Root Cause Analysis: Causal Versus Derived Events
April 15, 2014

Tom Molfetto
SericeNow

Share this

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.

Share this

The Latest

September 23, 2021

The Internet played a greater role than ever in supporting enterprise productivity over the past year-plus, as newly remote workers logged onto the job via residential links that, it turns out, left much to be desired in terms of enabling work ...

September 22, 2021

The world's appetite for cloud services has increased but now, more than 18 months since the beginning of the pandemic, organizations are assessing their cloud spend and trying to better understand the IT investments that were made under pressure. This is a huge challenge in and of itself, with the added complexity of embracing hybrid work ...

September 21, 2021

After a year of unprecedented challenges and change, tech pros responding to this year’s survey, IT Pro Day 2021 survey: Bring IT On from SolarWinds, report a positive perception of their roles and say they look forward to what lies ahead ...

September 20, 2021

One of the key performance indicators for IT Ops is MTTR (Mean-Time-To-Resolution). MTTR essentially measures the length of your incident management lifecycle: from detection; through assignment, triage and investigation; to remediation and resolution. IT Ops teams strive to shorten their incident management lifecycle and lower their MTTR, to meet their SLAs and maintain healthy infrastructures and services. But that's often easier said than done, with incident triage being a key factor in that challenge ...

September 16, 2021

Achieve more with less. How many of you feel that pressure — or, even worse, hear those words — trickle down from leadership? The reality is that overworked and under-resourced IT departments will only lead to chronic errors, missed deadlines and service assurance failures. After all, we're only human. So what are overburdened IT departments to do? Reduce the human factor. In a word: automate ...

September 15, 2021

On average, data innovators release twice as many products and increase employee productivity at double the rate of organizations with less mature data strategies, according to the State of Data Innovation report from Splunk ...

September 14, 2021

While 90% of respondents believe observability is important and strategic to their business — and 94% believe it to be strategic to their role — just 26% noted mature observability practices within their business, according to the 2021 Observability Forecast ...

September 13, 2021

Let's explore a few of the most prominent app success indicators and how app engineers can shift their development strategy to better meet the needs of today's app users ...

September 09, 2021

Business enterprises aiming at digital transformation or IT companies developing new software applications face challenges in developing eye-catching, robust, fast-loading, mobile-friendly, content-rich, and user-friendly software. However, with increased pressure to reduce costs and save time, business enterprises often give a short shrift to performance testing services ...

September 08, 2021

DevOps, SRE and other operations teams use observability solutions with AIOps to ingest and normalize data to get visibility into tech stacks from a centralized system, reduce noise and understand the data's context for quicker mean time to recovery (MTTR). With AI using these processes to produce actionable insights, teams are free to spend more time innovating and providing superior service assurance. Let's explore AI's role in ingestion and normalization, and then dive into correlation and deduplication too ...