Cloud Computing Complicates Enterprise IT Operations

Cloud computing at least indirectly is creating complexity for enterprise information technology managers, as a single web or mobile transaction now crosses an average of 37 different technology systems or components, a survey by Dynatrace finds. 

About 76 percent of IT professionals surveyed by Dynatrace say they do not have complete cloud application performance visibility.

That is one problem AIOps hopes to alleviate, providing greater application performance while reducing monitoring overhead. 

source: Dynatrace

AIOps Value and Application

The value of AIOps--as noted by Gartner, includes the ability to:

• Reduce noise (false alarms)
• Determine causality, identifying the probable cause of incidents
• Capture anomalies
• Detect trends that may result in outages before their impact is felt
• Drive the automation of low-risk to medium-risk recurring tasks
• Improve user effectiveness and automation using chatbots and virtual support assistants (VSAs) 
• Triage problems

Gartner groups AIOps solutions into three buckets:

• Domain-agnostic AIOps — Vendors going to market with a general-purpose AIOps platform. These products tend to rely mostly on monitoring tools to perform data capture and cater to the broadest use cases.
• Domain-centric AIOps — Vendors that have the key components, but with a restricted set of use cases. They essentially do the same thing they did before but now they’re replacing rules, heuristics and fingerprints with math (algorithms). These vendors are focused on one domain (for example, network, endpoint systems or APM). However, there have been some efforts by domain-centric solutions to hybridize these categories and evolve to ingesting data from sources other than their own instrumentation tools and including this data in their analysis.
• Do-it-yourself (DIY) — Some open-source projects enable users to assemble their own AIOps platforms by offering tools for data ingest, a big data platform, ML and a visualization layer. End users can mix and match the components from multiple providers. 

Illustrative suppliers of domain-agnostic AIOps include firms such as:

Some enterprises actively build AIOps platforms by putting together all the required layers starting with streaming to acquire data (using Prometheus, for example), followed by aggregation (in InfluxData’s InfluxDB, for example) and a visualization tool (such as Grafana or Elastic Kibana), Gartner says. 

Some advanced adopters of do-it-yourself  AIOps platforms have built solutions that analyze the confidence level of their deployments in order to gauge risk, predict customer churn, and detect and automatically resolve problems before they have business impact. However, these deployments are in the minority, due to the skills needed to support them, maintenance requirements and support, Gartner adds. 

Domain-centric AIOps suppliers are further delineated by Gartner into those in the information technology service management (ITSM) space, others in the DevOps space and some in the Network Performance Monitoring and Diagnostics  (NPMD) or application performance monitoring (APM) segments of the market. 

Use of AI in IT operations has been driven by the adoption of digital transformation and the resultant need to address the following:

• Rapid growth in data volumes generated by the IT systems, networks and applications
• Increasing data variety with the need to analyze events, metrics, traces (transactions), wire data, network flow data, streaming telemetry data, customer sentiment and more
• The increasing velocity at which data is generated, as well as the increasing rate of change within IT architectures and challenges in maintaining observability and improving engagement due to the adoption of cloud-native and ephemeral architectures
• The need to intelligently and adaptively automate recurring tasks and predict change success and SLA failure
• Use of AIOps platforms to augment IT functions such as event correlation and analysis, anomaly detection, root cause analysis and natural language processing is growing rapidly. However, use of AIOps for functions such as ITSM and DevOps is progressing at a slower pace.
• AIOps platform offerings have split into two approaches: domain-agnostic and domain-centric solutions.
• Enterprises that adopt AIOps platforms use them as a force multiplier for monitoring tools correlating across application performance monitoring, IT infrastructure monitoring, network performance monitoring and diagnostics tools, and digital experience monitoring.
• AIOps platform maturity, IT skills and operations maturity are the chief inhibitors to rapid time to value. Other emerging challenges for advanced deployments include data quality and lack of data science skills.

Asia Data Centers Adopted Artificial Intelligence to Deal with Operating Cost

Data center owners and operators are concerned about high operating costs as much as any other enterprises, with 61 percent of surveyed Asia Pacific data center professionals saying they have adopted artificial intelligence for operations, according to a survey by Data Center Dynamics.

AI or machine-based analysis and application technologies are reported used by 15 percent to 25 percent of respondents, roughly the same percentages that report having adopted big data analytics, internet of things or software-defined infrastructure. 

source: Data Center Dynamics 

High operating costs were the most-frequently-mentioned reasons for using AI in the data center. 

source: Data Center Dynamics

Fully Autonomous Behavior? Not Many are That Comfortable, So Far

It probably will be a while before most information technology staffs would be comfortable operating at stage 5 of this AIOps maturity model. “Fully autonomous” behavior is not going to be approved until organizations and staffs get much more familiar with fully-developed AIOps capabilities. 

And even then, many will probably add in some human oversight, for some autonomous functions. 

source: DZone

51% Say Their Organizations Log 1 Million to 10 Million Events Each Day

About 26 percent of respondents to a poll sponsored by AIOps Exchange say they use 50 or more different monitoring tools in their enterprise. Some 40 percent reported that they log more than a million events every day.

Also, 11 percent of respondents say their organizations log more than 10 million events per day.  

The biggest benefit from AiOps was customer satisfaction, the top value listed by half of respondents. 

source: AIOps Exchange

How Gartner Sees AIOps Evolving

Gartner anticipates that, over the next five years, wide-scope AIOps platforms will become the de facto form-factor for the delivery of AIOps functionality as opposed to AIOps functionality embedded in a monitoring tool.

Early on, the emphasis has been on ways to:

• Reduce noise (for example, in the form of false alarms or redundant events)
• Provide better causality, which helps identify probable cause of incidents
• Capture anomalies that go beyond static thresholds to proactively detect abnormal conditions
• Extrapolate future events to prevent potential breakdowns
• Initiate action to resolve a problem (either directly or via integration)

But the ability to “act” is the future. AIOps analysis is expanding beyond its initial usage as a better solution for event correlation and analysis in IT operations, Gartner argues. 

source: Gartner

Gartner believes that AIOps will evolve into a bidirectional solution that not only ingests data for analysis, but also initiates actions based on its analysis, including:

• Alerting
• Problem triage
• CMDB population
• Run book automation
• Application release orchestration

source: Gartner

Perhaps the fullest development of AIOps will come when pattern detection is used to detect and then alter organization behavior with respect to customer experience. In that sense, AIOps will expand to include:

• a pattern detection algorithm to improve the customer relationship process by detecting the patterns of behavior customers expressed in digital experience monitoring data. Use the machine learning algorithms in AIOps to perceive the patterns that relate user navigation with:

• Digital experience data from APM
• Order data pulled from payloads in business transactions
• Sentiment data from social media
• Service desk requests and statuses
• Account activity from the CRM system

AIOps Without Automation is Unthinkable

The notion that AIOps could possibly work without representing much-greater degrees of autonomous behavior is likely silly, as the whole point is to automate and correlate process information from many sources, leading to faster fault isolation and repair.

source: 451 Research

Anunta Tech AIOps platform available for VMware Cloud on AWS

Anunta Tech  says its software as a service based AIOps platform, EuVantage® is now available to customers of VMware Cloud on AWS. 

The company says VMware Cloud on AWS brings together VMware's enterprise-class Software-Defined Data Center software and elastic, bare-metal infrastructure from Amazon Web Services to give organizations consistent operating model and application mobility for private and public cloud. 

EuVantage delivers analytics-based end-user-centric cross-domain visibility and correlation to customers of VMware Cloud on AWS, Anunta Tech says. 

Its unique differentiator is its analytics, the cross-domain visibility and correlation it generates and the overall end-user-centric approach, which results in up to 70 percent reduction in Mean-Time-To-Resolution.

42% of IT Professionals Say They Use More than 10 Monitoring Systems

In a recent survey on demand and use of AIOps, about 27 percent of information technology professionals surveyed said they had “never heard of the term” according to Big Panda. 

Some 42 percent of respondents reported using more than 10 monitoring tools, while 19 percent of respondents said their organizations used more than 25 separate monitoring tools. Most observers say the time it takes to isolate and fix alarm conditions is complex because so many different monitoring systems and alarms are routinely generated. 

source: Big Panda

source: Big Panda

AIOps Wrings Value from Full Packet Capture

Cheap computation and storage mean many impossible operations have become practical. Use of millimeter wave frequencies for commercial end user communications is among the salient examples, but enterprise ability to capture and process nearly all data generated by the enterprise, for the purpose of wringing insights out of that data provides another example. 

According to Cisco, companies can expect to see their network traffic triple by 2022. “This will require organizations to make a proportional increase in data storage and maintain a brute force, record-everything approach for network forensics that will cost companies significantly more in terms of time and money,” says Randy Caldejon, CounterFlow CEO. 

AIOps plays a key role here, he argues. “Full packet capture is finally entering the age of practicality because of the introduction of AIOps,” he argues. 

“Thanks to AIOps, security analysts now have an opportunity to utilize more open source technologies and experiment with ML and AI to make packet capture work better for them and their organization,” he says. “Before, it was unrealistic to expect a group of analysts in a security operations center to proactively ferret through petabytes of data in search of an anomaly.”

Gartner defines AIOps as the application of machine learning (ML) and data science to IT operations problems. The firm also predicts that large enterprises use of AIOps tools will reach 30 percent by 2023.

Why AIOps Might Help IT Operations Improve

A new Digital Enterprise Journal (DEJ) study, The Roadmap to Becoming a Top Performing Organization in Managing IT Operations, finds that top-performing information technology organizations outperform others in large part because they are able to detect performance issues faster, resolve issues faster and create new products faster, at lower cost. 

Top-performing organizations can proactively detect 79 percent of performance issues ahead of time while all other organizations are only able to detect 39 percent of performance problems. 

The average mean time to incident resolution for TPOs is 38 minutes while it takes five times more time (224 minutes) for all other organizations.

Top performers also can deliver innovative products and services at a faster pace (5.1 times faster release velocity as compared to all other organizations) and do so at a lower cost (4.2 times more end-users supported per IT full-time employees.

source: OpsRamp

And that is where AIOps plays a role, improving pattern recognition and anomaly detection, therefore reducing the amount of time before performance issues are identified and remedied. 

source: OpsRamp

AIOps Market Growth Looks a Lot Like IT Ops Growth

This 2026 forecast for the information technology operations market looks suspiciously like projections for AIOps. Probably because most of the market is repurposed IT ops revenue, with AI features added. 

source: Zion Research

So here's one forecast for AIOps. 

source: Maximize Market Research

And here's another. 

source: Garner Insights

And another. 

source: Coherent Market Insights

AIOps Platform Market Forecasts are Plentiful, but Are They Accurate?

There is no shortage of AIOps market studies, forecasts and estimates. The question some of us might have is whether AIOps revenue is actually incremental new revenue or simply some of that and a rebranding of legacy IT ops solutions. Personally, I'd lean towards the latter interpretation.

The AIOps platform market will see a 26 percent on a compound annual growth rate to 2026, according to Coherent Market Insights. How much of that growth is shifted from existing enterprise information technology or analytics spending budgets is not so clear. 

source: Coherent Market Insights

Other researchers see sales growth on the order of 25 percent CAGR until 2025, or as much as 34 percent, to reach $18.51 billion, by 2026, according to Data Bridge Market Research. 

Platform revenue is expected to be about $1.76 billion at the moment, according to Data Bridge.

AIOps is about Analytics Driving Insight

You still can get an argument about whether the primary purpose of AIOps is insight or action, but all agree inferences and insight are essential to the value proposition. 

source: OpsRamp

Gartner predicts larger enterprises’ use of AIOps and digital experience monitoring tools for monitoring applications and infrastructure will rise from five percent in 2018 to 30 percent in 2023. 

The AIOps analytics market represented sales of about $2 billion in 2016, according to IDC. 

source: IDC

Automation, which is critical to running AIOps smoothly and efficiently, helps drive AIOps to perform, according to CIO:

• Automated monitoring: discover the full environment and identify when new endpoints, such as a virtual server or machine, a new mobile device, or even a new cloud platform, are brought up
• Automated AIOps: run AIOps while adhering to established policies and dependency mapping and requiring no advance configuration
• Automated remediation: quickly and efficiently execute the necessary steps to resolve any fault or performance events

AIOps helps enterprises consolidate and analyze infrastructure operational data coming at them at a rapidly increasing pace from myriad sources, says Micro Focus. 

It can reduce the overall volume of potentially damaging events, provide alerts to conditions that could cause an outage, isolate the cause of those events, and apply process automation to remediate events, Micro Focus argues.