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Top Use Cases for IBM i Data in Splunk: IT Operations Analytics

Today’s computing environments are a complex arrangement of many hardware components and several software layers, and it is vital that each of these parts functions to the best of its ability. In the case of customer-facing systems, the failure of one element can impact hundreds, thousands or even millions of users.

Ensuring the optimal performance and availability of IT systems and applications, while also controlling IT costs and maximizing the use of critical resources, has become a significant challenge for IT professionals.

For decades, organizations have collected different types of data and monitored systems to support better operations and address issues.

With today’s powerful IT Operations Analytics (ITOA) platforms, we are able to unlock the value that has been hidden in the detailed logs that are generated by enterprise systems. However, as with most technology solutions, the devil is in the details.

  1. STRUCTURE. Machine data usually comes in files of semi-structured, unformatted data. Of course, each system or “machine” has its own way of logging data, which makes this task even more challenging.
  2. SEQUENCE. Machine data is mostly sequential. This means, to get the most meaningful insights, you must look at the entire chain of events.
  3. VOLUME. Machine data volumes can be massive. With hundreds of servers and other types of systems, dozens of applications and logs recording every step of a given event or transaction, volumes can easily reach terabytes of data per day.
  4. TIMING. We all know data loses value over time, but when it comes to operational intelligence, the value diminishes exponentially. That’s why both real-time data and a researchable historical record for analysis is critical for machine data.

These factors alone can make an operational intelligence initiative far more complicated than a traditional business intelligence project. As a result, operational intelligence requires an unconventional approach involving ITOA powered by multiple, simultaneous streams of machine data, correlated together and possessing a searchable, continuous machine record.