Entering a Golden Age of Data Monitoring
June 13, 2018

Thomas Stocking
GroundWork Open Source

Share this

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.

Thomas Stocking is Co-Founder and VP of Product Strategy at GroundWork Open Source
Share this

The Latest

May 23, 2019

The first word in APM technology is "Application" ... yet for mobile, apps are entirely different. As the mobile app ecosystem is evolving and expanding from pure entertainment to more utilitarian uses, there's a rising need for the next generation of APM technology to stay ahead of the issues that can cause apps to fail ...

May 22, 2019

For application performance monitoring (APM), many in IT tend to focus a significant amount of their time on the tool that performs the analysis. Unfortunately for them, the battle is won or lost at the data access level. If you don’t have the right data, you can’t fix the problem correctly ...

May 21, 2019

Findings of the Digital Employee Experience survey from VMware show correlation between enabling employees with a positive digital experience (i.e., device choice/flexibility, seamless access to apps, remote work capabilities) and an organization's competitive position, revenue growth and employee sentiment ...

May 20, 2019

In today's competitive landscape, businesses must have the ability and process in place to face new challenges and find ways to successfully tackle them in a proactive manner. For years, this has been placed on the shoulders of DevOps teams within IT departments. But, as automation takes over manual intervention to increase speed and efficiency, these teams are facing what we know as IT digitization. How has this changed the way companies function over the years, and what do we have to look forward to in the coming years? ...

May 16, 2019

Although the vast majority of IT organizations have implemented a broad variety of systems and tools to modernize, simplify and streamline data center operations, many are still burdened by inefficiencies, security risks and performance gaps in their IT infrastructure as well as the excessive time it takes to manage legacy infrastructure, according to the State of IT Transformation, a report from Datrium ...

May 15, 2019

When it comes to network visibility, there are a lot of discussions about packet broker technology and the various features these solutions provide to network architects and IT managers. Packet brokers allow organizations to aggregate the data required for a variety of monitoring solutions including network performance monitoring and diagnostic (NPMD) platforms and unified threat management (UTM) appliances. But, when it comes to ensuring these solutions provide the insights required by NetOps and security teams, IT can spend an exorbitant amount of time dealing with issues around adds, moves and changes. This can have a dramatic impact on budgets and tool availability. Why does this happen? ...

May 14, 2019

Data may be pouring into enterprises but IT professionals still find most of it stuck in siloed departments and weeks away from being able to drive any valued action. Coupled with the ongoing concerns over security responsiveness, IT teams have to push aside other important performance-oriented data in order to ensure security data, at least, gets prominent attention. A new survey by Ivanti shows the disconnect between enterprise departments struggling to improve operations like automation while being challenged with a siloed structure and a data onslaught ...

May 13, 2019

A subtle, deliberate shift has occurred within the software industry which, at present, only the most innovative organizations have seized upon for competitive advantage. Although primarily driven by Artificial Intelligence (AI), this transformation strikes at the core of the most pervasive IT resources including cloud computing and predictive analytics ...

May 09, 2019

When asked who is mandated with developing and delivering their organization's digital competencies, 51% of respondents say their IT departments have a leadership role. The critical question is whether IT departments are prepared to take on a leadership role in which collaborating with other functions and disseminating knowledge and digital performance data are requirements ...

May 08, 2019

The Economist Intelligence Unit just released a new study commissioned by Riverbed that explores nine digital competencies that help organizations improve their digital performance and, ultimately, achieve their objectives. Here's a brief summary of 7 key research findings you'll find covered in detail in the report ...