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Root Cause Analysis: Causal Versus Derived Events

Tom Molfetto

Today’s business landscape is saturated with data. Big Data has become one of the most hyped trends in the tech space, and all indicators point to the reality that this volume of data is only going to grow. IDC estimates that we’ll see a 60% growth in structured and unstructured data annually. Global 2000 organizations are investing billions of dollars into harnessing the power of Big Data to help make it meaningful and actionable. In other words, organizations are spending a ton of money in an effort to translate data into information.

Data – in and of itself – is fairly useless. When data is interpreted, processed and analyzed – when its true meaning is unearthed – it becomes useful and is called information. Thus the race between players like Splunk, QlikView and others to be the first or the best to harness the power of Big Data by translating it into actionable information.

Helping data center personnel and enterprise IT professionals translate their data into information by isolating causal versus derived events is really relevant to businesses these days. In most of my explorations, I have discovered that organizations are using a best-of-breed approach to monitoring, in what has resulted in a sort of Balkanization of the data center. In a common use case: network teams may be using Cisco for monitoring, the database teams use Oracle and web server teams uses Nagios. But nothing ties all of that information together in a unified view. There is no monitor of monitors, or manager of managers, so to speak. Let alone a unified view that goes beyond the IT components and maps them to their associated business services.

So what happens when a LAN port fails, and the app server and database server that both communicate through that LAN port also fail as a result? In that scenario, the LAN port failure is the causal event and the app/database server failures are derived events. By being able to quickly distinguish between the two types of events, and isolate the root cause of the failure, the dependent business services can be restored while minimizing negative impact on overall operations.

Standard monitoring solutions will trigger a bunch of red flags showing failures, but in order to make the map “come alive” it needs to be architected and displayed in a topological format. This is what allows easier assessment of root cause versus derived events, and what contributed to a dramatically reduced Meant-Time-To-Know (MTTK) with regard to diagnosing the underlying issues impacting business services.

Best-of-breed monitoring tools should continue to be leveraged in their respective domains, but the most forward-thinking organizations are unifying these tools from a service-centric perspective to create a monitor of monitors that maps IT components to associated business services, and connects with the best-of-breed solutions to create a complete and up-to-date topology that empowers IT to do their jobs more effectively.

Providing IT with the tools required to interpret data meaningfully and isolate the root cause of problems helps to create an informed perspective from which decisions can be made and responses taken.

Tom Molfetto is Marketing Director for Neebula.

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Root Cause Analysis: Causal Versus Derived Events

Tom Molfetto

Today’s business landscape is saturated with data. Big Data has become one of the most hyped trends in the tech space, and all indicators point to the reality that this volume of data is only going to grow. IDC estimates that we’ll see a 60% growth in structured and unstructured data annually. Global 2000 organizations are investing billions of dollars into harnessing the power of Big Data to help make it meaningful and actionable. In other words, organizations are spending a ton of money in an effort to translate data into information.

Data – in and of itself – is fairly useless. When data is interpreted, processed and analyzed – when its true meaning is unearthed – it becomes useful and is called information. Thus the race between players like Splunk, QlikView and others to be the first or the best to harness the power of Big Data by translating it into actionable information.

Helping data center personnel and enterprise IT professionals translate their data into information by isolating causal versus derived events is really relevant to businesses these days. In most of my explorations, I have discovered that organizations are using a best-of-breed approach to monitoring, in what has resulted in a sort of Balkanization of the data center. In a common use case: network teams may be using Cisco for monitoring, the database teams use Oracle and web server teams uses Nagios. But nothing ties all of that information together in a unified view. There is no monitor of monitors, or manager of managers, so to speak. Let alone a unified view that goes beyond the IT components and maps them to their associated business services.

So what happens when a LAN port fails, and the app server and database server that both communicate through that LAN port also fail as a result? In that scenario, the LAN port failure is the causal event and the app/database server failures are derived events. By being able to quickly distinguish between the two types of events, and isolate the root cause of the failure, the dependent business services can be restored while minimizing negative impact on overall operations.

Standard monitoring solutions will trigger a bunch of red flags showing failures, but in order to make the map “come alive” it needs to be architected and displayed in a topological format. This is what allows easier assessment of root cause versus derived events, and what contributed to a dramatically reduced Meant-Time-To-Know (MTTK) with regard to diagnosing the underlying issues impacting business services.

Best-of-breed monitoring tools should continue to be leveraged in their respective domains, but the most forward-thinking organizations are unifying these tools from a service-centric perspective to create a monitor of monitors that maps IT components to associated business services, and connects with the best-of-breed solutions to create a complete and up-to-date topology that empowers IT to do their jobs more effectively.

Providing IT with the tools required to interpret data meaningfully and isolate the root cause of problems helps to create an informed perspective from which decisions can be made and responses taken.

Tom Molfetto is Marketing Director for Neebula.

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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