This document provides an overview of artificial intelligence for IT operations (AIOps). It discusses how AIOps uses machine learning and analytics to help organizations better monitor and manage their IT infrastructure. Specifically, it notes that AIOps platforms ingest diverse infrastructure data, analyze it using statistics and machine learning, and apply what they learn to detect anomalies, understand relationships, and predict future behavior. The document also highlights that AIOps can help address long-standing challenges around setting SLAs, identifying potential problems, and planning infrastructure changes. Finally, it discusses how AIOps solutions must address mainframe and IBM i systems to provide a complete view of an organization's IT environment.

1. What Does Artificial Intelligence
Have to Do with IT Operations?
Ed Hallock

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3. Ed Hallock, President & CEO of ZERC Consulting Inc.
Ed Hallock is President & CEO of ZERC Consulting Inc., a software product
management and marketing consultancy. He is a highly experienced Information
Technology Professional with a broad experience base in software product
development, support, product management, marketing, and business development.
Prior to establishing ZERC Consulting, Ed benefited from working for some of the
largest independent software vendors, in a variety of roles, providing enterprise
solutions to Global 1000 corporations.
Ed has extensive experience in performance and availability management for systems
and applications. He holds a bachelor’s degree in Computer Science from Montclair
State University in Upper Montclair, New Jersey. In addition to published articles, Ed
has presented at numerous industry events as well as corporate related conferences
and seminars.
Today’s Presenter

4. • Question: How well is the IT infrastructure performing in
support of the business?
• Plethora of tools to detect, manage, and resolve problems
that are causing disruption of services:
• Domain-based performance monitors
• Network monitors
• Application monitors
• End-user response time measurement facilities
• The challenge remains: How to correlate information so that
business services can be accurately monitored and managed.
• And it’s not like we haven’t tried…
Why Are We Here?

5. The promise:
• Map IT components to the business services they supported
• Show how well those business services were running
• Cross-platform visibility and correlation
The failure:
• Complex in its make-up, and suffered from deficient underlying technologies
• Hard to create and update service definitions
• Rigid in how service data was collected and managed
• Relied on too few data sources for actionable business insight
• Weak integration between metrics from distributed platforms with mainframes
The Unfulfilled Promise of BSM

6. • Driven by the emergence of analytics platforms like Splunk and Elastic
• Organizations looking to change the approach to managing IT infrastructure
• Need to move away from platform specific tools requiring extensive domain expertise
• Analytics provide a multi-platform, cross-discipline, integrated view across the IT infrastructure
The Emergence of ITOA & Big Data Analytics
IT Operations Analytics (ITOA): ITOA is a market for solutions that bring advanced analytical
techniques to IT operations management use cases and data. ITOA solutions collect, store,
analyze, and visualize IT operations data from other applications and IT operations
management (ITOM) tools, enabling IT operations teams to perform faster root cause
analysis, triage, and problem resolution.

7. 3 Key Properties of AIOps:
• Ingest infrastructure and application availability, performance, and event data of different types across all IT platforms and components
• Analyze the data collected using statistics methods and provide visualization through reports and dashboards
• Apply machine learning algorithms to collected data to discover patterns, detect anomalies, understand relationships, and predict
future behavior
Ingest, Analyze, Learn!
Gartner Group predicts that by 2023, 30% of large enterprises will be using artificial intelligence for IT operations (AIOps)
Deliver Cross-Domain Analysis and Visibility With AIOps and Digital Experience Monitoring, Published 5 July 2018 (ID: G00352799)
What is Artificial Intelligence Ops (AIOps)?
Artificial Intelligence for IT Operations: Platforms that combine big data and machine learning
functionality to enhance and potentially replace a broad range of IT Operations processes and
tasks, including monitoring, event correlation and analysis, service management, and automation.

8. • Provide the capability to capture data from a variety of platforms and sources
• Normalize the data so it can be effectively correlated and easily visualized
• Include advanced search capabilities and extensive analysis
• In-depth visualization: basic reporting, event monitoring, and comprehensive dashboards
• Address the requirements IT organizations have dealt with in the past using different platform tools
Analytics Platform – A Critical Component of AIOps
 Network performance monitoring
 IT infrastructure monitoring
 Application performance monitoring
 Digital Experience Monitoring (DEM)
 Service Level Management (SLA) monitoring

9. Machine learning has come to be known as a discipline within computer science that uses
statistical techniques to give computer systems the ability to "learn" with data, without being
explicitly programmed.
Machine Learning – Completing the AIOps Solution
• Learning about, and from, the data to make data-driven decisions
• Used to quickly create, deploy and continuously monitor a high volume of analytic models
• Make better use of data and drive better outcomes
• The “artificial intelligence” that when combined with ITOA provides an AIOps platform

10. • Get a deep understanding of the impact that service degradation has on the components in the service stack
• Proactively organize and correlate relevant metrics and events according to the business services they support
• Detect anomalies and understand patterns in the behavior of the IT components and applications
• Predict future behavior with self-learning capabilities
• Simplify operations, prioritize problem resolution, and align IT with the business
Benefits of AIOps
Gartner Group predicts that by 2023, AIOps platforms will become the
prime tool for analysis of monitoring data and today's domain-specific
monitoring tools will feed their important data into AIOps for
consolidated, higher-level analysis.
Deliver Cross-Domain Analysis and Visibility With AIOps and Digital Experience Monitoring,
Published 5 July 2018 (ID: G00352799)

11. IBM z and IBM i
are critical to many
organizations
91%of executives predict long-term
viability of the mainframe as the
platform continues evolving to
meet digital business demands
>100kcompanies today use IBM i
technology to run significant
workloads & power critical
business applications
BMC 12th Annual Mainframe Research Results – Nov. 2017 Syncsort 2018 State of Resilience: The New IT Landscape for Executives:
Threats, Opportunities and Best Practices.” Jan. 2018
that’s 2,500,000,000 -- business
transactions per mainframe per day
2000+ organizations overall
2.5 B
92 of the World’s Largest
Banks, 10 of the world’s
largest insurers, and 23 of 25
of the largest U.S. retailers…
Depend on IBM
mainframes!

12. • IBM System Z® and IBM i® are a valuable source of intelligence
• Mission-critical platforms for many organizations
• System Z is the back-end for many web and mobile applications
• Provides high-volume transaction processing and database services
Addressing the IBM Gap

13. • Analytics platforms have for the most part ignored these systems
• Inability to include these platforms, became one of the primary
short-comings that killed BSM solutions
• Responsibility of gathering IBM system data sources pushed onto
other vendors to supply
• Require special technology from partner vendors
• Must understand the IBM system data sources
• Map, normalize, and transform the data into a format easily
ingested and understood by analytics platforms
Addressing the IBM Gap

14. Big Iron to Big
Data Analytics
Challenges
So many data sources
Mainframe:
Systems Management Facility (SMF),
Syslog, Log4j web and application logs,
RMF, RACF, USS files and standard
datasets
IBM i:
QAUD Journal, QHIST, Message Queues,
Operational Logs
Format of data
Mainframe:
• Complex data structures (SMF) with
headers, product sections, data
sections, variable length and self-
describing
• EBCDIC not recognized outside of
the mainframe world
• Binary flags and fields
IBM i:
• Complex data structures with
unique journal entry types, headers,
product sections, data sections,
variable length and self-describing
• IBM i journals in DB2
• Collection Services
• IBM i information needs to be
converted to workable formats such
as Syslog, CEF, JSON, etc.
Volume of data
Millions of records generated daily
Difficulty to get the
information in a timely
manner
• Not real-time, typically have to wait
overnight for an offload
• Typical daily FTP upload/downloads
can’t get granular

15. • Leading automatic forwarder of IBM z/OS® mainframe and IBM i log data to analytics platforms
• Enables organizations to analyze and visualize IBM system information in Splunk, Elastic, and other analytics platforms
• Gathers all the required data sources needed to fill the IBM systems gap in the IT analytics strategy
Syncsort Ironstream®

16. • AIOps is nothing to fear
• Just different (better?) technology to address long-standing
requirements
• It can help to…
• Effectively set Service Level Agreements (SLAs)
• Identify when potential problems might occur
• Help to plan for needed changes in the IT capacity or environment
• Machine learning and AI might seem scary… but it is what IT has
been asking for a long time
• The best way to understand how the infrastructure will perform is
to analyze past performance and learn from it
Summary

17. Thank You!
Ed Hallock
President & CEO, ZERC Consulting, Inc.
p 727-385-8115
ehallock@syncsort.com