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Using Log Analytics to Accelerate Hybrid Incident Identification

By Kekahu Aluli posted Tue August 31, 2021 09:45 AM

  
Decide Phase: Log Analysis
Introduction

Organizations are drawn to the promise of #AIOps to leverage AI-driven Intelligence and automation to make quick and accurate decisions to maintain resiliency. AIOps uses artificial intelligence to simplify IT operations management and accelerate and automate problem resolution in complex modern IT environments. A recent blog by Sanjay Chandru set the stage for guiding you on Best practices for taking a hybrid approach to AIOps. We learned that a key capability of AIOps is deciding root causes. Accurately diagnosing and deciding how to fix problems quickly in dynamic and complex environments across hybrid cloud infrastructure and applications empowers IBM Z IT Ops teams and accelerates the AIOps journey.


In this blog we will focus on Log Analytics: accelerate hybrid incident identification with real-time operational analytics.

 
Client challenges

The IT Operations Analytics (#ITOA) market is amidst a tremendous growth period driven by several factors including, the proliferation of data, increased drive towards a hybrid-cloud infrastructure approach, and the effects of COVID-19 forcing a ramp up of analytics to mitigate its impact. These external factors have forced the modern enterprise to adapt to meet their client’s expectations.


A key element of this IT Operations Analytics ecosystem is a platform in which you can analyze and understand both large amounts and a wide range of operational data. The complexity of data types and sources/ destination of such data further complicate this equation. What good is the data being generated if you are unable to leverage it to make key business decisions?


When we examine how mainframe teams fit into this ITOA framework, they have a more unique set of needs. The most important thing for these teams is reducing the number of unplanned outages and being able to get to the root cause of any potential issue before they impact end users. From this statement it becomes abundantly clear that there is a need to be able to visualize Z operational data within the context of the entire enterprise, rather than being a black box where it’s difficult to understand how changes within the mainframe affect clients and end users. They also need a way to quickly dive deep into the data to minimize potential adverse effects from unplanned outages.

 
What's now required and how different then what I have today?

Today, most mainframe shops are still operating in organizational silos without an effective way to gather insights about what happens outside their environments. On the same playing field, the distributed side of the house does not have a clear picture to what happens to once data flows into the mainframe. What’s needed is a Log Analytics platform that can provide contextualized insights on Z operational data. Only with these powerful visualizations and insights will we start to see the mainframe become a first-class citizen of the hybrid-cloud infrastructure. 


This begs the exploration of another key gap in the current ITOA landscape. The true value of data visualization and correlation is its ability to move the needle on effective decision making. The Z platform generates numerous records that can be used to do just that, but as with anything, proper knowledge of these key indicators is needed. What’s required is a log analytics solution that can make sense of these Z specific KPIs and present them in a more consumable manor.

 
How IBM can help

As the world’s largest mainframe solutioners, IBMers are the experts in all things Z. IBM Z Operational Log and Data Analytics delivers a solution specifically tailored to maximize your investment and extract key insights from your Z system. The log analytics component is the piece focused on accelerating your hybrid incident identification with real-time operational analytics.


One of the key IBM differentiators in this space is the ability to leverage a built-in, single collection and data curation pipeline. This advanced streaming capability gives you the ability to complete powerful filtering and data transformation to optimize the ingestion pattern that best fits your enterprise. For example, the ability to stream Z operational data using the HTTP Event Collector on Splunk has enabled many users to see ingesting reduction upwards of 7x. These capabilities enable you to make operational decisions to reduce ingestion costs, or stream 7x the data at the previous rate. These innovative features continue to be the backbone of a well-oiled log analytics solution.


Another key piece of this puzzle is having analytics flexibility. IBM Z Operational Log and Data Analytics gives users the ability to leverage an IBM Z focused log analytics platform as well as industry leading platforms such as Splunk and Elk. This is extremely helpful for the wide range of use cases we see from our clients. For some, the need to have a platform that runs on Z is crucial, therefore they choose to leverage the Z IBM platform delivered by IBM Z Operational Log and Data Analytics. For others who’ve invested in Splunk or Elastic, having the ability to now see Z data on the same screen and in the context of their hybrid ecosystem is of extreme value. In any case, the flexibility offered by this solution is something that lends itself to the core of the AIOps strategy.

 
Client outcome

APIS IT and their mainframe teams were struggling to keep up with the increasingly complex workloads and meeting client expectations. They were looking for a way to extract value from their mainframe data and move towards a proactive approach in their operations.


APIS IT relies on the IBM Z log analytics capabilities (formerly IBM Z Operations Analytics) as they seek to elevate their hybrid-cloud workloads. With this tool, they can collect IBM Z operational data, performs metric-based anomaly detection, then delivers it to APIS IT’s Splunk interface in near real-time. Z data visualized in the same context as the rest of their hybrid ecosystem, enables them to focus on making strides in their AIOps transformation.


After implementing this solution, APIS IT was able to get Increased visibility into their mainframe, which is a crucial piece of their hybrid-cloud environment. They saw a reduction of costs associated with their previous homegrown solutions. Finally, they were able to see an increase in productivity by letting employees focus on their actual job, vs wasting time sifting through data.


“IZOA is the tool that simplifies the mainframe so I can put more sense into the data.” - Dražen Zadro, Systems Engineer, APIS IT d.o.o.

 
What are my next steps?
Depending on where you are on your journey to adopting more of these AIOps best practices we are sharing the following resources to obtain a deeper understanding:

  • To assess your current stage of AIOps maturity and identify action oriented next steps for adopting more AIOps best practices, inquire about the 15-minute online AIOps Assessment for IBM Z.
  • Join the AIOps on IBM Z Community to follow this blog series about best practices for taking a hybrid approach to AIOps
  • And finally, to research our IBM Z products that are implementing AIOps technologies to improve operational resiliency visit our product portfolio page.
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