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How "Predict-and-Prevent" Monitoring Software is Helping Enterprises

Girish Muckai
HEAL Software Inc.

It isn't uncommon for IT departments to be overwhelmed by alerts each week, causing alarm fatigue and making it hard for them to prioritize troubleshooting. Therefore, disruption of operations is often the first signal of IT problems, leaving enterprises to rely on an outdated break-and-fix model. This can result in significant financial and productivity losses.

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 unrelated events and provide a variety of potential root causes. However, this means remedial actions are always after the fact; and the tools are not able to eliminate downtime.

While the "break and fix" model has been the norm for most enterprises, new monitoring technology has started to take its place. The recent paradigm shift in IT operations and the diagnosis of application health has changed the focus of IT operations from quick detection and problem fixing to preventive healing, where digital enterprises prevent problems before they occur.

Preventive healing uses AI and ML to stop any possible outage by acting before it occurs. This enables IT departments to detect a likely outage, shifting teams to a "predict and prevent" approach versus the outdated "break and fix" method.

More so than simply preventing outages, predictive systems also bring value to the greater business. This technology can analyze business growth data in order to model future states of the ecosystem and determine where the capacity bottlenecks are. This data makes it possible to optimize resource deployments, reducing both capital and operating costs. Moreover, the ML model can be trained and refined further with these additional insights.

Businesses are also able to make smarter business decisions and save valuable resources when leveraging preventive healing software. Under the traditional "break and fix" model, which is focused on mitigating risk and containment, enterprises are left throwing money at problems and over-deploying resources to avoid outages. This can include paying for excess capacity to ensure redundancy, as well as assigning valuable development teams to fix problems. Shifting to "predict and prevent" allows the IT department to use their resources to support imminent problems.

Preventive healing can also help address alarm fatigue. IT teams often have a lot on their plate, so when a new alarm sounds, it can be difficult for them to address as there can be a host of potential problems. 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 so that 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.

Preventive healing software for IT operations 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 predicting and preventing problems. However, not all software is the same.

Here are four key capabilities to look for when choosing a preventive healing software:

1. Predictive and Preventive

Some AIOps software can intelligently detect anomalies and leverage healing actions and remedial workflows to bring system parameters back to normal before an issue occurs.

2. Collective Knowledge

Because software is often connected, it is helpful to seek out a solution that is 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

Preempting an outage or issue is complex and requires detailed algorithms and 24x7 monitoring, well beyond the scope of even the best IT professionals. Some technology uses 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 can be used to determine causation and ensure that responses are coherent and complete.

4. Remedial and Autonomous

New technology can provide remedial actions in two scenarios: By 1) scaling up to handle the workload and 2) triggering autonomous correction of underlying issues that cause anomalies. Look for a solution that has intelligent ML engine techniques to ensure it always delivers the best response to the problem.

As IT continues to move to a multi-cloud environment, it is the perfect time for adopters and decision-makers to assess the gaps in their current IT offerings. Moving from the "break and fix" to "predict and prevent" model is the only way to provide confidence that a company's IT infrastructure is up and running all the time and applications are available 24x7.

Girish Muckai is Chief Sales and Marketing Officer at HEAL Software Inc.

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How "Predict-and-Prevent" Monitoring Software is Helping Enterprises

Girish Muckai
HEAL Software Inc.

It isn't uncommon for IT departments to be overwhelmed by alerts each week, causing alarm fatigue and making it hard for them to prioritize troubleshooting. Therefore, disruption of operations is often the first signal of IT problems, leaving enterprises to rely on an outdated break-and-fix model. This can result in significant financial and productivity losses.

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 unrelated events and provide a variety of potential root causes. However, this means remedial actions are always after the fact; and the tools are not able to eliminate downtime.

While the "break and fix" model has been the norm for most enterprises, new monitoring technology has started to take its place. The recent paradigm shift in IT operations and the diagnosis of application health has changed the focus of IT operations from quick detection and problem fixing to preventive healing, where digital enterprises prevent problems before they occur.

Preventive healing uses AI and ML to stop any possible outage by acting before it occurs. This enables IT departments to detect a likely outage, shifting teams to a "predict and prevent" approach versus the outdated "break and fix" method.

More so than simply preventing outages, predictive systems also bring value to the greater business. This technology can analyze business growth data in order to model future states of the ecosystem and determine where the capacity bottlenecks are. This data makes it possible to optimize resource deployments, reducing both capital and operating costs. Moreover, the ML model can be trained and refined further with these additional insights.

Businesses are also able to make smarter business decisions and save valuable resources when leveraging preventive healing software. Under the traditional "break and fix" model, which is focused on mitigating risk and containment, enterprises are left throwing money at problems and over-deploying resources to avoid outages. This can include paying for excess capacity to ensure redundancy, as well as assigning valuable development teams to fix problems. Shifting to "predict and prevent" allows the IT department to use their resources to support imminent problems.

Preventive healing can also help address alarm fatigue. IT teams often have a lot on their plate, so when a new alarm sounds, it can be difficult for them to address as there can be a host of potential problems. 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 so that 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.

Preventive healing software for IT operations 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 predicting and preventing problems. However, not all software is the same.

Here are four key capabilities to look for when choosing a preventive healing software:

1. Predictive and Preventive

Some AIOps software can intelligently detect anomalies and leverage healing actions and remedial workflows to bring system parameters back to normal before an issue occurs.

2. Collective Knowledge

Because software is often connected, it is helpful to seek out a solution that is 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

Preempting an outage or issue is complex and requires detailed algorithms and 24x7 monitoring, well beyond the scope of even the best IT professionals. Some technology uses 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 can be used to determine causation and ensure that responses are coherent and complete.

4. Remedial and Autonomous

New technology can provide remedial actions in two scenarios: By 1) scaling up to handle the workload and 2) triggering autonomous correction of underlying issues that cause anomalies. Look for a solution that has intelligent ML engine techniques to ensure it always delivers the best response to the problem.

As IT continues to move to a multi-cloud environment, it is the perfect time for adopters and decision-makers to assess the gaps in their current IT offerings. Moving from the "break and fix" to "predict and prevent" model is the only way to provide confidence that a company's IT infrastructure is up and running all the time and applications are available 24x7.

Girish Muckai is Chief Sales and Marketing Officer at HEAL Software Inc.

Hot Topics

The Latest

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...