Making Use of Artificial Intelligence for IT Operations Analytics / AIOps

Brent PhillipsBy Brent PhillipsArtificial Intelligence for IT Operations Analytics

Enterprise computing systems and storage operations teams have a difficult job: manage the IT infrastructure so that application availability is always efficiently maintained. But this is virtually impossible due to the complexity and disparity of the meta-data and reporting tools for all the various infrastructure components. A lack of information is not the problem, rather the great need is to derive meaningful intelligence out of all the information.

But the cloud, for example, will not work for all applications due to performance and security requirements. And outsourcing doesn’t make infrastructure performance problems go away, in fact it can make them harder to resolve. So most enterprise organizations will still benefit from and require deep infrastructure performance analysis capabilities.

In recent years, a new class of products initially called IT Operations Analytics (ITOA) have come on the market with the design objective of providing a single interface into all the data generated from disparate devices, and more importantly, helping interpret what it really means for performance, availability, and efficiency.

The idea is to employ the computer to do more of the work of deriving meaningful intelligence out of all the data. If designed correctly, this is a type of artificial intelligence which is done by the machine and enables human IT operations teams to be more effective. In 2017 Gartner coined the term AIOps which is a nice nomenclature for the capability.

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6 Signs You Already Have a Skills Gap for z/OS Performance and Capacity Planning

Brent PhillipsBy Brent PhillipsSigns you have a skills gap for z/os performance and capacity planning

The mainframe skills gap is a well-known issue, but most of the focus is on mainframe application development. A large z/OS mainframe organization may have thousands of application developers but only 20 or fewer performance & capacity planning staff. Even though fewer in number, these IT staff have an outsized impact on the organization.

The problem, however, is not just about recruiting new IT staff members to the team. The road to becoming a true z/OS performance and capacity (perf/cap) expert is far longer and more difficult than what is necessary for a programmer to learn to code in a mainframe programming language like COBOL. Consequently, it is not feasible to fill the performance and capacity planning gap with new recruits, and recruiting experienced staff from the short supply is difficult. Even teams that have all the headcount positions filled very often exhibit at least some of the signs that they are being negatively impacted by insufficient levels of expert staff.

A primary contributor to the problem is the antiquated way of understanding the RMF and SMF performance data that most sites still use. The way this data is processed and interpreted not only makes it difficult for new IT staff to learn the job, but it also makes the job for the existing experts more difficult and time consuming.

Here are six signs that indicate your z/OS performance and capacity team would benefit by modernizing analytics for your infrastructure performance and configuration data.

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z/OS Performance Monitors – Why Real-Time is Too Late

By Morgan Oatsperformance monitor

Real-time z/OS performance monitors are often advertised as the top tier of performance management. Real-time monitoring means just that: system and storage administrators can view performance data and/or alerts indicating service disruptions continuously as they happen.

In theory, this enables administrators to quickly fix the problem. For some companies, service disruptions may not be too serious if they are resolved quickly enough. Even though those disruptions could be costing them a lot more than they think, they believe a real-time monitor is the best they can do to meet their business needs.

For other companies, optimal z/OS performance is essential for day-to-day operations: banks with billions of transactions per day, global retailers, especially on Black Friday or Cyber Monday, government agencies and insurance companies that need to support millions of customers at any given time, transportation companies with 24/7 online delivery tracking; the list goes on and on.

For these organizations and many others, real-time performance information is in fact, too late. They need information that enables them to prevent disruptions – not simply tell them when something is already broken.

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