Emma Mak • Staff Writer

Healthcare organizations are up against it. They must deliver high-quality patient care, ensure compliance with regulations, manage complex IT infrastructures, and cope with internal and external stakeholders’ increasing demands and expectations.  

Two powerful technologies can help: Artificial Intelligence for IT Operations (AIOps) and Digital Experience Management (XM) enable organizations to optimize IT operations and enhance Digital Employee Experience (DEX). Combined, they’re a remedy for what ails many healthcare organizations — inefficient systems and staff burnout — ultimately leading to improved patient outcomes. 

How to Get Started with AIOps and XM  

As with all digital transformation initiatives, successful implementation of AIOps and XM requires a strategic and systematic approach. Here are some steps to follow:

1. Assess your current state: Evaluate your IT maturity, capabilities, and challenges, and identify your goals and priorities for AIOps and XM.
2. Choose the right tools: Select the best AIOps and XM solutions and vendors that suit your needs and budget and can integrate well with your existing IT environment and processes.
3. Implement and optimize: Deploy and configure your AIOps and XM solutions and monitor and measure their performance and impact. Continuously refine and improve your AIOps and XM strategies and practices based on data and feedback.

These steps can be challenging for healthcare organizations on their own. Implementing AIOps and XM is more complex than purchasing software. A managed services provider (MSP) with expertise in implementing and integrating toolsets within customers’ systems can be a great partner throughout the process, helping you gain the best ROI. 

AIOps and XM Defined 

AIOps is a set of tools and techniques that use AI, machine learning, and automation to analyze, monitor, and manage IT systems and processes. Implemented correctly, it can help IT teams reduce complexity, increase efficiency, and resolve issues faster and more proactively.

XM is a holistic approach that measures and improves how employees interact with and perceive the digital tools and systems they use daily. This helps IT teams provide seamless, user-friendly, and personalized experiences that boost employee engagement, productivity, and retention. 

By using AIOps to group related alerts using event correlation, the healthcare organization successfully reduces alert fatigue and improves incident management for their EHR system.

AIOps + XM Benefits: Monitoring and Improving IT Infrastructure Health

By combining AIOps and XM, healthcare organizations can achieve the following benefits:

• Reduced downtime, cost, and risk: AIOps can help IT teams prevent, detect, and resolve incidents that affect the availability and performance of critical IT systems and applications, such as electronic health records, telehealth platforms, and medical devices. It can also help IT teams optimize resources, reduce operational costs, and comply with security and privacy regulations.
• Improved employee satisfaction and performance: XM can help IT teams understand which digital processes are slowing down and frustrating doctors, nurses, administrators, and researchers. By enabling the collection and analysis of staff feedback, sentiment, and tool usage data, IT teams can identify and address pain points, bottlenecks, and opportunities for improvement.
• Enhanced patient care and outcomes: Together, AIOps and XM ensure healthcare IT systems and applications are reliable, secure, and user-friendly, helping staff become less stressed and more productive and efficient. This leads to better patient satisfaction, safety, and quality of care, as well as reduced errors, costs, and risks. 

Real-World Examples 

AIOps

• Challenge: A hospital’s IT team is inundated with alerts when the EHR system experiences a network outage that affects multiple servers and applications. Meanwhile, an incident that affects patient privacy is lost in the alert noise.
• Solution: AIOps helps IT teams cut through the noise. In this case, it would have grouped all the alerts related to the network outage into one incident and prioritized the data breach incident affecting patient privacy, assigning it critical status so it could be resolved quickly.

XM

• Challenge: A nurse needs to access patient electronic health records (EHR) on her tablet. She’s having trouble— the app is slow and unresponsive, and she must wait to view each patient’s information. It’s the end of her shift; it’s been a difficult day, and she’s exhausted and frustrated.
• Solution: Using XM, the IT team would have been monitoring the performance and reliability of the EHR app, proactively identifying and resolving any issues that could affect user experience. While her day full of caring for patients would still have been challenging, her experiences with technology would have helped maximize her efficiency in caring for her patients rather than adding to her burden. 

Compucom’s Matrix Platform  

We’ve designed our comprehensive portfolio of IT services around user experience and XM, and we’re an early adopter of AIOps tools and techniques. Our experience and automation platform — Matrix — combines AIOps and XM to drive transformation, innovation, and continuous process improvement throughout your IT environment, freeing up time for your IT team to focus on other critical initiatives that can further improve patient care. 

The post Optimizing Healthcare IT Infrastructure with AI appeared first on Compucom.

Emma Mak • Staff Writer

Digital technologies such as cloud computing, big data, artificial intelligence (AI), and the Internet of Things (IoT) have unleashed enticing new possibilities for businesses. But they have also made enterprise IT infrastructures increasingly complex, especially with hybrid and multi-cloud environments.  

What’s the answer to this complexity? It’s Digital Experience Management (XM) and Artificial Intelligence for IT Operations (AIOps). The effective implementation of these toolsets can help tremendously reduce technical complexity, allowing companies to get a better handle on the IT aspect of their business. 

AIOps: Anomaly Detection for Better Troubleshooting

XM is all about the users — it measures and improves how employees interact and perceive the digital tools and systems they work with daily, helping companies provide seamless, user-friendly experiences. When companies invest in XM, they’re prioritizing and promoting employee engagement, productivity, and retention.

Ideally, XM should collect useful user data, such as endpoint telemetry, service and support metrics, event sensors, targeted surveys, and user sentiment analysis. Then, performance indicators should be generated, which are analyzed against the dimensions of a successful digital workplace – technology-enabled workers, self-sufficient workers, well-supported workers, and workplace flexibility.

AIOps: Event Correlation to Lower Alert Fatigue 

AIOps, on the other hand, is focused on IT infrastructure. It helps IT teams improve the efficiency and reliability of their company’s IT management and processes. The ongoing goals are reduced downtime, costs, and risk.

By automating and streamlining IT tasks and helping collect and organize the massive amounts of data generated by IT Operations, AIOps frees up your IT talent and boosts their capabilities for smarter and faster incident resolution, root cause analysis, and capacity planning.

Compucom’s Matrix platform: XM + AIOps 

XM and AIOps use AI and automation, but they have different goals and perspectives. Both technologies are complementary and important for achieving digital transformation and organizational success.  

Compucom’s experience and automation platform, Matrix, combines XM and AIOps. We’ve made Matrix central to our services portfolio, providing smart insights and improvements for our managed services to deliver better user digital experiences and optimized IT Operations. 

We Focus on Your Business Outcomes  

While we are an IT services provider, our customers understand that we’re not just there to help them with technology. Our customers, many of them long-term, benefit from our focus on and understanding of their business.

As Brent Asbil illustrates, “You may have an issue, for example, at a bank branch, that possibly spans the networking and security towers. By combining different aspects together to create a business view within our AIOps toolset, we can create visibility into all the infrastructure that matters for a branch location. We can help customers bring those towers together to solve the issue swiftly and effectively.”

This is also true of our XM solution; we encourage our customers to focus XM efforts on the tools and systems most closely linked to desired business outcomes so that they’ll reap the most benefits.

Our Matrix experience and automation platform helps you leverage the latest digital technologies without getting bogged down by technical complexity, setting you up for success and resulting in optimized IT operations and an improved digital employee experience. 

The post Transforming Operations: Harnessing the Power of AIOps and Digital Experience Management appeared first on Compucom.

Emma Mak • Staff Writer

If you’re reading this, perhaps you’re trying to figure out if Artificial Intelligence for IT Operations (AIOps) could help your company, or maybe you’re just brushing up on the latest IT lingo with a black coffee or latte in your hand. Either way, you will come away with some real-life examples of how AIOps can — more than humanly possible — help IT teams improve the efficiency and lower costs of their IT operations. 

AIOps: Anomaly Detection for Better Troubleshooting

Today’s complex IT environments make monitoring very noisy, with frequent or irrelevant alerts crowding out the most important ones. Anomaly detection uses machine learning algorithms to identify patterns and trends in data and detect deviations from normal behavior. This means monitoring can more easily adapt to seasonal or cyclical variation without manual tuning to avoid false positives or negatives.

A significant advantage of anomaly detection is that it can help you discover unknown or hidden issues you may not have anticipated or defined thresholds for, enabling proactive action before users are impacted.  

Example

A retail business implements AIOps for more proactive troubleshooting. Normal operational baselines are built, accounting for spikes in ordering patterns during seasonal changes.

One day, AIOps detects an increase in average response time for a crucial ordering application, indicating a spike in demand outside the expectations for the time of year. Happily, stakeholders identify the likely cause as the introduction of a new line of unexpectedly and wildly popular products – they’re riding the latest TikTok fad!  

Since AIOps is trained in handling an increase in usage corresponding to seasonal changes, it recommends an automation to create new instances of the application so that ordering processes are not impacted. Based on knowledge of the organization’s topology, AIOps also provides operators with details of this remediation for cohort devices and applications so they can proactively ensure the unexpected spike will be handled smoothly. 

AIOps: Event Correlation to Lower Alert Fatigue 

Even the most dedicated system or network administrator will learn to tune out alerts if too many have turned out to be false alarms.  

AIOps uses machine learning algorithms to analyze the alerts from different sources and find the patterns and dependencies among them. It then groups related alerts based on common attributes, such as time, location, source, or type, and filters out irrelevant or false alerts based on predefined thresholds. Then, natural language processing generates meaningful incidents that describe the issues’ nature, severity, and impact.

Example

A healthcare organization has a cloud-based electronic health record (EHR) system monitored by various tools for performance, availability, security, and compliance. However, many of the alerts are redundant or irrelevant.  

AIOps helps their IT team:

• Group the alerts – for example, if the EHR system experiences a network outage that affects multiple servers and applications, AIOps groups all the alerts related to the network outage into one incident.
• Filter out irrelevant alerts, such as those expected due to routine maintenance or testing activities for the EHR system.
• Prioritize incidents based on their urgency, importance, or business impact. If the EHR system has some incidents that affect patient safety or privacy, such as data loss or breach, AIOps prioritizes these incidents and assigns them a critical status.

By using AIOps to group related alerts using event correlation, the healthcare organization successfully reduces alert fatigue and improves incident management for their EHR system.

AIOps: Faster and More Accurate Root Cause Analysis (RCA)

Getting to the root cause of a performance issue can take up a lot of time, especially when teams are siloed and have limited visibility into the complete picture.

AIOps augment teams’ abilities to find the source of an issue and collaborate to speed up Mean Time to Resolution (MTTR). By leveraging AIOps to detect the pattern of impact from an event, operators can use events and their root causes as modeled “fingerprints” within the time series data and logs, speeding up AIOps’ ability to recognize and resolve incidents.

Example

A government organization implements AIOps, hoping to reduce the number of and increase the quality of generated service desk tickets:

1. Monitoring tools pick up a recurring CPU spike on a server at 2 AM every morning.
2. AIOps generates a ticket each time, but after checking for signs of the spike an hour later, closes the ticket with no known cause.
3. During Problem Management processes, an operator notes the recurring tickets and creates an automation to query the device as soon as the CPU spike is detected, taking a snapshot of running processes.
4. The operator identifies the pattern; an antivirus process runs daily on the server at 2 AM. 
5. The operator trains AIOps that, before creating a CPU spike ticket for the server, it should check to see if it’s just the antivirus process running.
6. This remediation is suggested to operators when other incidents match the fingerprint.

Get a Handle on Your  IT Operations 

In our five-part AIOps series, we’ve explained what it is, how it works (part 1 and part 2), and offered advice on how to go beyond the hype to get its full advantages (hint: it’s all about the proper integration of toolsets with your IT systems). Finally, we’ve concluded with some real-world examples of its benefits.

To optimize IT operations, your IT team needs to understand the big picture by correlating metrics, events, and logs and then connecting the dots to figure out solutions. AIOps gives them automation and advanced tools to help them achieve that.

Partner with a provider with real-world experience, like Compucom, and go beyond the buzzword to truly effective AIOps. 

In this series:

• The Big Deal About AIOps
• How AIOps Works: Tame Big Data and Get to the Crux of the Matter
• How AIOps Works: Continually Smarter & More Effective IT Operations
• AIOps: Going Beyond the Buzzword

The post Real-World AIOps: Examples and Benefits appeared first on Compucom.

You don’t see much about Artificial Intelligence for IT Operations (AIOps) in today’s headlines. News about AI leans toward its more controversial capabilities to create content and (creepy) “art.” And the world wonders, is this what we want from AI?

When the camera was invented in the 1800s, a critic decried photography as “the mortal enemy of all art,” echoing fears of contemporary painters that it would make them irrelevant. Luckily, that was far from what happened — photography inspired Impressionism and the modern art movement.

Just like photography spurred artists to new levels of creativity, AI is at its best when it builds on our human capabilities — freeing us from drudgery and allowing us more time for creative, challenging, and fulfilling work. AIOps, isn’t intended to displace IT teams but rather help them better cope with the significant issues they face today:

• Sorting through overwhelming infrastructure data from many sources
• Ever-present and escalating cyber threats
• An ongoing shortage of IT talent 

Successful AIOps Hinge on Integrating the Right Toolsets 

This blog is part two of our more in-depth look at how AIOps works. As we discussed previously, the key to a successful implementation is integrating the right toolsets with your infrastructure to enable three main functionalities:

1. Data ingestion from across IT domains
2. Analysis, correlation, and recommendations 
3. Fixing the issue (through automated and/or human intervention) and proactive remediation

In this blog, we’ll take a closer look at the final part of the process in an advanced AIOps implementation: fixing the issue.  

Enable Your IT Teams with Automated Remediation and Better Insights

Automated Remediation

It’s possible for AIOps implementations only to provide monitoring and make recommendations, but this fails to realize its full benefits. A more sophisticated implementation includes the capability for automated responses through an execution engine.

This provides some key advantages:

• Rather than waiting on human engineers to respond, incidents can be resolved more quickly, helping improve IT operations and, by extension, the user experience.
• AIOps tools can use historical data to improve automated remediation over time, growing more effective the longer the tools are in place.

This doesn’t mean handing over complete control for incident response. Policies can be set to configure how AIOps tools respond in various scenarios. For example, adjustments to specific infrastructure components, such as a critical server, could require review and approval from a human engineer. 

Better Decision-Making and Proactive Remediation 

In order to have a strategic rather than reactive approach to maintaining effective and efficient IT operations, you need to be able to see the big picture. Today, that’s almost impossible without AIOps. The days of searching through log files to identify possible issues are gone. It’s no longer humanly possible to deal with the vast amounts of data from the metrics, logs, traces, emails, and tickets in a typical enterprise infrastructure, especially when functions may be siloed and perhaps come from different providers.

A well-integrated AIOps platform can help with these challenges by:  

• Determining a model of normal operating parameters for a system and then monitoring for anomalies. Anomalies can then either alert the right IT operators so they can work to resolve an issue before it happens or trigger an automation, for example, starting other instances of an in-demand application during predicted spikes in use.
• Creating an accurate topology that provides real-time insight into the state of critical systems and services so IT teams can focus on innovation rather than just responding to the everyday problems inherent to complex IT operations. 

Get Strategic About IT Operations 

In this blog and the last, we’ve looked at how AIOps can help you cut through the noise created by overwhelming amounts of data and alerts so you can get to the crux of IT operations issues and solve them proactively. 

Next, we’ll finish our series by showing how an experienced provider like Compucom can help you go beyond the buzzword to a truly effective implementation and hear some real-world success stories. 

In this series:

• The Big Deal About AIOps
• AIOps: Going Beyond the Buzzword
• How AIOps Works: Tame Big Data and Get to the Crux of the Matter
• Real-World AIOps: Examples and Benefits

The post How AIOps Works: Continually Smarter & More Effective IT Operations appeared first on Compucom.

Greg Druffel •  Managing Solution Architect

Short for Artificial Intelligence for IT Operations, AIOps uses artificial intelligence (AI) and machine learning (ML) to automate and optimize IT processes. And it’s a popular buzzword lately. But, to go beyond the buzzword, cut through the hype, and realize the benefits, AIOps components must be properly integrated with your IT systems.  

When done right, AIOps equips your IT talent with powerful tools, freeing them from tedious tasks and allowing them to focus on more valuable, interesting work like cost and risk reduction, ultimately enhancing user experience.  

Early AIOps Adoption and Expertise

Gartner coined the term AIOps in 2017. But even before then, Compucom’s delivery teams were investigating ways to help customers cope with the increasing complexity and scale of IT infrastructure combined with the exploding volume of real-time data. As an early adopter of AIOps capabilities, we recognized the urgent need to help our customers better handle their IT operations.

Since they were initially introduced, AIOps tools have matured, becoming more scalable. Through experience, we’ve refined a set of combined tools that can readily integrate our customers’ existing capabilities and customize them to suit their needs and preferences.

To date, we have retail, finance, and healthcare customers that are benefiting from our AIOps expertise. We provide AIOps as part of our Infrastructure Management and Cloud Technology services, and we can support on-premises, cloud, and hybrid environments. 

Our AIOps Approach

Compucom’s approach integrates toolsets to handle five AIOps components.

1. The Event Management component monitors and manages network, server, storage, application, and IoT data, passing events to the Situational Awareness component.
2. The Situational Awareness component correlates events to determine an “incident,” reducing event noise by 98%. The incident is enriched with relevant data to help understand system and business impact in virtual situation rooms that enable collaboration across multiple domains. Algorithms are applied to determine the root cause, learning from previous results and from manual training by the operators.
3. The Engage component (IT Service Management or ITSM) manages and records all actions and results from the Situational Awareness and Orchestration Services components. This dramatically improves record and ticket quality, allowing for much more effective Problem Management.
4. The Orchestration Services component is instructed by the Situational Awareness component when additional information or action from an endpoint or service is needed. It enables operators to design workflows for diagnosis and remediation, which may include steps asking for human input.
5. The Execution Services component directly interacts with the endpoint devices. It executes the commands from the Orchestration Services component and returns the results.  

Monitor the Right Sources. Use the Right Tools.

Sophisticated AIOps monitors and ingests data from three layers. Events from these layers can then be correlated to create context and a better understanding of inefficiencies or bottlenecks:

• Hybrid IT Infrastructure: Sources such as compute, network, storage, IoT, logs, and databases as well as events from other Managed Services Providers (MSPs)
• Applications: These include all in-scope business applications hosted within the Hybrid IT Infrastructure. 
• Business Transactions:  This includes monitoring transactions within the environment, like real-time order volume.  

As we put together our version of AIOps, we prioritized the following:

• “Plug and Play” Flexibility: Our flexible interface enables us to easily integrate Compucom and customer tools for various application and infrastructure support requirements — such as monitoring tools, ticketing systems, and incident management systems — switching them out as needs evolve.
• Better Visibility into Applications and Infrastructure: Your organization’s topology provides context for AIOps, and visualizations and dashboards show the big picture of your applications and infrastructure.
• Enhanced Diagnosis and Incident Remediation Capabilities: Incident diagnosis is enhanced through advanced intelligent event aggregation, filtering, correlation, and machine learning. Remediations are optimized with artificial intelligence and business process automation. 
• A Minimal Footprint Within Your Managed Environment: The AIOps platform and associated execution engine have a small footprint to minimize the burden on your computing environment. 

Effective AIOps Delivers Serious Advantages

Through anomaly detection, event correlation, and enhanced root cause analysis, Compucom’s AIOps equips IT teams to:

• Improve root cause analysis across IT system silos.
• Reduce network congestion.
• Remedy the increased the average response time for an application.
• Cut down the overwhelming volume of alarms.
• Identify issues during change management processes.
• Lower Mean Time to Detect (MTTD) and Mean Time to Remediate (MTTR).
• Dramatically reduce ticket counts and improve ticket quality. 

Get a Handle on Your  IT Operations 

So, is AIOps overhyped? Should system and network administrators start quaking in their Skechers if their boss mentions AIOps because robots are coming to steal their jobs? Definitely not.

With AIOps spending expected to reach $40 billion by 2026, many companies are jumping on board the AIOps train. Ensuring you reach your goals rather than just paying for hype is essential. Going with an experienced provider like Compucom allows your IT talent to reap the benefits of truly effective AIOps.

Our next blog will look at real-life AIOps success stories, showing examples of the benefits to IT operations you can expect from a well-designed implementation. 

In this series:

• The Big Deal About AIOps
• AIOps: Going Beyond the Buzzword
• How AIOps Works: Tame Big Data and Get to the Crux of the Matter
• Real-World AIOps: Examples and Benefits

The post AIOps: Going Beyond the Buzzword appeared first on Compucom.

Can you think of any truly utopian sci-fi movies about Artificial Intelligence (AI)? Even lighter-hearted ones can’t resist the sci-fi trope of AI going rogue. A recently released film, The Creator, is a beautifully shot example of the typical dystopian AI tale — likely hoping to capitalize on society’s recent stress over the dramatic advances in AI. 

So, is our future with AI destined to be all doom and gloom like the movies? Definitely not. This blog is the second in a five-part series discussing one of many applications of AI that register much closer to the utopian end of the scale, Artificial Intelligence for IT operations, or AIOps.

As we discussed in our first blog in this series, AIOps helps companies ingest and analyze the overwhelming quantity of data and alerts generated by IT operations to identify patterns, trends, and anomalies. It learns from these and then makes predictions, initiates automated remediations, or offers recommendations that will aid IT teams in decision-making. The end goals are boosted efficiency, less downtime, and cost savings.

And who wouldn’t want to achieve those? But implementing AIOps is not as simple as buying a platform like Moogsoft or Splunk.

The key to a successful implementation is integrating the right toolsets with your infrastructure to enable three main functionalities:

• Data ingestion (from multiple sources across the infrastructure)
• Analysis, correlation, and recommendations 
• Proactive remediation (the fix)

In this blog, we’ll look more closely at the first two parts of the process: ingestion of the data and the subsequent analysis, correlation, and generation of recommendations. Part two will focus on how AIOps fixes issues and enables proactive remediation. 

A Closer Look at Data Ingestion 

In an enterprise organization, the applications, infrastructure, and network are often siloed, perhaps even delivered by multiple providers. This makes it challenging to gain a comprehensive overview of all IT domains to best manage them.

To complicate issues further, each component of IT operations may come with its own “language” or terminology for attributes. An effective AIOps framework creates a common syntax of attributes and data classes as it collects and integrates real-time and historical data from across a company’s various sources to build greater visibility and allow for cross-domain analysis of issues.

The sources could be different hosting models, such as on-premises, private cloud, public cloud, or hybrid. The systems needing monitoring might include:

• Point of Sale (PoS),
• Internet of Things (IoT) devices,
• PCs, servers, computers,
• Infrastructure, applications, middleware, databases, and backup.

Collecting the data for AIOps may mean plugging in existing monitoring tools, ticketing systems, and incident management systems, leveraging performance monitoring from service providers, or instrumenting the environment with monitoring tools.

Whatever the origin of data collected, it’s vital that the AIOps framework can handle the volume and scale as the organization grows. 

Taming the Data, Identifying Incidents, and Finding the Root Cause  

Event Analysis  

Once the data is ingested, the AIOps platform applies machine learning algorithms to filter, remove duplicates, normalize, and correlate events across multiple siloes, boiling the data down into more manageable “incidents.”  

The algorithms used in incident analysis continuously improve over time in two ways: learning from results applied to previous incidents and through manual training by operators.

Incident Enrichment and Management

Alerts and incidents can be enriched with data and details from external sources such as ITSM tools, financial systems, and business databases to aid in the diagnosis and remediation process.

Diagnosis and Recommendations

The AIOps platform runs diagnostics to identify the root cause. For example, it may run an automation to collect more information from an endpoint. It then determines how an incident can be resolved and builds recommendations. 

If the diagnostics cannot determine a resolution, AIOps escalates the incident and may dispatch a technician to perform further diagnostics. 

Get a Handle on Your IT Operations 

In this blog, we’ve delved deeper into how AIOps can help you cut through the noise of overwhelming amounts of data and alerts to get to the crux of issues in IT operations, whether it be a predicted slowdown or anomalies indicating a security threat. Our next blog will focus on the final step in a more sophisticated AIOps implementation: proactive remediation.

Later, we’ll finish our series by showing how an experienced provider such as Compucom can help you go beyond the buzzword to a truly effective implementation and hear some real-world success stories.

In this series:

• The Big Deal About AIOps
• AIOps: Going Beyond the Buzzword
• How AIOps Works: Continually Smarter & More Effective IT Operations
• Real-World AIOps: Examples and Benefits

The post How AIOps Works: Tame Big Data and Get to the Crux of the Matter appeared first on Compucom.

Why It’s the Future of IT Operations

How do you feel when you hear the term Artificial Intelligence (AI)? Excited? Anxious? Afraid? Suspicious? Were you an early adopter of ChatGPT, happily entering prompts to generate a week’s worth of Mediterranean diet recipes, or a blog that sounds like it Snoop Dogg or Cookie Monster wrote it? Or does the mention of AI make you shudder with thoughts of deep fakes and conspiracies, the voice of HAL 9000, SkyNet, and glitches in The Matrix? Frankly, for many of us, it’s a bit of both.

Whatever our trepidations, AI has rapidly moved from the realm of sci-fi to become part of our everyday lives. And the abundance of practical and beneficial applications for AI in business is undeniable – freeing us up from grunt work, augmenting our analytical capabilities, and saving us from costly human errors.

AIOps, or AI for IT Operations, is one such example. It uses Big Data, Machine Learning (ML), and AI to automate and improve how IT operations are managed, making them more efficient, reliable, and effective. This first blog in our five-part AIOps series gives an overview of AIOps and its benefits.

Cutting Through the Noise to Find and Fix the Issue

In enterprise organizations with complex and vast IT ecosystems, it’s becoming tougher to maintain a firm grip on IT operations. A typical enterprise organization has numerous applications, services, and infrastructure components in varied locations, generating huge amounts of data and requiring constant monitoring and analysis.

Pair this with increased user expectations and dependence on technology, and it’s easy to see the need for a system to help your IT teams cut through the noise to understand and predict which improvements are needed. And if this system can also automate some remedial actions, you’ll greatly augment your capability to keep your IT operations running smoothly and efficiently.  

How Big Data and Machine Learning Work Together in AIOps 

Big Data for AIOps refers to large, diverse sets of information sourced from monitoring systems and engagement data such as events, logs, performance, availability, and capacity.

ML is a subset of AI that focuses on algorithms that can learn from data and improve their performance without being explicitly programmed. In AIOps, ML can help discover patterns, trends, and correlations in the data, using them to make predictions and recommendations. It also enables AIOps to adapt to evolving environments and user needs by using feedback loops and reinforcement learning.

AIOps uses Big Data, AI, and ML capabilities to:

• Collect ever-increasing volumes of data from multiple IT infrastructure components.
• Intelligently pinpoint significant events and patterns related to performance and availability.
• Diagnose the root cause for automatic or human resolution. 

The Components of an AIOps Implementation

According to a recent study, 67% of enterprise infrastructure is now cloud-based. A typical infrastructure can often be a combination of on-premises, private, and public cloud components and may be managed by multiple IT teams.

A successful AIOps implementation will carefully integrate several toolsets to help teams collaborate to observe, analyze, and act proactively to fix IT operations issues. These toolsets can be categorized into three functions:

1. Data Ingestion
   Observability tools handle the ingestion, aggregation, and analysis of performance data from your applications, infrastructure, and network.
   
2. Analysis, Correlation, and Recommendations
   AIOps solutions analyze, correlate, and enrich the data for better insights and automated actions, enabling IT teams to filter out the noise from alerts and zero in on anomalies, patterns, and trends in resource requirements to get the big picture of your IT operations in real-time.
   
3. Proactive Remediation
   AIOps tools can execute automated processes in response to recommendations, rectifying issues such as slowdowns promptly, sometimes before they even happen. 

Get a Handle on Your IT Operations 

Today’s companies face increased security threats and compliance requirements, difficulties managing vast quantities of system-generated data, and demands for complex and varied analysis by various stakeholders. Add to this list a shortage of IT talent to handle it all and it’s easy to see why there’s a dramatic rise in demand for AIOps.

As we continue with this blog series, we’ll dive deeper into how the components of AIOps work together, look at how an experienced provider like Compucom can help you go beyond the buzzword to realize its benefits and hear some real-world success stories.

In this series:

• AIOps: Going Beyond the Buzzword
• How AIOps Works: Tame Big Data and Get to the Crux of the Matter
• How AIOps Works: Continually Smarter & More Effective IT Operations
• Real-World AIOps: Examples and Benefits

The post The Big Deal About AIOps appeared first on Compucom.