AppDynamics and Cisco To Host Virtual Event on AIOps and APM

To mark the two year anniversary of Cisco’s intent to acquire AppDynamics, the worldwide leader in IT, networking, and cybersecurity solutions will join AppDynamics for a one-of-a-kind virtual launch event on January 23, 2019. At AppDynamics Transform: AIOps and the Future of Performance Monitoring, David Wadhwani, CEO of AppDynamics, will share what’s next for the two companies, and lead a lively discussion with Cisco executives, Okta’s Chief Information Officer, Mark Settle, and Nancy Gohring, Senior Analyst at 451 Research. At the event, we’ll talk through what challenges leaders face and how they’re preparing for the future of performance monitoring.

Technology Leaders to Weigh In On the Impact of AI and the Future of Performance Monitoring

Today, application infrastructure is increasingly complex. Organizations are building and monitoring public, private, and hybrid cloud infrastructure alongside microservices and third party integrations. And while these developments have made it easier for businesses to scale quickly, they’ve introduced a deluge of data into the IT environment, making it challenging to identify issues and resolve them quickly.

APM solutions like AppDynamics continue to lead the way when it comes to providing real-time business insights to power mission critical business decisions. However, recent research has revealed a potential blind spot for IT teams: A massive 91% of global IT leaders say that monitoring tools only provide data on the performance of their own area of responsibility. For IT teams that want to mitigate risk as a result of performance problems, and business leaders who want to protect their bottom line, this blind spot represents a huge opportunity for improvement.

The Next Chapter in the AppDynamics and Cisco Story

As application environments continue to grow in complexity, so does the need for more comprehensive insight into performance. But technology infrastructure is simply too large and too dynamic for IT operations teams to manage manually. Automation for remediation and optimization is key–and that’s where innovations in artificial intelligence (AI) have the potential to make a huge difference in monitoring activities.

So, what does the future of performance monitoring look like?

Join us at the virtual event on January 23, 2019, to find out. David Wadhwani, alongside Cisco executives, will make an exciting announcement about our next chapter together. During the broadcast, we’ll also feature industry analysts and customers as we engage in a lively conversation about the emerging “AIOps” category, and what impact it will have on the performance monitoring space.

You won’t want to miss this unique virtual event.

Register now for AppDynamics Transform


What Is AIOps? Platforms, Market, Use Cases & The Future of Performance Monitoring

The term “AIOps” stands for “artificial intelligence for IT operations.” Originally coined by Gartner in 2017, the term refers to the way data and information from an IT environment are managed by an IT team–in this case, using AI. This definition from Gartner provides more granular detail related to the concept and explicates the value of an AIOps platform:

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

But why should an enterprise IT team care about about AIOps?

To answer that question, let’s dig deeper to understand the story behind AIOps, explore the elements of AIOps platforms, and review three potential use cases.

The Core Elements of An AIOps Platform

Today’s application environments are exploding in complexity. According to the Wall Street Journal, midsize to large companies now use an average of eight different cloud providers for various enterprise applications and services. Compounding this complexity is the sheer volume of data produced by application infrastructure, and the high potential for performance problems each time an update or change is made to that existing infrastructure. While application performance monitoring (APM) solutions provide real-time alerts for performance problems, there’s evidence that IT teams need more support to effectively monitor the increasingly complex landscape.

And that’s where AIOps platforms enter the picture.

Rather than reacting to issues as they arise in the application environment, AIOps platforms allow IT teams to proactively manage performance challenges faster, and in real-time–before they become system-wide problems. That’s because AIOps platforms have the ability to ingest large volumes of data originating from all areas of the application environment, and analyze it using AI to identify areas of remediation and optimization.

AIOps platforms also play a critical role in eliminating the manual component of identifying issues within the IT landscape, a problem that’s compounded by the still siloed nature of the monitoring environment. In fact, recent research from AppDynamics revealed that 91% of global IT leaders said monitoring tools only provide data about how releases impact their own area of responsibility, and not the broader IT environment, or the business. With an AIOps platform, IT doesn’t have to work harder to get smarter about what’s happening within every facet of application infrastructure.

Make no mistake, AIOps platforms have compelling potential. But as of right now, the category itself is emergent and highly fluid. Case in point: Gartner defines AIOps platforms as having several key components, however, those components are broad enough that many tools could potentially fit into this category now or in the future. Here’s how a Gartner analyst, Pankaj Prasad, described AIOps platforms:

“AIOps platform technologies comprise of multiple layers that address data collection, storage, analytical engines and visualization. They enable integration with other applications via application programming interfaces (APIs) allowing for a vendor-agnostic data ingestion capability.”

While Gartner’s elements of an AIOps platform are somewhat broad–as are many others out there in the market–the category will continue to evolve in the years ahead as the technology becomes more rigorous, and its use cases more apparent. What’s more, many of these shifts will happen alongside changes in the broader APM space. A more pared down overview of core AIOps platform components would include:

  • Machine learning
  • Performance baselining
  • Anomaly detection
  • Automated root cause analysis
  • Predictive insights

What Problems Does An AIOps Strategy Solve?

Growing complexity and the deluge of data within the application environment puts new demands on IT professionals to both synthesize meaning from this influx of information and connect it to broader business objectives. In this highly demanding environment, IT teams need all of the help they can get when it comes to performance optimization.

That’s where AIOps platforms can play a pivotal role in advancing IT organizations and reducing the complexity within the application environment. With AIOps, you can bring all data into a single place, and scale it to understand your environment from every possible angle. This provides teams with the flexibility needed to automate certain tasks when appropriate, and use AI to pinpoint problems faster.

From reducing the cognitive overhead of parsing through volumes of data within the application environment to the potential for self-healing capabilities that help solve major performance problems, AIOps is an exciting space that could help IT professionals in three major areas:

  1. Drive faster and better decision-making. Broadly speaking, AIOps platforms and related AI features have the potential to become smart enough about IT environments in order to surface insights and provide them to leaders for faster and better decision-making.
  2. Decrease MTTR. Outages and performance problems hurt the bottom line of every business, so IT organizations must actively seek out ways to reduce the mean time to resolution (MTTR). With AIOps, it’s possible that IT teams could decrease MTTR and prevent emerging issues, and in doing so, reduce the costs associated with performance problems.
  3. Build a more proactive approach to performance monitoring. According to research from AppDynamics, 74% of IT professionals would like to build a more proactive approach to performance monitoring. With AIOps technology, there’s potential to take it a step further, and respond to issues in real-time. What’s more, by taking in the totality of application environment data, AIOps platforms could connect performance insights to business outcomes. This would finally close the loop on the impact of performance on the business and customers, and it would help organizations take action before small issues become larger problems.

Looking Ahead to the Future of Performance Monitoring and AIOps

Right now, AIOps technology is still relatively new, the terms and concepts relatively fluid, and there’s a great deal of work to be done before anyone can deliver on the promise of AIOps. What is established, however, is that AIOps is already a mindset focused on prediction over reaction, answers over investigation, and actions over analysis. And that’s why IT leaders should keep an eye on the rise of AIOps as a whole, and start preparing for what’s next in monitoring and observability. If history is any indication, there’s enormous potential for transformation in the space in a short period of time.

The Rise of AIOps: How Data, Machine Learning, and AI Will Transform Performance Monitoring

Over the last decade, application environments have exploded in complexity.

Gone are the days of managing monoliths. Today’s IT professionals are tasked with ensuring the performance and reliability of distributed systems across virtualized and multi-cloud environments. And while it may be true that the emergence of this modern application environment has provided the speed and flexibility professionals demand, these numerous services have unleashed a deluge of data on the enterprise IT environment.

Application performance monitoring (APM) solutions have proven essential in helping leaders take back control by providing the real-time insights needed to take action. But as the volume of data in IT ecosystems increases, many professionals are finding it challenging to take a proactive approach to managing it all. While automating tasks have helped teams free up some bandwidth for operations and planning, automation alone is no match for today’s increasingly complex environments. What’s needed is a strategy focused on reducing the burden of mounting IT operations responsibilities, and surfacing the insights that matter the most so that businesses can take the right action.

So, what are forward-thinking IT professionals doing to stay ahead of the curve?

Many are applying what’s being called an AIOps approach to the challenge of application environment complexity. This approach leverages advances in machine learning and artificial intelligence (AI) to proactively solve problems that arise in the application environment. Even though relatively new, the approach is gaining momentum. And for good reason: Using AI to identify potential challenges within the application environment doesn’t just help IT professionals get ahead of problems — it helps companies avoid revenue-impacting outages that jeopardize the customer experience, the business, and the brand.

In order to fully understand the rise of AIOps and why it has developed the momentum it has, we wanted to dig deeper to uncover the actual challenges faced by IT professionals, and how they’re managing them in an increasingly complex application environment. To accomplish that, AppDynamics undertook a study of 6,000 global IT leaders in Australia, Canada, France, Germany, the United Kingdom, and the United States. Their responses answered three key questions about the shift in the performance space:

(1) What’s the current enterprise approach to managing increasing application environment complexity?

(2) How are global IT leaders taking a proactive approach to identifying problems in the application environment?

(3) How broadly is AI identified as a potential solution to reducing complexity in IT ecosystems?

Let’s see what the research revealed.

The Demand for Proactive Application Performance Monitoring Tools

Today, midsize to large companies use an average of eight different cloud providers for various enterprise applications and services. As a result, IT professionals are managing an ever-increasing set of tasks that have the potential to become disconnected if not managed properly. What’s more, within these highly distributed systems, IT leaders must grapple with the impact of new code being deployed, as well as the virtually infinite potential outcomes associated with doing so. Without a unified view of how all of these elements interact, there’s significant potential for issues to arise that impact performance — and, ultimately — the customer experience.

New research from AppDynamics underscores the cause for concern: 48% of enterprises surveyed say they’re releasing new features or code at least monthly, but their current approach to monitoring only provides a siloed view on the quality and impact of each release. In fact, of those enterprises that release on that cadence, a massive 91% say that monitoring tools only provide data on how each release drives the performance of their own area of responsibility.

Research from AppDynamics indicates performance monitoring remains siloed.

Should these findings raise eyebrows? Absolutely.

That’s because they indicate that for the vast majority of those surveyed, a holistic view of business and customer value is still difficult to achieve. And that puts innovation — as well as modern, best-in-class software development practices like continuous delivery — at serious risk.

But that’s where leveraging data about the application environment using machine learning, as well as AI, can make a massive difference. Instead of merely ingesting data from every dimension of the application environment, these tools can help IT professionals build a more proactive approach to APM.

And, by all accounts, that’s what most global IT leaders want.

According to research findings from AppDynamics, 74% of surveyed said they want to use monitoring and analytics tools proactively to detect emerging business-impacting issues, optimize user experience, and drive business outcomes like revenue and conversion. But according to our research, 42% of respondents are still using monitoring and analytics tools reactively to find and resolve technical issues. There’s indication, however, that this approach is extremely problematic for businesses. Beyond a serving as a pain point for IT professionals in terms of capacity and resource planning, reactive monitoring — in some cases — can potentially cost businesses hundreds of thousands of dollars in lost revenue.

The majority of IT professionals want to use monitoring tools more proactively.

How Reactive Monitoring Hurts Performance, Revenue, and Brand

From e-commerce to banking, booking flights to watching movies on Netflix, applications have proliferated people’s lives. As a result, consumers have high expectations for application performance that businesses must deliver on. If not, they risk jeopardizing brand loyalty and, as our research revealed, their bottom line.

“As the broader technology landscape undergoes its own dramatic change, forcing businesses to double down on their customer focus, managing the performance of applications has never been more critical to the bottom line.” — Jason Bloomberg, The Rebirth of Application Performance Management

IT professionals have long relied on the mean time to repair (MTTR) metric to evaluate the overall health of an application environment. The longer it takes to resolve an issue, the greater the potential for it to turn into a significant business problem, particularly in an increasingly fast-paced digital world. However, in this latest AppDynamics research, we made a startling discovery: Most organizations are grappling with a high average MTTR:  Respondents reported that it took an average of 1 business day, or seven hours, to resolve a system-wide issue.

But that wasn’t the most alarming finding.

Our research also revealed that many enterprise IT teams weren’t notified about performance issues via monitoring tools at all. In fact:

  • 58% find out from users calling or emailing their organization’s help desk
  • 55% find out from an executive or non-IT team member at their company who informs IT
  • 38% find out from users posting on social networks

AppDynamics research reveals how performance problems are being discovered in the enterprise.

To fully appreciate the impact of 7 hour MTTR on a business, AppDynamics asked survey respondents to report the total number of dollars lost during an hour-long outage, and used that figure to extrapolate the typical cost of an average, day-long outage. For the United States and United Kingdom, the cost of an average outage totals $402,542 USD and $212,254 USD, respectively (the cost of an outage in the United Kingdom was converted into United States dollars).

United States

AppDynamics research revealed that companies in the United States on average lose $402,542 for a single service outage.

United Kingdom

The high cost of a performance outage in the United Kingdom.

It’s important to note that these figures reflect the total cost for a single outage in the enterprise — if a company has more than one, that figure can rise dramatically. In fact, a substantial 97% of global IT leaders surveyed said they’d had performance issues related to business-critical applications in the last six months alone.

Of the 6,000 IT professionals AppDynamics surveyed, 97% said they’d experienced a service outage in the last six months.

In addition to the impact on a company’s bottom line, global IT leaders reported that
reactive performance monitoring had created stressful war room situations and damaged their brand. 36% said they had to pull developers and other teams off other work to analyze and fix problems as they presented themselves, and nearly a quarter of respondents said slow root cause analyses drained resources.

The takeaway here is clear: global IT leaders need to build a more proactive approach to APM in order to lower MTTR and protect their bottom line. But in today’s increasingly complex application environment, that’s easier said than done.

Unless, of course, you’re developing an AIOps strategy to manage it.

The Risk of Not Adopting an AIOps Strategy

AppDynamics research showed that the overwhelming majority of IT professionals want a more proactive approach to APM, but one of the main ways of achieving that — through the adoption of an AIOps strategy — isn’t being widely pursued by global IT teams in the near-term.

In fact, the global IT leaders AppDynamics surveyed reported that although they believe AIOps will be critical to their monitoring strategy, only 15% identified it as a top priority for their business in the next two years.

AppDynamics research reveals that the vast majority of IT professionals surveyed don’t have an AIOps strategy in place in the near-term.

What’s more, the capabilities that respondents identified as essential to APM in the next 5 years are precisely those that AIOps has the potential to help provide. For example:

Intelligent alerting that can be trusted to indicate an emerging issue.
49% of respondents identified this feature as core to their performance monitoring capabilities in the next five years. By ingesting data from any application environment, AIOps platforms and technology can play a pivotal role in not just automating existing IT tasks, but identifying and managing new ones based on potential problems detected in the application environment.

Automated root cause analysis and business impact assessment.
44% of respondents said solving problems quickly and understanding their impact on the business would play a crucial part of their performance management in the years ahead. With the help of AIOps technology, this can be achieved, providing increased agility in the face of potential service disruptions or threats, and without additional drain on resources.

Automated remediation for common issues.
42% of survey respondents said that they needed to build automated remediation into their strategy for performance monitoring. With AIOps, it’s easy to not only automate remediation for known issues, but unknown issues, too. That’s because it not only ingests data from your application environment, but provides more intelligent insights as a result of it.

Leading The Way With AIOps Strategy and Platforms  

Despite increasingly complex application environments, few of the global IT leaders surveyed are prioritizing the development of an AIOps strategy, which would allow them to implement the platforms and practices to permit proactive identification of issues before they become system-wide problems. Instead, global IT leaders report an average MTTR rate that hovers at a full business day, and has the potential to cost companies hundreds of thousands of dollars in lost revenue with each incident.

What’s more, AppDynamics research findings also make it clear that many global IT leaders are struggling to integrate monitoring activities into the purview of the broader business. This can cause significant delays in MTTR, as noted, as well as make companies vulnerable to service disruptions that can cause irreparable harm to the customer experience, and the enterprise as a whole.

While IT leaders have expressed a desire for a more proactive approach to monitoring, this research indicates that there’s still plenty of work to be done on numerous fronts. But the first step is clear: IT leaders must prioritize the development of an AIOps strategy and related technology. In doing so, they’ll  simplify the demands of an increasingly complex application environment, and build a stronger connection from IT to the business as a whole.

Editor’s Note: In this piece, the term “global IT leaders” refers to the respondents surveyed for this report. The term “IT professionals” refers to people in the IT or related professions as a whole.

Soundbites: Innovative IT Leaders on the Impact of AI for IT Operations

We live in a world of constant evolution, where today’s best technologies can quickly become irrelevant. As a result, businesses are innovating faster than ever before to meet consumer demands and stay competitive within their respective industries.

However, as enterprises innovate at such a rapid pace, they reduce visibility across the technology stack – application, infrastructure and network – and introduce significant operational complexity. To deal with these complexities, businesses are now turning to AI and machine learning to glean real-time insights and automate tasks that will help technology operations teams focus on what matters most – driving business and customer value.

This marriage between IT and AI brings with it a world of opportunity. So, we decided to chat with some of today’s top IT leaders from around the globe to pick their brains on how they see AI changing IT operations. Check out some of the soundbites below:

Mark Wood, Digital DevOps Lead at Vodafone

“AI will fundamentally change IT operations. I think it will go hand in hand with the whole DevOps model that is starting to progress through businesses: If you don’t need to be so concentrated on the operations because you’ve got AI, machine learning, and systems doing a lot of that for you, it gives you more time to focus on the development side and making sure the development is right.”

Guillermo Diaz, Chief Information Officer and SVP at Cisco

“I think one of the elements of having a world class operation is this notion of taking it to a whole other level. We call it autonomic operations – where we’re making sure that we’re not only detecting problems and repairing them, but they happen automatically or autonomously and things just fix themselves. This is where I’d like to take our organization, so we can have our people work on even more value-added things.”

Chuck Medhurst, President and GM at BMW Technology

“We do a lot of work within our group around machine learning for predictive analytics because that’s where we see digital services moving within the industry. We have data scientists who are taking a look at customer data to understand customer behavior. Ultimately, we want to be able to take that data and then use our own algorithms to figure out how we can improve customer experiences.”

Spencer Allen, Digital Production Senior Manger at Vodafone

“My utopia would be something akin to Minority Report, in which you fix things, the IT systems fix themselves before we actually know or the customer knows – and that’s the most important piece – whether something’s wrong or not. So, for instance if servers are failing, they’re switched out before we even know there’s a problem. The code corrects itself. I think the potential for use of machine learning and AI – is the sky’s the limit. It’s quite scary as to where it might go, but as a business, it could save fortunes, and also make us fortunes.”

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AIOps: A Self-Healing Mentality

The first time I began watching Minority Report back in 2002, the film’s premise made me optimistic: Crime could be prevented with the help of Precogs, a trio of mutant psychics capable of “previsualizing” crimes and enabling police to stop murderers before they act. What a great utopia!

I quickly realized, however, that this “utopia” was in fact a dystopian nightmare. I left the theater feeling confident that key elements of Minority Report’s bleak future—city-wide placement of iris scanners, for instance—would never come to pass. Fast forward to today, however, and ubiquitous iris-scanning doesn’t seem so far-fetched. Don’t believe me? Simply glance at your smartphone and the device unlocks.

This isn’t dystopian stuff, however. Rather, today’s consumer is enjoying the benefits that machine learning and artificial intelligence provide. From Amazon’s product recommendations to Netflix’s show suggestions to Lyft’s passenger predictions, these services—while not foreseeing crime—greatly enhance the user experience.

The systems that run these next-generation features are vastly complex, ingesting a large corpus of data and continually learning and adapting to help drive different decisions. Similarly, a new enterprise movement is underway to combine machine learning and AI to support IT operations. Gartner calls it “AIOps,” while Forrester favors “Cognitive Operations.”

A Hypothesis-Driven World

Hypothesis-driven analysis is not new to the business world. It impacts the average consumer in many ways, such as when a credit card vendor tweaks its credit-scoring rules to determine who should receive a promotional offer (and you get another packet in your mailbox). Or when the TSA decides to expand or contract its TSA PreCheck program.

Of course, systems with AI/ML are not new to the enterprise. Some parts of the stack, such as intrusion detection, have been using artificial intelligence and machine learning for some time.

But with potential AIOps use cases, we are entering an age where the entire soup-to-nuts of measuring user sentiment—everything from A/B testing to canary deployment—can be automated. And while there’s a sharp increase in the number of systems that can take action—CI/CD, IaaS, and container orchestrators are particularly well-suited to instruction—the harder part is the conclusions process, which is where AIOps systems will come into play.

The ability to make dynamic decisions and test multiple hypotheses without administrative intervention is a huge boon to business. In addition to myriad other skills, AIOps platforms could monitor user sentiment in social collaboration tools like Slack, for instance, to determine if some type of action or deeper introspection is required. This action could be something as simple as redeploying with more verbose logging, or tracing for a limited period of time to tune, heal, or even deploy a new version of an application.

AIOps: Precog, But in a Good Way

AIOps and cognitive operations may sound like two more enterprise software buzzwords to bounce around, but their potential should not be dismissed. According to Google’s Site Reliability Engineering workbook, self-healing and auto-healing infrastructures are critically important to the enterprise. What’s important to remember about AIOps and cognitive operations is that they enable self-healing before a problem occurs.

Of course, this new paradigm is no replacement for good development and operation practices. But more often than not, we take on new projects that may be ill-defined, or find ourselves dropped into the middle of a troubled project (or firestorm). In what I call the “fog of development,” no one person has an unobstructed, 360-degree view of the system.

What if the system could deliver automated insights that you could incorporate into your next software release? Having a systematic record of real-world performance and topology—rather than just tribal knowledge—is a huge plus. Similar to the security world having a runtime application self-protection (RASP) platform, engineers should address underlying issues in future versions of the application. In some ways, AIOps and cognitive operations have much in common with the CAMS Model, the core values of the DevOps Movement: culture, automation, measurement and sharing. Wouldn’t it be nice to automate the healing as well?