The Holiday Season means it is time for APMdigest's annual list of Application Performance Management (APM) predictions, the most popular content on APMdigest, viewed by tens of thousands of people in the IT community around the world for more than a decade. Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2021.
Throughout the year, APMdigest covers a variety of related technologies beyond APM, and this year's predictions list offers an equally broad scope of topics. In addition to APM, the related technologies covered include AIOps, MLOps, Observability, Open Telemetry, Log Analytics, End-User Experience Management (EUEM), Monitoring, IT Service Management (ITSM). This year, remote work from home (WFH) also takes center stage, inevitably impacting many of the predictions just as it has impacted the industry and all our lives.
Some of these predictions may come true in the next 12 months, while others may be just as valid but take several years to be realized. Still others may be wishful thinking or unbased fears. Several predictions even directly contradict each other. But taken collectively, this list of predictions offers a timely and fascinating snapshot of what the IT industry and the APM market are thinking about, planning, expecting and hoping for 2021.
The predictions will be posted in 6 parts over the next week and a half, with separate lists of predictions for NPM and Cloud to follow after the holidays.
A forecast by the top minds in Application Performance Management today, here are the predictions. Part 1 covers the buzzword of 2020, AIOps.
RAPID DIGITAL TRANSFORMATION
CIOs who don't nail down instrumentation and automation are going to lose their jobs. Those CIOs who aren't planning for their rapid transformation and changing their organizational structure and trying to drive a service-oriented mindset will be fired in three years. And, I'm not trying to be melodramatic. The people who are moving slowly and methodically are going to be left behind.
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Every large company is on a long-term transition to digital and cloud — so they can effectively compete against the likes of Amazon, and the Silicon Valley startups who are aiming for them. Companies have been trying to transform and in 2020 they all got an unintended boost in their push to the cloud — because they had to. The Fortune 500, having seen relative success with cloud and digital and are not going back. They are all doubling down. 2021 will be the year of the digital double down.
CEO and Co-Founder, Skyflow
AIOPS: ITOPS STRATEGIC PRIORITY
AIOps will be a strategic priority as companies try to do more with less all while complexities grow.
Regional CTO, AppDynamics
AIOps Wins the Year! AIOps is the application of advanced analytics in the form of machine learning (ML) and artificial intelligence (AI) towards automating network operations. The goal is for ITOps teams to move more quickly. The dramatic changes to network operations and usage over the last eight months will continue throughout 2021. As such, network managers need the ability to understand these new network baselines, bandwidth usage trends, application SLAs, and the new potential bottlenecks or trouble hotspots brought on by increased usage of cloud services, changing traffic patterns and migration from traditional WAN to SDWAN networks. Without AIOps technology, teams will struggle to process and interpret the large data outputs from these systems. As a result, AIOps will be the hero technology of 2021.
VP of Engineering, LiveAction
In 2021, we will see the wide adoption of AIOps as more organizations move to the cloud. Improved AI capabilities will provide pattern recognition capabilities that deliver operational analytics to network administrators at light speed. Combined with automation and self-healing technology, AIOps will change the way network administrators spend their time. Instead of performing operational tasks that produce results, they will evolve into analysts that interpret results, providing insight back to the organization.
Principal Federal Solutions Architect, Ivanti
AIOps will become the foundation for automation: AI and ML technology and solutions will be used to drive contexts from complex and distributed systems to help contextual automation across development and operations.
VP Product Development and Cloud Operations, OpsRamp
The transition towards a digital-online economy continues to increase pressure on enterprises to accelerate digital transformation initiatives. The coronavirus has exacerbated the existing technology gap that is preventing many enterprises from successfully leveraging their data in an optimized manner to address growing needs for streamlining business processes, complying with stricter regulations and meeting growing customer expectations. Enterprises will need to implement new technologies like AIOps to automate operations and data management with autonomous capabilities, especially as data volumes peak. This will help them better align with their business stakeholders; becoming more agile and delivering rapid responsiveness to internal and external application users while maintaining a highly resilient infrastructure.
VP of Marketing, GigaSpaces Technologies
With a year of unpredictability behind us, enterprises will have to expect the unexpected when it comes to making technology stacks infallible and proactive. We'll see demand for AIOps continue to grow, as it can address and anticipate these unexpected scenarios using AI, ML, and predictive analytics. The increasing complexity of digital enterprise applications spanning hybrid on-premise and cloud infrastructures coupled with the adoption of modern application architectures such as containerization will result in an unprecedented increase in both the volume and complexity of data. While data overload from modern digital environments can delay repair and overwhelm IT Ops teams, noisy datasets will be a barrier of the past as smarter strategies and centralized AIOps systems help organizations improve the customer experience, deliver on modern application assurance and optimization, tie it to intelligent automation, and thrive as autonomous digital enterprises. In fact, conventional IT Operations approaches may no longer be feasible — making the adoption of AIOps inevitable to be able to scale resources and effectively manage modern environments.
CPO, BMC Software
APM CONVERGES WITH AI
In 2021, I'm looking to see integration and or adaptation of APM with specialized AI based analysis and remediation platforms.
Co-Founder and CEO , Edgevana
The power of AIOps is that it can snap into monitoring tools and observability practice by enabling on-call engineers to ingest data from multiple sources — whether from any monitoring or incident management tools that are emitting alerts. We will continue to see a lot more interoperability among technology in the AIOps space to become more data agnostic. The end goal will be to normalize that data from multiple sources, identify relationships across them and start to group and correlate alerts and incident data resulting from the same core issue in order to further reduce alert noise and enable faster resolution.
Director, Product Marketing, New Relic
In 2021, advances in AI and machine learning will allow devices to self-heal and self-secure by as much as 80%, allowing IT to set policies and know their devices and data are secure. Not only will this mean that IT can focus on transforming their business to be more competitive in their market, users can expect to receive a more ambient, personalized device experience where they can remain productive regardless of where they are working or the device they use.
Senior Director of Product Management, Ivanti
In 2021, we will see a focus on CloudOps automation with prebuilt corrective behaviors. Self-healing is a feature where a tool can take automated corrective action to restore systems to operation. However, you have to build these behaviors yourself, including automations, or wait as the tool learns over time. We've all seen the growth of AIOps, and the future is that these behaviors will come pre-built with pre-existing knowledge that can operate distributed or centralized. This means that from day one you can automate most of the issues that cloud and non-cloud systems will need to deal with, and knowledge will build and be shared over time.
Automatic remediation or this idea of AIOps providing self-healing capabilities was talked about a lot in 2020. Let's be honest, most AIOps offerings out there today aren't doing that, yet. So far the focus has primarily been on detecting anomalies to be able to predict and prevent issues before they happen, on correlating events and alerts to reduce noise and and on enriching incidents and alerts with metadata as well as context to be able to diagnose and get to the root cause faster. In the longer term, we will see AIOps technology expand its scope to include automatic remediation. It will do so by providing automation runbooks and scripts that are tailored to specific problems or by tighter coupling with automation technology that's already out there in the market so teams can close the gap between issue detection and remediation.
Director, Product Marketing, New Relic
AIOPS FOR INCIDENT MANAGEMENT
Digital transformation initiatives will remain a primary business focus in 2021 — but increased competition among digital services means the stakes have never been higher for delivering flawless user experiences. To remain competitive, organizations must place greater importance on the systems and processes technical teams use to detect and resolve service degradations. Traditional approaches to incident management combine tribal knowledge with cumbersome processes, resulting in inefficient resource use and longer service degradations. Over the next year, more enterprises will implement modern approaches to incident management by automating processes throughout the incident management lifecycle — facilitating dynamic collaboration, delivering increased resilience, and using data to inform and improve processes. AIOps for incident management will continue to evolve with new technologies working with large datasets from an increasing number of signals to proactively identify incident root causes. While the industry is currently midway through this evolution, it's quickly advancing to help existing teams successfully manage their rapidly growing portfolio of digital services.
Accelerated digital transformation initiatives in 2020 points to increased convergence of features and capabilities across AIOps and modern incident management in 2021. Looking toward the next year, I anticipate there will be a convergence of features and capabilities across AIOps and modern incident management as vendors seek to own more of the value chain and progress from insight to action. The acceleration of digital transformation we witnessed in 2020 served as a precursor to this consolidation, acting as a market driver for companies who now realize they need to be investing in these capabilities. We'll see a continued acceleration in feature development — either in-house or through acquisitions — in the AIOps and incident management space as vendors aggressively look to build, buy or partner in order to advance and accelerate their product roadmaps.
REFINED RISK ASSESSMENT
As the AIOps space continues to mature, we see an opportunity for vendors to refine their risk assessment capabilities to enable customers to fix issues with near-certainty, without breaking anything else in the system. In 2021, one area where we will see increased focus from both vendors and more adoption among users will be around enabling more elegant dependency mapping so engineers can accurately assess risk as a part of the remediation process or build-deploy cycle for software changes, to ensure that a change in one part of an environment won't break the system elsewhere.
Director, Product Marketing, New Relic
AI-ENABLED FEEDBACK LOOPS
2021 will be the year of AI-enabled feedback loops between data pouring in from APM, Observability platforms and ITOM, and their ultimate resolution as refined actions for SREs and DevOps teams. Given how fast-changing and ephemeral the hybrid IT and cloud infrastructure underneath application workloads is becoming, an AIOps layer must filter the storm of incoming data, assigning relevancy, root cause, and responsibility in conjunction with ITSM and issue resolution tools. Companies that fail to intelligently adapt to this deluge of data from multiple sources will likely get swept under, or at least fail to make much forward progress due to constant alerts and firefighting.
Principal Analyst, Intellyx
AIOps will continue to disappoint as organizations transition to webs of microservices. It relies on past data to train models, which is increasingly meaningless in fast-moving webs of microservices.
CEO and Co-Founder, Glasnostic
MLOPS BECOMES MAINSTREAM
The MLOps field has grown massively in just a few years of existence, but it has yet to meaningfully mature. That will change in 2021 — as ML models become an increasingly common piece of our applications, business owners will demand that the models remain up-to-date with the deluge of new data that is being collected to enhance them. This means that DevOps professionals will need to embrace new tooling and a new way of thinking about how to continuously integrate data into ML models. Continuous re-training, feature stores, and production model monitoring are going to be essential components of the MLOps stack.
CEO and Co-Founder, Determined AI
MLOPS - NOT AIOPS – WILL BE A CHALLENGE
One of the biggest issues in 2021 will be putting machine learning and artificial intelligence into production. This tends to be called MLOps, in contrast to AIOps. To overcome these challenges, progress will need to be made on tooling, as the tools for versioning data, managing models, and testing AI are still primitive. To help with this, DevOps and SRE will need to extend practices past traditional applications to include AI applications. For example: keeping source code in a repository is basic, along with deploying directly from the repository. For AI, however, the training data is actually more important than the source code. Testing is also absolutely fundamental and we'll need to answer how to test an application whose performance might change over time, as well as how to test for problems like fairness and bias.
VP of Content Strategy, O'Reilly Media
ITOPS AND DEVOPS CONVERGENCE
Successful organizations will blur (or erase) the line between ITOps and DevOps. People say the DevOps movement is a transformation and a journey; I actually don't think it's a journey. Instead, I think it's a different way of adopting, and it increases the heterogeneity of the operating model for our companies.
SVP of Cloud, Splunk
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Technology convergence will accelerate: 2021 will bring the convergence across the board. Everything-defined software will continue to accelerate while public, private cloud, and edge computing will start to coexist and operate seamlessly. CloudOps, DevOps and ITOps will converge to operate in the context of business and drive efficiency and agility.
VP Product Development and Cloud Operations, OpsRamp
Read Bhanu Singh's recent blog on APMdigest: IT Now Viewed as Strategic Differentiator During COVID-19
DevOps and AIOps will become a more mature hand-in-hand reality in 2021, with developers and operations teams collaborating closer to: better act on production issues, prevent issues earlier in development cycles; and respond faster to production outages.
Chief Evangelist, Perfecto at Perforce Software
Go to: 2021 Application Performance Management Predictions - Part 2, covering APM and Observability