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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

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 ...