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Monitoring as a Differentiator: Breaking Silos and Building Understanding

David Drai

Monitoring a business means monitoring an entire business – not just IT or application performance. If businesses truly care about differentiating themselves from the competition, they must approach monitoring holistically. Separate, siloed monitoring systems are quickly becoming a thing of the past.

I see time and again cloud monitoring companies working with a myopic focus on the Infrastructure area – a critical mistake. They concentrate on system health but avoid business health like the plague. Although CPU, Disk, Memory and other infrastructure KPIs are essential to maintain a healthy system, their coverage is limited and lacking an equally crucial component that drives how well a company is operating – its business. Today there is simply no excuse for having incomplete monitoring capabilities, and it is more necessary than ever to get out of monitoring siloes.

Cloud Monitoring 1.0 and the Evolution of Metrics

Monitoring infrastructure provides some visibility to overall system health by keeping machines up and running – but it is not at all adequate to determine what is occurring on the business side of a company. Infrastructure monitoring is also far too basic to keep up with updates within applications – essentially putting blinders on a company's leadership.

As it stands, infrastructure monitoring tools usually run in conjunction with other internal tools to gain an angle on the business, or analysts rely on Business Intelligence solutions that may be connected to infrastructure monitoring through internal scripts. In most cases, these 1.0 level tools require a great deal of internal development and maintenance which are difficult to scale.

In the past few years, time series metrics have been the main driver of growth in cloud monitoring systems. This approach of normalizing almost all data per a single time series representation has enabled the provision of generic solutions for many cases and different customers. Because of its rudimentary ability, it is not surprising that open source solutions are becoming so widespread among the businesses which are beginning to understand the importance of monitoring. The ability to represent all metrics in the same manner using the same dashboards and time series function sets has significantly simplified this monitoring method providing good but not fully comprehensive information.

Today's Challenges of Monitoring Business

One of the main challenges of monitoring business KPIs is that static rules and alerts are too limiting. Particularly for metrics that change per trends or seasons, static alerts are difficult to maintain because of their inherent variability. Even in the simplest cases, it is very difficult to define thresholds for thousands of metrics because it requires the user to have working knowledge of their normal range. For e-commerce companies, the holiday season is always a peak time in sales and every metric is going to behave "abnormally." It is nearly impossible for large data-driven companies, which are monitoring so much, to start making changes to reset the threshold for every single metric – talk about a nightmare.

Another challenge of monitoring so many metrics is defining rules manually especially when each metric has a different normal range. Unfortunately, it is essential that this be done to achieve effective configuration. Amazon needs to know that "Elf on a Shelf" dolls are going to sell heavily in November and that gift certificates will be sold later in the month.

Cloud Monitoring 2.0: for IT, applications AND BUSINESS

The newest generation of monitoring centralizes all company activity into a single unified solution, rather than separate solutions for IT, application, and business. This is the holistic understanding that companies have been working towards for so long – the ability to understand every metric separately and together. It is one thing to see an infrastructure anomaly on its own, but to be able to contextualize it with the correlated impact on the business affords an entirely new way to problem-solve and measure the health of a company. Beyond addressing the immediate issues this type of top-down monitoring approach offers tremendous value.

Without a smart mechanism to monitor so many rules and alerts, companies are bound to compromise what they monitor, sacrificing all for a few selected metrics. Analysts are not fortune tellers – there is no way to define what the best metrics are to monitor. This creates an inevitable delay in detection of issues, which severely limits how proactive a company can be in the varied business scenarios it faces. It also limits the granularity of the organization's visibility – bringing us back to where we were with Cloud Monitoring 1.0.

Only recently the implementation of AI in BI is enabling companies to solve challenges in monitoring. By automating the ability to differentiate between what is normal and abnormal behavior (no matter the trend or time of year) businesses finally have a chance to review a comprehensive and automatic evaluation of anomalies. With the addition of AI to monitoring, companies can differentiate themselves by how quickly they respond to changing conditions; how quickly they find bugs and glitches, how rapidly they respond to customers in crisis, and how swiftly they leverage a business opportunity triggered by a celebrity's viral Instagram post.

While companies engage with their customers in more ways than ever before, finding ways to break out of monitoring silos is going to be the key that companies use to successfully scale and compete with industry giants.

David Drai is CEO and Co-Founder of Anodot.

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Monitoring as a Differentiator: Breaking Silos and Building Understanding

David Drai

Monitoring a business means monitoring an entire business – not just IT or application performance. If businesses truly care about differentiating themselves from the competition, they must approach monitoring holistically. Separate, siloed monitoring systems are quickly becoming a thing of the past.

I see time and again cloud monitoring companies working with a myopic focus on the Infrastructure area – a critical mistake. They concentrate on system health but avoid business health like the plague. Although CPU, Disk, Memory and other infrastructure KPIs are essential to maintain a healthy system, their coverage is limited and lacking an equally crucial component that drives how well a company is operating – its business. Today there is simply no excuse for having incomplete monitoring capabilities, and it is more necessary than ever to get out of monitoring siloes.

Cloud Monitoring 1.0 and the Evolution of Metrics

Monitoring infrastructure provides some visibility to overall system health by keeping machines up and running – but it is not at all adequate to determine what is occurring on the business side of a company. Infrastructure monitoring is also far too basic to keep up with updates within applications – essentially putting blinders on a company's leadership.

As it stands, infrastructure monitoring tools usually run in conjunction with other internal tools to gain an angle on the business, or analysts rely on Business Intelligence solutions that may be connected to infrastructure monitoring through internal scripts. In most cases, these 1.0 level tools require a great deal of internal development and maintenance which are difficult to scale.

In the past few years, time series metrics have been the main driver of growth in cloud monitoring systems. This approach of normalizing almost all data per a single time series representation has enabled the provision of generic solutions for many cases and different customers. Because of its rudimentary ability, it is not surprising that open source solutions are becoming so widespread among the businesses which are beginning to understand the importance of monitoring. The ability to represent all metrics in the same manner using the same dashboards and time series function sets has significantly simplified this monitoring method providing good but not fully comprehensive information.

Today's Challenges of Monitoring Business

One of the main challenges of monitoring business KPIs is that static rules and alerts are too limiting. Particularly for metrics that change per trends or seasons, static alerts are difficult to maintain because of their inherent variability. Even in the simplest cases, it is very difficult to define thresholds for thousands of metrics because it requires the user to have working knowledge of their normal range. For e-commerce companies, the holiday season is always a peak time in sales and every metric is going to behave "abnormally." It is nearly impossible for large data-driven companies, which are monitoring so much, to start making changes to reset the threshold for every single metric – talk about a nightmare.

Another challenge of monitoring so many metrics is defining rules manually especially when each metric has a different normal range. Unfortunately, it is essential that this be done to achieve effective configuration. Amazon needs to know that "Elf on a Shelf" dolls are going to sell heavily in November and that gift certificates will be sold later in the month.

Cloud Monitoring 2.0: for IT, applications AND BUSINESS

The newest generation of monitoring centralizes all company activity into a single unified solution, rather than separate solutions for IT, application, and business. This is the holistic understanding that companies have been working towards for so long – the ability to understand every metric separately and together. It is one thing to see an infrastructure anomaly on its own, but to be able to contextualize it with the correlated impact on the business affords an entirely new way to problem-solve and measure the health of a company. Beyond addressing the immediate issues this type of top-down monitoring approach offers tremendous value.

Without a smart mechanism to monitor so many rules and alerts, companies are bound to compromise what they monitor, sacrificing all for a few selected metrics. Analysts are not fortune tellers – there is no way to define what the best metrics are to monitor. This creates an inevitable delay in detection of issues, which severely limits how proactive a company can be in the varied business scenarios it faces. It also limits the granularity of the organization's visibility – bringing us back to where we were with Cloud Monitoring 1.0.

Only recently the implementation of AI in BI is enabling companies to solve challenges in monitoring. By automating the ability to differentiate between what is normal and abnormal behavior (no matter the trend or time of year) businesses finally have a chance to review a comprehensive and automatic evaluation of anomalies. With the addition of AI to monitoring, companies can differentiate themselves by how quickly they respond to changing conditions; how quickly they find bugs and glitches, how rapidly they respond to customers in crisis, and how swiftly they leverage a business opportunity triggered by a celebrity's viral Instagram post.

While companies engage with their customers in more ways than ever before, finding ways to break out of monitoring silos is going to be the key that companies use to successfully scale and compete with industry giants.

David Drai is CEO and Co-Founder of Anodot.

Hot Topics

The Latest

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...