Making Use of Artificial Intelligence for IT Operations Analytics / AIOps

B._Phillips-web0By 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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IntelliMagic Sessions at SHARE Sacramento

IntelliMagic will be at SHARE Sacramento, March 11 – 16, hosting a Lunch & Learn and presenting four performance and capacity sessions you won’t want to miss! Join us at booth #205 to see the latest in intelligent analytics for performance and capacity planners.

IntelliMagic sessions at SHARE Sacramento

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AI and z/OS Performance and Capacity Analysis: 2018 Predictions

B._Phillips-web0By Brent Phillips2018 Predictions on AI and z/OS Performance and Capacity Analysis

2018 is gearing up to be a watershed year for z/OS performance and capacity professionals.

Industry analysts have been talking for some years now about Artificial Intelligence (AI) and the role it will play in our work. But what that truly means, and its value in day-to-day operations has not yet been understood or realized by most professionals in this field.

There are many different types of AI, but not all are useful in making the computer do the kind of infrastructure performance and availability health assessment work that is no longer feasible for human analysts to proactively do every day. But when properly designed and deployed, it has proven very effective to implement automated, AI-driven decision making about what all the data means for identifying current or near-term performance problems and their root-causes.

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What is AIOps? The Benefits Explained

By Morgan Oats

August 2017 ushered in a new term heralded by Gartner in the form of AIOps: Artificial Intelligence for IT Operations. The term has certainly generated a lot of market hype, but what exactly is AIOps, and how can it help support your business operations?

Gartner’s official definition for AIOps is:

“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”

AIOps: Artificial Intelligence for IT Operations

Source: Gartner [https://blogs.gartner.com/andrew-lerner/2017/08/09/aiops-platforms/]

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

B._Phillips-web0By Brent Phillips

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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Bridging the z/OS Mainframe Performance & Capacity Skills Gap

B._Phillips-web0By Brent Phillips

Many, if not most organizations that depend on mainframes are experiencing the effects of the mainframe skills gap, or shortage. This gap is a result of the largely baby-boomer workforce that is now retiring without a new generation of experts in place who have the same capabilities. At the same time, the scale, complexity, and change in the mainframe environment continues to accelerate. Performance and capacity teams are a mission-critical function, and this performance skills gap represents a great risk to ongoing operations. It demands both immediate attention and a new, more effective approach to bridging the gap.

Bridging the z/OS Mainframe Performance and Capacity Skills Gap

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