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Entering a Golden Age of Data Monitoring

Thomas Stocking

The importance of artificial intelligence and machine learning for customer insight, product support, operational efficiency, and capacity planning are well-established, however, the benefits of monitoring data in those use cases is still evolving. Three main factors obscuring the benefits of data monitoring are the infinite volume of data, its diversity, and inconsistency. However, it's these same factors that are fueling a Golden Age of systems monitoring.

1. Data Availability is Increasing

The trend over the last several years has been to collect more data – more than can ever be analyzed by humans. Data monitoring tools, by their very function, are in and of themselves a significant source of data. With the advent of NoSQL databases, optimize-on-read technologies, and the availability of very fast data consumers (influxdb, Opentsdb, Cloudera, etc.), the amount of data from monitoring systems is exploding.

2. Monitoring Data is Diverse

You would think more is better, as is often the case with data. That is what we learned in high school stats class, after all. However, more isn't always better, and in fact, most of the data we gather from monitoring is rather difficult to analyze programmatically. There are many reasons for this such as the complexity of modern IT infrastructures as well as the diversity of data.

Data diversity is an old IT problem. We collect data on network traffic, for example, using SNMP counters in router and switch MIBs. We also use netflow/sflow and do direct packet capture and decoding. So to even answer the question, "Why is the network slow?" we have at least three potential data sources, each with its own collection method, data types, indices, units and formats. It's not impossible to do analysis on the data we collect, but it is hard to gain insight when dealing with what my colleagues and I call "plumbing problems."

3. Monitoring Data is Inconsistent

You would think after all this time monitoring systems there would be a standard for the storage and indexing of metrics for analysis. Well, there is. In fact, there are several (Metrics 2.0, etc.). Yet, we are still dealing with inconsistency across tools in such basic areas as units, time scales, and even appropriate collection methods. With these inconsistencies, sampling data at five minutes vs. five seconds can yield vastly divergent results.

Benefits from Monitoring Data

Despite these issues, we are moving into a Golden Age of analysis. It's clear the most consistent parts of the monitoring data stream such as availability (as determined by health checks, for example) can be mined for very useful data, and used to create easily understood reports. If you combine this with endpoint testing, such as synthetic transactions from an end-user perspective, the picture of availability becomes much clearer and can be used to effectively manage SLAs.

Delving a level or two deeper, measurements of resource consumption over time can reveal trends that help with capacity planning and cost prediction. Time series analysis of sets of data that are consistent can reveal bottlenecks and even begin to point the way to root cause analysis, though we are still far away from automating this aspect.

The Future of Data Monitoring

There's a revolution in monitoring data with the advent of the cloud. We are suddenly able to gather a lot of data on the availability and performance of nearly every aspect of our systems that we run in the cloud.

In fact, as far as APIs go, there are even services that will consume all of your application traffic and analyze it for you, opening the possibility of dynamic tracing of transactions through your systems. If you are going cloud-native, you can take advantage of this area of unprecedented completeness and consistency of data, with minimal "plumbing" to worry about.

However, expect your job to get both easier and harder. Easier, since you will have more data, and sophisticated systems to analyze it. These systems and data it produces are becoming more homogeneous with cloud technologies and more consistent as the monitoring industry settles on standards. This will provide you better data for the systems you buy to analyze.

It will also be harder. When your systems fail, you won't easily find the data needed to fix things yourself. Similar to your cloud vendor, your monitoring system will be a complex and powerful toolset that will need time to learn, and you will absolutely be reliant on your providers for their expertise in its finer points.

Despite these challenges, the potential impact of effective data monitoring is significant. Effective data monitoring can help reduce outage and availability issues, support capacity planning, optimize capital investment, and help maintain productivity and profitability across an entire IT infrastructure. As IT systems become increasingly more complex, data monitoring becomes increasingly more vital.

Hot Topics

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Entering a Golden Age of Data Monitoring

Thomas Stocking

The importance of artificial intelligence and machine learning for customer insight, product support, operational efficiency, and capacity planning are well-established, however, the benefits of monitoring data in those use cases is still evolving. Three main factors obscuring the benefits of data monitoring are the infinite volume of data, its diversity, and inconsistency. However, it's these same factors that are fueling a Golden Age of systems monitoring.

1. Data Availability is Increasing

The trend over the last several years has been to collect more data – more than can ever be analyzed by humans. Data monitoring tools, by their very function, are in and of themselves a significant source of data. With the advent of NoSQL databases, optimize-on-read technologies, and the availability of very fast data consumers (influxdb, Opentsdb, Cloudera, etc.), the amount of data from monitoring systems is exploding.

2. Monitoring Data is Diverse

You would think more is better, as is often the case with data. That is what we learned in high school stats class, after all. However, more isn't always better, and in fact, most of the data we gather from monitoring is rather difficult to analyze programmatically. There are many reasons for this such as the complexity of modern IT infrastructures as well as the diversity of data.

Data diversity is an old IT problem. We collect data on network traffic, for example, using SNMP counters in router and switch MIBs. We also use netflow/sflow and do direct packet capture and decoding. So to even answer the question, "Why is the network slow?" we have at least three potential data sources, each with its own collection method, data types, indices, units and formats. It's not impossible to do analysis on the data we collect, but it is hard to gain insight when dealing with what my colleagues and I call "plumbing problems."

3. Monitoring Data is Inconsistent

You would think after all this time monitoring systems there would be a standard for the storage and indexing of metrics for analysis. Well, there is. In fact, there are several (Metrics 2.0, etc.). Yet, we are still dealing with inconsistency across tools in such basic areas as units, time scales, and even appropriate collection methods. With these inconsistencies, sampling data at five minutes vs. five seconds can yield vastly divergent results.

Benefits from Monitoring Data

Despite these issues, we are moving into a Golden Age of analysis. It's clear the most consistent parts of the monitoring data stream such as availability (as determined by health checks, for example) can be mined for very useful data, and used to create easily understood reports. If you combine this with endpoint testing, such as synthetic transactions from an end-user perspective, the picture of availability becomes much clearer and can be used to effectively manage SLAs.

Delving a level or two deeper, measurements of resource consumption over time can reveal trends that help with capacity planning and cost prediction. Time series analysis of sets of data that are consistent can reveal bottlenecks and even begin to point the way to root cause analysis, though we are still far away from automating this aspect.

The Future of Data Monitoring

There's a revolution in monitoring data with the advent of the cloud. We are suddenly able to gather a lot of data on the availability and performance of nearly every aspect of our systems that we run in the cloud.

In fact, as far as APIs go, there are even services that will consume all of your application traffic and analyze it for you, opening the possibility of dynamic tracing of transactions through your systems. If you are going cloud-native, you can take advantage of this area of unprecedented completeness and consistency of data, with minimal "plumbing" to worry about.

However, expect your job to get both easier and harder. Easier, since you will have more data, and sophisticated systems to analyze it. These systems and data it produces are becoming more homogeneous with cloud technologies and more consistent as the monitoring industry settles on standards. This will provide you better data for the systems you buy to analyze.

It will also be harder. When your systems fail, you won't easily find the data needed to fix things yourself. Similar to your cloud vendor, your monitoring system will be a complex and powerful toolset that will need time to learn, and you will absolutely be reliant on your providers for their expertise in its finer points.

Despite these challenges, the potential impact of effective data monitoring is significant. Effective data monitoring can help reduce outage and availability issues, support capacity planning, optimize capital investment, and help maintain productivity and profitability across an entire IT infrastructure. As IT systems become increasingly more complex, data monitoring becomes increasingly more vital.

Hot Topics

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...