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AIOps

2019 Predictions for AIOps

Many enterprises have found that Artificial Intelligence for IT operations (AIOps) platforms help them better manage their hybrid IT environment and improve system availability. Next-generation solutions – call it AIOps+ — now offer advanced capabilities such as automated pattern discovery and prediction, automated it infrastructure entity discovery and application mapping, and data and event correlation across the IT stacks. Based on these advanced features, we predict more firms will adopt AIOps+ in the coming year.

#1 – Enterprises will experiment with AIOps tools but will deploy AIOps+ solutions

While standalone AIOps tools can deliver useful capabilities such as anomaly detection and noise reduction across both legacy and digital environments, organizations may discover the promise of AIOps is difficult to achieve without understanding the business application context. Enter AIOps+ that works across the IT environment, ingesting and correlating massive amounts of data from a variety of sources, from business transactions to applications to it infrastructure. The full-stack data correlation delivered by AIOps+ will increase the accuracy of the machine learning predictions by enabling the ML algorithms to work on real-time, application specific events.

#2 – AIOps+ will help organizations succeed with digital transformations

While traditional IT environments can accept MTTR in the range of hours, modern organizations that are increasing their reliance on digital processes, will not survive unless they can depend on hybrid IT environments with significantly higher uptime performance. Imagine an on-line reservation system, a patient onboarding application, or an eCommerce site – what would be the business impact if they were down for hours or day? AIOps+ solutions provide the advanced real-time/predictive analytics needed for IT teams to make sense of vast sets of event data and drive faster IT incident response. By extracting meaningful insights from business transaction metrics, application flows and incident data, IT can quickly determine the root cause of an issue and even predict when the next outage will occur. Industry analyst Gartner sees an increasing role for artificial intelligence systems  in the next few years as IT departments struggle to support their sprawling digital environments with limited staff.

#3 – MSPs will start deploying AIOps+ to increase their revenues and efficiency

Given the pressing need for 24/7 system availability and reliability, AIOps+ is becoming the future of the agile, digital enterprise. Clients depend on their MSPs to meet their service level agreements (SLA’s) and maintain system availability at all time. However, as more mission-critical applications migrate to the cloud, the siloed, fragmented nature of distributed hybrid IT operations can hinder the incident diagnosis/resolution process. Performing root-case analysis and system diagnostics is complicated by several factors such as: where is the problem located and who owns it; is the issue related to an application or infrastructure failure; and how is the problem impacting system performance?  AIOps+ can provide the end-to-end visibility of distributed IT environment and real time data analysis that MSP’s need to automate root cause analysis and quickly resolve service performance issues.

#4 – Enterprises will appoint centralized teams to take responsibility for AIOps+ deployments

Today, very few organizations have a centralized team responsible for IT operations. Since today’s tools are siloed, IT organizations have structured themselves in the same manner with compute, network, applications and storage analysts each focused on their niche, each using a disparate tool. They still are not using cross-functional tools that correlate data across each silo. In isolation, it may not look as if there is an issue however when the data is correlated, issues emerge. With AIOps+ solutions, organizations gain access to powerful new capabilities. Building a centralized team ensures that the organizations gets a holistic view of the entire hybrid IT environment.

#5 – Consolidation in the AIOps market will start to occur, with those offering AIOps+ gaining ground

Many AIOps vendors have emerged in the market – and not all are worth the hype. Today, there are big and small AIOps providers with varying capabilities. As the AIOps market matures, we foresee a supplier shakeout, with those offering limited AIOps capabilities not surviving. Rather, we think successful vendors will be those offering robust AIOps+ solutions with advanced features such full stack correlation, agentless auto-discovery, automated remediation/restoration, data anomaly detection, and seamless integration with ITSM tools.

#6 – Traditional siloed ITOM/ITOA budgets will freeze and be reallocated to AIOps+

To keep up with the demands of digital business, organizations need to rethink their approach to managing silo hybrid IT environments and system maintenance. As the pain of operational volume, value, variety and velocity of big data analytics grows, organizations need an effective alternative to disparate application monitoring tools and siloed solutions. We predict forward-thinking organizations will reallocate budgets to AIOps+ solutions to better manage, plan, and troubleshoot issues across the entire IT infrastructure monitoring in real-time.

#7 – An ecosystem of solutions (built by SI/VAR/MSPs) will emerge to enrich existing AIOps+ platforms

The best AIOps solutions can seamlessly integrate with other IT assets, ERP applications, and multiple data sources. For example, FixStream’s new release integrates with ServiceNow and Cherwell ITSM/CMDB platforms, New Relic APM, SolarWinds and ManageEngine monitoring tools, etc. delivering functions such as auto-ticketing and change management, and creating an integrated solution greater than the sum of its parts. Taking it a step forward, we see a community of value-added resellers (VARs), system integrators (SI’s) and MSP’s emerging to create customized solutions for AIOps+ platforms that enhance its capabilities, perhaps to meet the needs of a specific industry or to incorporate advanced security features.

Conclusion

In 2019, we think more IT organizations will recognize how AIOps+ solutions can detect, predict and resolve business issues across an enterprise’s entire hybrid IT environment. For example, the latest AIOps+ offering from FixStream enables customers to detect patterns with 90%+ probability and reduce MTTR to just minutes, so IT operations can achieve appropriate service levels and meet customer expectations.

For more information on how FixStream AIOps+ can help you modernize IT ops this year, view our video.

Predict and Resolve Issues across Hybrid IT with AIOps

Virtual Systems require a new approach to IT Management

The need for business agility and 24×7 availability has driven IT systems to rapidly evolve, becoming more virtual, shared and dynamic. While this new virtualized, distributed approach to IT is optimal for digital business applications, it makes it harder to diagnose and resolve performance issues. When you have system components acting independently across siloes, Operations teams can struggle to monitor and analyze end-to-end system performance.

Without a clear view of the entire interconnected system environment and its various elements, your Operations team could be “flying blind”.  Just as air traffic controllers need the data provided by radar to safely navigate and direct various airplanes simultaneously, your IT team needs new tools that help them effectively identify, monitor and manage hybrid IT systems.

Improve the View with Auto Discovery and Application Mapping

Application performance can degrade for a multitude of reasons, along an entire path. Knowing the path traversed by each application is therefore critical to uncover outages. This requires a real-time, accurate auto-discovery process and application mapping.

Artificial Intelligence for IT Operations (AIOps) can help organizations achieve dynamic, real-time, accurate visibility across their hybrid IT environments. Using multi-layer correlation and predictive analytics across business KPI’s, application and infrastructure entities, AIOps can automate the discovery and mapping of critical business processes to application process flows, hosts, backend databases and infrastructure entities such as switches, routers and storage arrays. IT Leaders can visualize operations at a glance and application dependency details at every device level for operational or change management.

By automatically discovering and mapping your complex, multi-tier enterprise applications, you can always maintain an accurate and complete inventory of system components and application dependencies. For instance, Broadcom used AIOps to drastically increase the accuracy of their system components inventory. And, the information can be used to show the potential impact of system changes, ensuring that your transitions and migration plans don’t affect the performance of business-critical applications. Data center migrations can be completed up to 75% more quickly. (Watch this video illustrating the results achieved by Maxim Integrated)

Connecting the dots between business, application and infrastructure issues

IT teams face a daily deluge of data and alerts, making it difficult to discern which incidents need to be dealt with first. Operational data is typically siloed and decentralized, existing in disparate domains and stacks. IT team members often have to manually correlate and analyze thousands or millions of data points to resolve an issue.

Manual correlation is time-consuming and prone to error, and data – often repetitive and even irrelevant — quickly gets out-of-date. Analyzing and interpreting all that data into valuable, actionable insights can be a daunting, never-ending task.

Automated data correlation between business, application, and infrastructure would help, but often isn’t possible with outdated tools. If IT teams can’t analyze the data and isolate the problems quickly, organizations suffer system outages that last longer than necessary. This leads to dissatisfied customers, lost revenues, and unmet business needs.

Adding AIOps can deliver measurable value in key ITSM areas, such as ticket and event suppression and mean time to ticket resolution (MTTR). With AIOps, IT Operations can quickly identify and resolve system issues in minutes, not hours. As a result, your IT teams can become more productive, as they focus on projects that deliver business value versus chasing down noisy alerts. For instance, the City of Las Vegas uses AIOps to predict outages across any of their Oracle ERP applications, helping preserve their revenue flow.

Automated Anomaly Detection and Prediction Increases System Availability

Traditional tools are limited in their abilities because they provide domain-centric views without correlating events, thus impacting the visibility and the management of a hybrid cloud environment. During an IT incident, distributed applications make it difficult to track down where the problems are occurring.

Using machine learning techniques, AIOps correlates data about distributed applications and underlying infrastructure, making predictive analysis and efficient root cause analysis possible. The AIOps platform can learn and identify normal patterns of behavior for various system components and metrics. If the algorithms then detect anomalies, they can trigger alerts and actions by automation tools to identify and even fix basic problems. Over time, AIOps can use data patterns to predict potential incidents so they can be resolved before impacting system operations.

Achieve Proactive IT Management with AIOps

Traditional business models have been disrupted by digital transformation and new technology such as mobile and cloud. As enterprises launch more and more applications to support the user experience throughout the customer lifecycle, IT departments will struggle with infrastructure management and problem resolution.

AIOps solutions are expected to solve these challenges in a very elegant and effective way, revolutionizing IT Operations and accelerating digital transformation initiatives. Therefore, industry analysts are now predicting that the AIOps market will reach $11B by 2025.

FixStream AIOps brings together big data, machine learning and artificial intelligence techniques to transform IT operations. For instance, FixStream can predict outages with over 90% accuracy across very complex IT environments, reducing the noise by 40%. By providing a consolidated business-centric view of hybrid IT environments, FixStream AIOps solution helps IT teams proactively manage, plan, and troubleshoot revenue-impacting business-critical processes in real time.

For more information on how FixStream’s AIOps Platform can transform your IT operations, download our free book AIOps for Dummies.

How Integrating AIOps with ITSM will Modernize IT

By Enzo Signore

Introduction – The Importance of a Single Accurate View of IT Infrastructure

As organizations transform digitally, the systems and processes required to manage their dynamic, hybrid IT infrastructures often can’t keep pace. So, it’s natural for IT executives to want new solutions that help them understand and anticipate how different IT components will interact while delivering critical business applications. Not only is this understanding useful for effective system diagnostics, it’s critical for forecasting the impact of infrastructure changes.

Having an accurate and up-to-date view of what IT assets you have, where they are located, how they are configured, and the relationships existing between them are all vital functions for system management and business scaling. But building and maintaining a single accurate source of this information is problematic. In response, many IT departments have created a Configuration Management Database (CMDB), a database that contains all relevant information about IT assets installed across the IT environment, as well as data describing the relationship between specific items and how they interconnect.

Done right, the CMDB can provide a trusted source for information and a total up-to-date view of your IT configuration items, their attributes, and relationships. Done wrong, and the CMDB can end up with incorrect or redundant data, leading to ineffective decision making, inaccurate configurations, and duplication of effort. As with any database project, the analogy “garbage in, garbage out” is appropriate here and a main factor in CMDB projects failing. If the CMDB isn’t populated with accurate and timely information, and if that data isn’t kept clean, the CMDB project won’t deliver its potential value, wasting time and money. But without the right tools, this is a difficult task. No wonder that, by 2019, up to 90% of organizations are forecast to get their CMDB implementation wrong, according to Gartner.

The FixStream/Cherwell Software Partnership – Turning Information into Insight

In response, FixStream, a pioneer of Artificial Intelligence for IT Operations (AIOps), is working with Cherwell Software, a global leader in enterprise service management and CMDB software. Together, the two companies will deliver a next-generation integrated IT Operations solution that combines machine learning with workflow automation to help find, predict and resolve ITSM issues better and faster.

The joint solution, demonstrated at Cherwell Global Conference in Colorado Springs (Sept. 18-20), integrates FixStream’s AIOps solution with Cherwell’s workflow automation software to automate the discovery of entities across data-centers. Once the entities are identified, the FixStream solution will facilitate the ingestion of accurate inventory information into Cherwell’s CMDB, so the data stored there is correct and updated as needed.

How does the solution work? After creating an accurate and real-time inventory of the physical, logical, and virtual entity across the IT stack via an agent-less auto-discovery process, the FixStream software will automatically update the Cherwell CMDB. FixStream will also correlate business transactions with applications and infrastructure, visualizing operational issues in the context of the business applications. It will then apply machine learning to detect patterns, enabling IT teams to predict and prevent outages with over 80% probability across the entire system stack.

Armed with this deep insight, enterprises will be able to prioritize and proactively resolve issues and vulnerabilities across their IT infrastructure before they cause business outages and lost revenue. IT staff will be able to identify and visualize in seconds the sequence of correlated events across datacenters, as well as automate the root cause analysis of critical business processes, increasing Configuration Management Databases (CMDB) accuracy.

How will this integrated solution improve IT Operations? The ability for IT organizations to understand how applications, servers, middleware, databases, networking devices, and storage devices interact to deliver business services is critical for anticipating the impact of infrastructure changes. By improving the accuracy and currency of the database’s data, the FixStream-Cherwell joint solution will improve the diagnostic analysis capabilities of the CMDB itself. Using real-time insights and powerful analytics to more quickly and accurately identify issues across the IT infrastructure will allow customers to better monitor and maintain critical business processes and avoid expensive downtime.

Conclusion

The integration of FixStream and Cherwell technology will create an integrated next-generation IT Operations solution that will provide insight into the IT infrastructure’s topology and vulnerabilities. The integration solution is designed to help IT teams to prioritize incidents’ resolution by forecasting their potential impact on business operations with the goal of avoiding damaging business outages and lost revenue.

For more information about the FixStream and Cherwell integration, read our press release here.

Faster Problem Resolution with AI-Enabled Predictive Analytics

Outdated Technology and IT Complexity Hampers Issue Resolution

The exponential growth of real-time business transactions, coupled with dynamic hybrid and software-based technologies, make even routine IT operations difficult when using outdated domain-centric tools. As application environments become more complex and distributed, and change more frequently, there are more failure indicators generated than what can be investigated using traditional approaches.

Detecting and diagnosing system problems manually is time-consuming and cumbersome. Inadequate IT operations management tools complicate the identification, analysis and resolution of system issues, as IT specialists struggle to establish a clear, complete view of what’s happening across the various technologies. As a result, operations teams often lack the insight needed to proactively monitor, troubleshoot and manage system environments. This hampers IT’s ability to detect and resolve incidents when a system disruption occurs.

AIOps – Fixing IT Problems Before They Happen

Powered by machine learning, AIOps has advanced analytical capabilities to help IT organizations forecast and resolve system issues. Using its artificial intelligence capabilities, AIOps can be taught to observe and recognize patterns and anomalies over time. Then it will be able to automatically analyze massive amounts of digital data, correlate leading indicators, and use historical behavior to help predict what could happen next. Power of analytics combined with correlation across IT domains delivers contextual operational insight. The insight gained from reviewing the results can help your team predict and prevent business outages, even before they cause actual problems.

AIOps can be taught to examine data trends and provide an early warning whenever it discovers possible issues. For example, the application can detect trends and patterns within the “noise” of millions of system incident reports, helping analysts uncover potential risks and performance issues. By automatically applying proactive and predictive insights, AIOps can help IT organizations diagnose and resolve system threats and performance risks, forestalling a damaging outage. This increases system uptime and performance, driving better outcomes for the business.

When system outages do occur, the predictive insights from AIOps can speed up root-cause analysis and remediation. By quickly and accurately diagnosing the root cause, your team can find and fix problems faster, often reducing downtime from hours to just minutes. The result is optimized system availability and enhanced business operations.

Conclusions:

AIOps predictive analytics can provide up-to-date insight into your organization’s entire application and IT infrastructure resources, which can lead to a better understanding of the interdependencies between system components. This provides CIOs with valuable knowledge to better manage the increasing complexity and dynamic nature of their sprawling hybrid IT architectures.

The FixStream AIOps platform can quickly predict business application issues across an enterprise’s entire hybrid IT stack. Using its end-to-end view across all domains, the AIOps platform helps customers rapidly identify issues and predict outages, so they can save time and solve problems faster. For more information on how FixStream’s AIOps Platform can predict failure points across hybrid IT stacks, read our press release.

Getting a Handle on AIOps And Learning What These Platforms and Solutions Can Do for You

Getting a Handle on AIOps And Learning What These Platforms and Solutions Can Do For You

By Enzo Signore & Bishnu Nayak

As the headline suggests, we wrote this blog to inform readers like you about AIOps. The first question many of you probably have is: What the heck is AIOps?

Excellent question.

The simple answer is that AIOps stands for Artificial Intelligence for IT Operations. It’s the next generation of IT operations analytics or ITOA. And its value is in helping organizations address IT challenges on a number of fronts.

These challenges include:

  • The increasing complexity and dynamic nature of IT architectures
  • Digital business transformation
  • Siloed IT operations
  • Exponential data growth

All of the above render traditional, domain-centric monitoring and IT operations management inadequate. Such systems can’t correlate the onslaught of data various IT domains create. What’s more, they’re unable to provide insights IT operations teams need to proactively manage their environments. And that just won’t cut it.

AIOps solutions, however, can address these challenges. They enable enterprises to unify and modernize IT operations. And they allow enterprises to make the most of their existing network investments.

Let’s confront the above-noted IT challenges one at a time. Then we’ll explain how AIOps can help your business conquer them.

The Increasing Complexity and Dynamic Nature of IT Architectures

To increase business agility, IT organizations are deploying dynamic, modern IT architectures enabled by virtualization technologies. That includes containers, elastic clouds, microservices, and virtual machines.

At least a quarter of businesses had adopted containers by late 2017. The application container market was worth $762 million in 2016. By 2022 it will balloon to $2.7 billion. The use of cloud platforms is on the rise, as more businesses migrate more applications. By July 2018, 80 percent of all IT budgets will be committed to cloud solutions.

The dynamism these architectures and technologies enables is important for businesses. It helps them adjust to the fluctuating demands of millions of digital customers around the globe.

However, that often comes at the cost of decreased visibility. That’s because application workloads and flows are now abstracted from their physical infrastructure. And that creates new challenges in pinpointing potential issues.

So without end-to-end correlated data, adoption of these key technologies can be risky and cumbersome. Because IT staff will unable to effectively map current workloads to these new environments. And they’ll struggle to manage their performance and uptime. Plus, purchasing these new technologies can be extremely expensive, and AIOps can serve as insurance that organizations get maximum ROI from those investments.

“By 2022 the applications container market will be worth $2.7 billions”

Digital Business Transformation

Enterprises across the globe are leveraging digital technology to transform their businesses. Such efforts aim to provide better experiences to their prospects, customers, suppliers, and internal stakeholders.

To succeed as digital companies, businesses need to rethink their entire IT stack and operational strategy. And they need to ground these efforts with business-first considerations.

That should include how they think about application and network uptime.

Enterprises incur an average cost of $300,000 per outage. That’s if no revenue is at stake. If the outage impacts revenues, organizations lose an average of $72,000 per minute. That means companies lose a whopping $5.6 million per outage.

You can see why modern enterprises must make applications assurance and uptime their No. 1 objective. Those that don’t could face catastrophic damage to their revenues and reputation.

“Companies lose a whopping $5.6 million per outage.”

The Problem with Siloed IT

Research suggests 41 percent of enterprises use 10 or more tools for IT performance monitoring. Seventy percent use more than six. And you need even more tools to manage a hybrid cloud environment. That will include solutions to monitor workloads running in AWS, Azure, or multi-cloud environments.

Domain-centric tools provide a deep view into a specific domain. But they lack the ability to provide a correlated and end-to-end view across domains.

That’s a problem because cross-domain data collection, correlation, and visibility are key. They can enable you to track transaction problems like failed eCommerce orders to infrastructure issues like database timeout errors, for example.

But siloed management tools prevent most organizations from making these important connections. As a result, most enterprises suffer from very longer Mean Time To Repair intervals and unhappy customers.

MTTR averages 4.2 hours and wastes precious resources. Businesses employ an average of 5.8 full-time equivalent employees to address each incident. That FTE figure is as high as 11 in 15 percent of cases.

This drain of resources and finger pointing occurs as IT staff members struggle to manually correlate data. And often a whole lot of data is involved. Solving a critical business problem often entails using hundreds of data points – imagine how complex it becomes when IT is required to use thousands or millions of data points. That’s a lot.

“Mean time to repair averages 4.2 hours and wastes precious resources.”

The Challenge of Exponential Data Growth

Indeed, millions of data points are now flowing to the IT operations team in real time. This data deluge will only accelerate as adoption of containers, microservices, and virtualization grows.

And it’s growing big time. In the last 12 months, enterprises collected 88 percent more data than the prior year. Containers alone generate 18 times more data than traditional IT environments.

There are automated ways to collect and process this massive amount of data from an individual domain, but domain specific teams then need to manually correlate it. (And 79 percent of organizations reportadding more IT staff to address this problem is not an effective strategy.) This is not only time consuming but also prone to incorrect interpretation and results, requiring skilled resources from different domains, thus leading to a very long diagnostic process for root cause identification.

“Containers alone generate 18 times more data than traditional IT environments.”

To address these challenges, organizations need a new class of technology to modernize the IT operations process. This technology needs to be able to correlate millions of data points across all IT domains. It should have the smarts to apply machine learning to detect patterns. And it should present that information so organizations can easily see what’s happening and gain insights.

This technology is what we mean when we talk about AIOps.


AIOps Defined

Gartner recognizes AIOps as a new strategic IT segment.

Artificial intelligence for IT operations (AIOps) platforms are software systems that combine big data and AI or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation,” (Gartner – “Market Guide for AIOps Platforms” – Will Cappelli, Colin Fletcher, Pankaj Prasad. Published: 3 August 2017)

Figure 1: Gartner’s visualization of the AIOPS platform

AIOps Platform Enabling Continuous Insights Across IT Operations Management

The general process by which AIOps platforms and solutions operate includes three basic steps.

Observe

An AIOps platform first needs to observe the nature of data and its behavior. That involves collecting information through data discovery.

AIOps data discovery needs to support big data scale. That way it can address the volume of data from different IT domains and sources. Those sources may include legacy infrastructure or new container, hybrid cloud, or virtualized environment elements.

Whatever the data or source, speed is key to the observation part of the process. So the data must be collected in near real time to detect patterns. Performance- and health-related information is collected from hundreds of sources – using an agentless or agent model. Successful AIOps platforms leverage a combination of mechanisms to collect data from a multi-domain and multi-vendor environment. That environment may include an array of containers, hypervisors, network and storage solutions, public cloud, and other technologies and architectures.

A successful AIOps platform also combines the power of big data and machine learning with domain knowledge to identify data relationships and history to solve this complex problem.

Engage

An AIOps platform provides orchestration across key IT operations domains – most importantly IT Service Management.

ITSM activities such as change management and incident management have traditionally been manual. And they’re typically heavily dependent upon the Configuration Management Database. The problem with legacy CMDBs is they are highly unreliable for environments involving frequent change.

The AIOps platform provides analytics and input to make ITSM tasks more automated and reliable. For example, AIOps can update CMDBs using its knowledge of the environment, state, and changes. The AIOps platform’s ability to observe hybrid environments on an end-to-end basis provides this power. That ensures CMDB data is relevant and reliable. That allows for automation and faster and more accurate incident management. The automation also minimizes risks that might otherwise happen due to human error. And pattern recognition allows businesses to see and address problems before they affect end-user experiences.

Act

Automation or closed loop functions is the nirvana of AIOps platform.

Of course, automating critical IT operations using machine learning is new territory for most organizations. And IT leadership will need to get comfortable with it before they fully embrace automation. But new state-of-the-art automation – which uses advanced human inputs and machine learning – is maturing. And organizations can employ it today to do both simple and more complex jobs.

For example, they can employ it to clean log files to free up space. And they can use it to restart an application. Automation also can change application traffic policy on a router if AIOps sees the need.


How and Where AIOps Delivers Value

Enterprises that have deployed AIOps solutions have experienced transformational benefits. They include revenue growth, better customer retention, improved customer experience, lower costs, and enhanced performance.

Their operational teams have been able to:

  • Increase end-to-end business application assurance and uptime
    • Manage an integrated set of business and operational metrics
    • Predict and prevent outages
    • Dramatically reduce Mean Time to Detect and Mean Time to Repair
    • Lower the number of IT FTEs dedicated to troubleshooting
    • Decrease operational noise and alerts
  • Optimize IT and reduce IT costs
    • Replace older, silo-focused IT monitoring tools
    • Auto-discover complex, heterogeneous topologies
    • Gain visibility into the hybrid IT environment
    • Accelerate migration to the hybrid cloud
    • Expedite the adoption of hyper-convergence and microservices architecture
    • Reduce risk in consolidating and migrating data centers
  • Free up resources to enable IT operations to become a proactive source of innovation
    • Automate and reduce the cost of audits and compliance
    • Simplify IT processes
    • Break down silos across their IT teams
    • Enable less experienced staff to become more productive, faster

What the AIOps Architecture Looks Like

An AIOps solution includes the following functional blocks:

We’ll address these building blocks from the bottom up because that’s how AIOps itself works.

Open Data Ingestion

An AIOps platform collects data of all types from various sources. That may include data on faults, logs, performance alerts, and tickets. The ability to ingest data from the most diverse data sources is critical. It allows for an accurate, real-time view of all the moving parts across hybrid IT environments. More about open data ingestion here.

Auto-discovery

Given the very dynamic nature of modern IT environments, businesses need an auto-discovery process. That automatically collects data across all infrastructure and application domains – including on-premises, virtualized, and cloud deployments. And it identifies all infrastructure devices, the running applications, and the resulting business transactions. Read the auto-discovery blog.

Correlation

Then it’s time for the AIOps platform to correlate this data in a contextual form. So it needs to determine the relationships between infrastructure elements, between an application and its infrastructure, and between the business transactions and the applications.

To learn more about the importance of correlation, check out this blog.

Visualization

Once the end-to-end correlation process is completed, data need to be presented in an easy-to-use format. And that’s what visualization is all about.

Visualization is important because allows IT operations to quickly pinpoint issues and take corrective actions.

Of course, visualization in IT operations has become a commodity. Every solution includes a dashboard of some type. Yet an estimated 71 percent of organizations say data is not actionable. That’s why AIOps is important. It provides a new generation of visualization that makes data actionable.

Because visualization is key, we’ve also put together a blog on this topic. You can find it here.

Machine learning

Finding the root cause of a problem is key. But it’s even more critical to determine recurring patterns and predict likely future events.

AIOps solutions use supervised and unsupervised machine learning to determine patterns of events in a time-series. They also detect anomalies from expected behaviors and thresholds and predict outages and performance issues. Learn more about machine learning here.

Automation

Automation is a key component of AIOps as it delivers the end ROI to the customer. It does so by automating human IT ops tasks, reducing significant OPEX, and expediting innovation. And it reduces MTTR and can improve customer satisfaction.

AIOps enables IT operations to modernize existing processes. It allows IT Operations to make progress vs traditional ITOA strategies, abandon old, reactive processes, and become proactive, by predicting issues and preventing outages.

By providing an end-to-end correlated view of the entire IT environment, AIOps allows enterprises to accelerate their digital transformation strategies, adopt new technologies faster, and increase business productivity.

To learn more about FixStream, check out our AIOps solution whitepaper.

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