to a multi-cloud environment, now is the time for AIOps adopters and decision-makers to assess the gaps of current off­erings. Moving from the “break and fix” to “predict and prevent” model is the only way to provide confidence that a company’sIT infrastructure is up and running all the time and applications are available 24x7.Simply put: with predict and prevent the world is a better place for digital enterprises.