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Security observability: Protecting against vulnerabilities and threats

Security incidents and cyber-attacks are predicted to cost the world $9.5 trillion (yes, trillion) in 2024 and climb to over $13 trillion by 2028. These incidents can be very challenging to detect in complex distributed and microservice-based applications, which is why companies must prioritize robust security observability to safeguard modern applications against evolving threats.

Observability is key for bridging development and production insights to better understand the application’s attack surface, helping in early  detection and prevention of security vulnerabilities. Itgives you valuable application insights in real time. New Relic allows you to visualize all the security vulnerabilities in your source code, including in external dependencies, with New Relic vulnerability management.

While vulnerability management is a big part of evaluating security issues in your application, this blog is going to cover how MELT—metrics, events, logs, and traces—provides comprehensive security observability.

Key takeaways:

Build More Resilient Operations with PagerDuty Incident Management

Clearly, there are still gaps in accountability and coordination before, during and after incidents. To address these gaps, technical teams often rely on a mix of do-it-yourself and third-party tools, integrating into legacy systems for quick fixes. However, once the band-aid fix is implemented, the continuous influx of incidents leaves little time for reflection and improvement. Without dedicated time for documentation and learning, these incidents persist, hindering progress, leading to team burnout, and ultimately, delaying the development of revenue-driving features that customers are looking for.

The modern enterprise needs to transform its approach to managing incidents. By shifting the focus towards scalable processes, dynamic guidance and continuous improvement, organizations can drive accountability and help build resilience over time.

Introducing a new end-to-end incident management solution

Today, we’re excited to launch the Enterprise plan for Incident Management. As part of the PagerDuty Operations Cloud, this new product provides our customers with a unified platform that manages incidents from start to finish.

This new offering continues to deliver on the promises of the PagerDuty Operations Cloud to solve the biggest problems facing the modern digital enterprise. Today, customers are dealing with more complex digital ecosystems than ever. PagerDuty is making the incident lifecycle more resilient by offering flexibility and efficient solutions to resolve incidents. Our Incident Management solution for the enterprise streamlines alerting processes, automates remediation and triages, AI-powered insights from past incidents and correlated events, and post-incident reviews. Bringing together the best of PagerDuty’s Incident Management innovations with the new combined capabilities from our Jeli acquisition, customers have a single end-to-end offering. Specifically, Incident WorkflowsStatus Pages, and the offerings from our acquisition of Jeli, a Slack-first coordination and post-incident reviews today, are combined in Incident Management for enterprises.

By driving visible continuous improvement and offering remediation guidance at every stage of the incident lifecycle, PagerDuty eliminates the guesswork in managing incidents. Our proactive approach minimizes downtime and empowers businesses to redirect their efforts toward innovation. With PagerDuty Incident Management, companies can confidently navigate incidents knowing they’re on the path to sustained progress and success.

Here are three ways that PagerDuty helps transform major and minor incident management.

Mitigate revenue and reputational risks

PagerDuty Incident Management is the only platform that seamlessly integrates automation, communication, and resolution. New advanced actions added to Incident Workflows ensure timely status updates for important stakeholders and customers, promote SLA adherence, and help build reputational trust. Through our newest feature Jeli Post-Incident Reviews, organizations can easily analyze what went wrong and how to prevent incidents from happening in the future, fostering a more proactive approach to incident management:

Streamline processes for faster resolution

Streamline processes for faster resolution

 

Even all-level team members can effectively manage major incidents with PagerDuty Incident Management, which automatically assigns roles and tasks, promoting accountability and best practices. Teams can collaborate and communicate via Slack or Microsoft Teams, while PagerDuty unifies third-party information through automated updates, ensuring prompt response and resolution.

 

 

Consolidate, simplify, and save

Consolidate, simplify, and save

 

By integrating on-call capabilities, automated remediation guidance, and post-incident reviews and analysis into a united platform, PagerDuty eliminates complexity and reduces tech debt associated with overlapping tools. This consolidation transforms workflows, providing teams with a centralized hub for all necessary information and eliminating the inefficiency of switching between systems. This streamlined approach saves time, enhances collaboration, and reduces operating costs.

 

Make the move to PagerDuty Incident Management

Don’t let unscalable processes and tools hinder your organization’s ability to navigate IT complexity, minimize risk, and foster innovation. For customers whose organizations deploy the solution across teams to keep customer service teams in the loop with their technical counter

parts, there is also an enterprise-edition of Customer Service Operations now available.

Take the next step in modernizing your operations with PagerDuty Incident Management today.

Want to learn more? Watch our latest webinar, Prepare for the Unexpected: How Continuous Improvement in Incident Management Reduces Risk and Improves Quality of Life, for more information.

To learn more about Resilient Operations, Click  here to schedule a call  with our experts.

MLOps vs AIOps – What’s the Difference?

Some frameworks are for multiple teams that work separately, and this can increase risks and delays. To keep up with growing demands and avoid delays, we want to get rid of closed-off siloes. Under the DevOps model, we can merge two or more teams and focus on continuous integration and deployment, making communication better, deployment faster, and security – unbreakable. If you’re using Agile software development, DevOps is complimentary.

DevOps Cycle – Atlassian

We’ve been using DevOps in conventional software development for a while now, but we can also use it for machine learning and artificial intelligence.

Why do we need DevOps? What’s the reason behind tiering Machine Learning and Artificial Intelligence on top of DevOps? What’s the difference between ML Ops and AI Ops? Keep reading to find out.

What is MLOps?

Many industries integrate machine learning systems into their existing products and services because ML can be good for the bottom line, and it can sharpen your competitive edge.

The problem is that machine learning processes are complicated and often require a great deal of time and resources. To avoid overspending, companies need a framework that unifies the development and deployment of ML systems. MLOps is that framework. It standardizes and streamlines the continuous delivery of ML models into productions.

Before we explore why we need to use MLOps, let’s first look at the Machine Learning modeling lifecycle to understand what the focus is.

Learn more

MLOps: What It Is, Why it Matters, and How To Implement It (from a Data Scientist Perspective)

Lifecycle of a machine learning model

ML projects start with defining a business use case. Once the use case is defined, the following steps are taken to deploy a machine learning solution into production:

  1. Data Extraction – Integrating data from various sources
  2. Exploratory Data Analysis – Understanding underlying data and its properties
  3. Data Preparation – Curating data for successful execution of an ML solution
  4. Create ML Model/Solution – Creating and training ML model using ML algorithms
  5. Model Evaluation and Validation – Computing model on test data set and validate the performance
  6. Model Deployment – Deploying ML models in production

Building and processing ML systems is a manual process, and managing such systems at scale isn’t easy. Many teams struggle with the traditional manual way of deploying and managing ML solutions.

Read also

Developing AI/ML Projects for Business – Best Practices
The Life Cycle of a Machine Learning Project: What Are the Stages?

ML tiered on DevOps – overcoming challenges

To solve issues with manual implementation and deployment of machine learning systems, teams need to adopt modern practices that make it easier to create and deploy enterprise applications efficiently.

MLOps leverages the same principle as DevOps, but with an extra layer of ML model/system.

Source: Nvidia

“The modeling code, dependencies, and any other runtime requirements can be packaged to implement reproducible ML. Reproducible ML will help reduce the costs of packaging and maintaining model versions (giving you the power to answer the question about the state of any model in its history). Additionally, since it has been packaged, it will be much easier to deploy at scale. This step of reproducibility provides and is one of several key steps in the MLOps journey. – Neal Analytics

The traditional way of delivering ML Systems is common in many businesses, especially when they are just starting out with ML. Manual implementation and deployment is enough when models are rarely changed. A model might fail when applied to real-world data as it fails to adapt to changes in the environment or changes in the data.

MLOps toolset

MLOps frameworks provide a single place to deploy, manage and monitor all your models. Overall, these tools simplify the complex process and save a great deal of time. There are several tools available in the market and provide relatively similar services:

  1. Version Control – Keeping track of any changes in datasets, features, and their transformation.
  2. Track model training – Monitoring performance of models in training
  3. Hyperparameter tuning – Train model using a set of optimal hyperparameters automatically.
  4. Model deployment – Deploying machine learning model into production. check
  5. Model monitoring – Tracking and governing machine learning models deployed into production

Explore more tools

The Best MLOps Tools You Need to Know as a Data Scientist

While choosing any MLOps Tools, the above features are worth considering before you choose. Enterprises might also be interested in the providers where they allow free trials. Let’s have a look at a few MLOps tools:

It accelerates the deployment process by providing Autopilot that can select the best algorithm for the prediction, and can automatically build, train, and tune models.

Neptune focuses on logging and storing of ML Metadata, which makes it easier to query the data to analyse later.

Source: Metadata Store

Neptune has categorized ML metadata into three different areas:

  1. Experiment and model training data – This allows users to log different metrics, hyperparameters, learning curves, predictions, diagnostic charts etc.
  2. Artifact metadata – This contains information about data such as path to dataset, features details, size, last updated timestamp, dataset preview etc.
  3. Model metadata – Model metadata contains information such as who created or trained the model, links to training and experiments done as part of modelling, Multiple datasets details etc.

The above captured data later can be used for:

  1. Experiment tracking – Using Neptune, the team can have a holistic view of ML experiments run by different team members. The team can easily maintain and display various metrics which help them to compare different ML experiments performance.
  2. Model registry – Model registry helps users to know the ML package structure, details about who created the model and when etc. The teams can easily keep track of any changes in sources, datasets and configurations etc. Neptune lets you version, display, and query most of the metadata produced during model building.

Neptune provides an easy to use dashboard display where users can easily sort, filter and query the data. It lets developers focus on model building and takes care of all the bookkeeping.

See an example dashboard here.

In DataRobot, users can import models built using different languages and on other available ML platforms. Models are then tested and deployed on leading ML execution environments. DataRobot monitors service health, data drift, and accuracy using reports and alerts systems.

 

Source: Datarobot

MLflow has four main components:

  1. MLflow Tracking – ML Flow tracking component is organized around the concept of code execution. Each record of execution contains information, like code version, start and end time, code source, input parameters, performance metrics, and output file artifacts.
  2. MLflow projects –MLflow projects provide a convenient way to package machine learning code. Each project is a simple repository with multiple files and properties to run the project, such as Project Name, Entry – Points and the environment, library dependencies, etc.
  3. MLflow models – Mlflow format defines a standard format that lets you save a model in different flavors, such as python-function, PyTorch, sklearn, and it can be used by different platforms without much trouble at all.
  4. MLflow registry – MLflow Model Registry is a centralized place to collaboratively manage the lifecycle of an MLflow Model. It has a set of APIs and UI to register a model, monitor the versioning and stage transition. Developers can easily annotate these models by providing descriptions and any relevant information that can be useful for the team.

With the help of these components, teams can keep track of experiments and follow a standard way to package and deploy models. This makes it easy to produce reusable code. MLflow offers a centralized place to manage the full lifecycle of a model.

Source: Medium

Kubeflow is a platform that helps to tier the ML components on Kubernetes. 

Conceptual diagram of Kubeflow

Kubeflow provides many components, and these components can be used as a standalone service or combined –

  1. Notebook Servers – Kubeflow notebooks help in integrating with other components easily and are easy to share as users can create notebook containers or pods directly in the cluster.
  2. Pipelines – The Kubeflow pipeline platform consists of a UI to manage and track experiments. With the help of pipelines, users can schedule multistep ML workflows and Python support to manipulate pipelines and workflows.
  3. KFServing – KFServing provides serverless inferencing on Kubernetes and encapsulates the complex processes from users by handling them automatically.
  4. Katib – Katib helps to tune hyperparameters of applications written in any programming language. It’s an automated process and runs several training jobs within each tuning. It supports ML frameworks such as PyTorch, XGBoost, TensorFlow, etc.
  5. Training Operators – Kubeflow supports distributed training of machine learning models using different frameworks such as TensorFlow, PyTorch, MXNet, and MPI.
  6. Multi-Tenancy – Kubeflow supports a sharable resource pool across different teams while keeping their individual work secure.

Source: Azure

Azure ML is a cloud-based service for creating and managing machine learning model flow.  Azure ML combined with Azure DevOps help implement continuous integration (CI), continuous delivery (CD), and a retraining pipeline for an AI application.

A typical Azure MLOps architecture can combine components such as Azure ML, Azure pipelines, Container registry, Container Instances, Kubernetes, or Application Performance Insights.

Source: Microsoft Documentations

AIOps

According to Gartner, the average enterprise IT infrastructure generates 2 to 3 times more IT operations data every year. Traditional IT management solutions won’t be able to handle volumes this large and resolve the issues properly.

Enterprises need a solution that’s automated and can alert the IT staff when there’s a significant risk. A system that can tell them what’s wrong, and resolve repetitive issues by itself as well, rather than a staff monitoring the process manually.

AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.”

With the help of AIOps, enterprises can design a solution that can correlate data across different environments. A solution that can provide real-time insight and predictive analysis to IT operations teams, helping IT teams respond to issues efficiently and meet user expectations.

Gartner predicts that for large enterprises, exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.

Core element of AIOps

The definition of AIOps is dynamic, as each enterprise has different needs and accordingly implements AIOps solutions. The focus of AI solutions is to spot and react to real-time issues efficiently. Some core elements of AIOps can help an enterprise to implement AI solutions in IT operations.

Source: Gartner
  1. Machine Learning – AIOps or IT Analytics is about finding patterns. With the help of machine learning, we can apply the computational power of machines to discover these patterns in IT data.
  2. Anomaly detection – Any changes in usual system behavior can lead to downtime, a non-responsive system, and a bad customer experience. With AIOps, it’s possible to detect any kind of unusual behaviors or activities.
  3. Predictive insights – AIOps introduces predictability in IT operations. It can help IT staff to be proactive in capturing any problems before they occur, and it will eventually reduce the number of service desk tickets.
  4. Automated root cause analysis – Driving insights alone is not enough. The enterprise or the IT team should be able to take action as well. In the traditional management environment, IT staff would monitor the systems and take steps as and when required. Due to the increasing volume of IT infrastructure issues, it would be difficult for the staff to manage and resolve the issue on time. It takes a great amount of time to analyze the root cause when multiple systems are involved. With AIOps, the root cause can be done in the background automatically.

AI tiered on DevOps

AppDynamics surveyed 6,000 global IT leaders about application performance monitoring and AIOps.”

Artificial Intelligence for IT Operations and Dev Ops are two independent functions but, when combined, they can help to enhance the functionalities of systems. Managing a DevOps environment can be complex. Imagine going through tons of data to search for a cause that triggered an event. The teams will end up investing for hours. Many issues might be known, and some might be new or relative to previous events. Such events can be identified and resolved automatically.

DevOps is a business approach to deliver services and products to the client/market, and AI can help in streamlining testing, coding, releasing, and monitoring products with precision and efficiency.

Source: Data science Aero

“IDC predicts the global market for custom application development services is forecast to grow from $47B in 2018 to more than $61B in 2023, attaining a 5.3% Compound Annual Growth Rate (CAGR) in five years.” With these increasing demands, it will be impossible to fulfil the requirements with traditional IT Ops or development management.

AI tiered on DevOps will take traditional development management to another level by improving accuracy, quality, and reliability. According to Forbes, “Auto suggesting code segments, improving software quality assurance techniques with automated testing, and streamlining requirements management are core areas where AI is delivering value to DevOps today.

AIOps toolset

AIOps tools consume data from various services. They collect application logs, measure system health or performance, ultimately breaking the siloed IT information problem and bridging between issues of software, hardware, and the cloud.

  • Dynatrace – “The Dynatrace AIOps platform redefines performance monitoring allowing your teams to focus on proactive action, innovation, and better business outcomes.”

Dynatrace helps IT Operations with applications such as Root Cause Analysis, Event Correlation, and mapping to cloud environments to support continuous automation. Dynatrace functions can be categorized as below:

  1. Intelligent Observability – Advanced observability using contextual information, AI, and automation. Understand the full context of the issue and provide actionable answers after a precise root cause analysis.
  2. Continuous Automation – The manual effort of deploying, configuring, and managing is not worth it. Dynatrace proactively identifies the issues and determines their severity in terms of user and business impacts. It helps teams to achieve continuous discovery, effortless deployments, and automatic dependency mapping.
  3. AI-assistance – It performs fault-free analysis for root cause analysis. The Analysis is precise and reproducible. The AI engine is part of every aspect of Dynatrace.
  • AppDynamics– “AppDynamics helps to Prioritize what’s most important to your business and your people so you can see, share and take action in real-time. Turn performance into profit with a deeper understanding of user and application behavior.”

It has different performance measure categories and helps in correlating these metrics from different categories to resolve issues before they can impact business. It’s used for AI-powered application performance management.

AppDynamics
  1. User – Monitor key metrics across any device, browser, or third-party service to proactively identify end-user issues.
  2. Application – Unify IT teams and business by relating end-to-end observability of code affecting the KPIs that matters to the business.
  3. Infrastructure – It helps you to focus on the bottom line. Scale smarter through hybrid infrastructure and create a proactive infrastructure.
  4. Network – Monitor digital experience on any network. Users can correlate application performance with networks to identify application issues caused by network disruption.
  • BMC Helix– “BMC solutions deploy machine learning and advanced analytics as part of a holistic monitoring, event management, capacity, and automation solution to deliver AIOps use cases that help IT Ops run at the speed that digital business demands.”

BMC Helix is a BMC product for Operations Management. It helps teams proactively improve the availability and performance of the system. Helix focuses on service monitoring, event management, and probable cause analysis.

BMC Operation Management

BMC products can help with orchestrated workflows for event remediation, Intelligent ticket resolution, automated change and incident management, and much more.

  • Servicenow – Now Platform – “Now platform delivers cross-enterprise digital workflows that connect people, functions, and systems to accelerate innovation, increase agility, and enhance productivity.”

ServiceNow

It helps teams to work faster and smarter by unleashing the power of AI. The core capabilities of the Now platform that enable efficient digitization of workflows are:

  1. Process Optimization – Now platform maximizes efficiency across enterprises by providing a clear picture of how each system is connected and impacts others. Once the issue is identified, it helps to refine the processes and monitor them.
  2. Performance Analytics – Look at the trends to identify bottlenecks before it occurs and improves the performance as and when required.
  3. Predictive Intelligence – While Now Platform uses machine learning to automate routine tasks and resolve issues faster, Team can focus on more meaningful work. Use ML to classify incidents, recommend solutions, and proactively flag any critical issues.
  4. IntegrationHub – IntegrationHub lets users integrate Now Platform with other ServiceNow services as well as out-of-the-box spokes. It helps to reduce integration costs and improve the productivity of the team.
  5. Virtual Agents – Now platform provides an AI-powered conversational chatbot to help teams and end-users resolve issues faster.
  6. AI Search – Use semantic search capabilities to provide precise and personalized answers.
  7. Configuration management database – Provide visibility into your IT environment to make better decisions. Connect products across the entire digital lifecycle to help teams understand impact and risk.
  • IBM Watson AIOps – “Watson AIOps is an AIOps solution that deploys advanced, explainable AI across the ITOps toolchain so you can confidently assess, diagnose and resolve incidents across mission-critical workloads.”

Watson AIOps is trained to connect the dots across data sources and common IT industry tools in real-time. This helps in detecting and identifying issues quickly and transforming IT Operations with AIOps and ChatOps.

Source: IBM

Watson AIOps takes a set of metrics, logs, and incident-related data for training and building unsupervised models. It’s a ChatOps; the models need to be trained continuously to improve the accuracy of problem-solving.

  • Splunk – “Splunk is the only AIOps platform with end-to-end service monitoring, predictive management, and full-stack visibility across hybrid cloud environments.”

Splunk can help modernize your IT by – preventing downtime using predictive analytics, streamlining incident resolution, and correlating metrics from different services to identify the root cause.

Splunk’s innovations in domain-agnostic, service-centric AIOps give everyone in the Operations team the power to scale and the productivity to achieve faster remediation times.

MLOps vs AIOps

From the above explanations, it might be clear that these are two different domains and don’t overlap each other. Though, people often confuse MLOps and AIOps as one thing. When confused, remember:

  • AIOps is a way to automate the system with the help of ML and Big Data,
  • MLOps is a way to standardize the process of deploying ML systems and filling the gaps between teams, to give all project stakeholders more clarity.

Before we discuss the differences in detail, Let’s see an upfront comparison between MLOps and AIOps:

MLOps
AIOps

Standardizes ML system development process

Automates IT operations and systems

Increases efficiency and productivity of the team

Automates root cause analysis and resolution

Streamline collaboration between different teams

Process and manage a large amount of data effectively and efficiently

It is a crucial part of deploying AI and Data Science at scale and in a repeatable manner

It leverages revolutionary AI technologies to solve IT challenges

– Multi-source data consumptionrn– Source Code Controlrn– Deployment and Test Services-Tracking ML model using metadatarn– Automate ML experimentsrn– Mitigate risks and bias in model validation

– Application Monitoring-Automating manual or repetitive processesrn– Anomaly Detectionrn– Predictive maintenancern– Incident management

AI Ops, or “Artificial Intelligence for IT Operations” is the reverse of MLOps in one respect – it’s the application of ML to DevOps, rather than the application of DevOps to ML.

Let’s now have a look at different use cases and the benefits of implementing MLOps and AIOps.

Advantage of MLOps

As mentioned above, MLOps is focused on creating scalable ML systems. Let’s discuss how it’s different from the traditional way of developing ML Systems and why MLOps is important.

1. Orchestration of multiple pipelines

Machine learning model development is a combination of different pipelines (pre-processing, feature engineering model, model validation, etc). MLOps can help you orchestrate these pipelines to automatically update the model.

2. Managing ML Lifecycle

There are different steps of model development, and it can be challenging to manage and maintain using traditional DevOps. MLOps provides an edge to swiftly optimize and deploy ML models in production.

3. Scale ML Applications

The real issue arises when the data and usage increase, which can cause ML application failures. With MLOps, it’s possible to scale ML applications as and when demand increases.

4. Monitor ML systems

After deploying Machine learning models, it’s crucial to monitor the performance of the system. MLOps provide methods by enabling detection of model and data drifts.

5. Continuous Integration and Deployment

DevOps use continuous integration and deployment in software development but using the same is difficult when it comes to the development of ML systems. MLOps has introduced different tools and techniques where CI and CD can be leveraged to deploy machine learning systems successfully.

Real-life use cases of MLOps

  1. Web Analytics – Coinbase, AT&T
  2. Recommendation systems – OTT and Ecommerce platforms – A recommendation system that’s based on user behaviors while influencing these same behaviors. In this case, monitoring the predictions is essential to avoid a chain reaction.
  3. Share Market Analysis – Bloomberg
  4. Sports Analysis – ESPN, Sky Sports

Advantage of AIOps

AIOps has different use cases and benefits from MLOps as it leverages Machine learning techniques to improve IT Operations.

1. Proactive IT Operations

In a competitive environment, product and service success depends on customers’ satisfaction. Responding to an issue isn’t enough, but it’s crucial to predict if a failure will occur. It’s essential that IT Operations should be able to predict and remediate issues of applications, systems, and infrastructure.

2. Data-driven decision making

AIOps uses ML techniques in IT Operations e.g., Pattern Matching, Historical Data Analysis, and Predictive Analysis. With these ML techniques, the decisions will be purely data-driven and will reduce human error. Such automated response will allow IT operations to focus on resolution rather than detecting root cause.

3. Detecting anomalies and deviation from baseline

Using ML techniques like clustering, IT Operations can detect unusual behavior. AIOps helps in building these monitoring techniques that can be used in anomaly detection over network traffic and automatically modify firewall rules.

Real-life use cases of AIOps

  1. Predictive Alerting: Place Park Technologies and TDC NetDesign
  2. Avoiding Service Disruptions: Schaeffler Group
  3. Proper Monitoring of System: Enablis
  4. Blueprinting and Triaging of Incidents: PostNord AB

Conclusion

Throughout this article, we learned what MLOps and AIOps are, and how they can be used by companies to create effective, scalable, and sustainable systems. I hope you now understand the difference between these two, and where they can be used.

While AIOps is used primarily to act on application data in real-time, MLOps tools monitor similar data for the purposes of building machine learning models. The tools can be used together for businesses that need both feature sets.” – Trust Radius

Thanks for reading, To learn more on how to implement AIOps & MLOps, Click  Here  to schedule a call with our experts.

New Relic AIOps leadership fueled by collaboration with engineers

Within observability, AIOps has emerged as a game changer in the management and operation of complex IT environments, with engineers playing a vital role in safeguarding customer experiences and revenue streams.  New Relic is dedicated to providing data to engineers that allows them to fundamentally reshape the way they build and operate critical services so they can optimize their operations, reduce downtime, accelerate innovation, and enhance customer experiences while maintaining engineer happiness.

We’re especially thrilled that GigaOm acknowledged our AIOps for its advanced data collection, customizable dashboards, predictive capabilities, multi-cloud support, service level management, scalability, manageability & maintainability, and the ease of using our AIOps to quickly identify problems, determine root causes, and aid in remediation. Our top rankings in these key areas demonstrate our commitment to innovation and tangible value for engineers. This new distinction follows our recent recognition as a Leader and Outperformer in the GigaOm Radar for Cloud Observability, which we believe further validates the value and quality we provide to you.

Being a leader in both observability and AIOps, New Relic helps you to efficiently identify and resolve problems in your complex, distributed environments. There’s a noticeable shift away from integrating multiple “feature-focused“ solutions and toward consolidating them onto comprehensive observability platforms. Integrating a collection of tools from feature-focused vendors with a stand-alone AIOps vendor creates significant integration challenges, visibility gaps, and inefficiencies when trying to resolve urgent issues, all while driving costs up. On the other hand, some combined observability platforms have strong capabilities in some areas while only providing “good enough” functionality in others. New Relic customers do not have to settle for this compromise. Thousands of engineers like you have chosen our all-in-one observability platform because of our ability to help you collect, analyze, and troubleshoot telemetry data, and provide an integrated workflow across all of the telemetry coming from your entire stack.  This allows you to deliver world-class digital experiences while maintaining a low total cost of ownership.

We want you and all engineers to continue to choose us because you genuinely love New Relic and have confidence that we’ll continue to build on our strengths like:

  • Massively scalable data ingestion
  • Commitment to open source
  • Built-in automated remediation
  • Seamless API integration with orchestration and incident management tools
  • Observability built directly into your IDE
  • All while maintaining unmatched scalability and reliability

There’s more coming soon on New Relic AI, which will empower engineers to effectively monitor and analyze complex IT environments, making observability more accessible and actionable than ever before. We believe New Relic AI will reimagine how engineers practice observability and drive further ubiquity.

Thank you for getting us here. We are grateful for the confidence you have placed in us and for being integral to our journey. We are excited to continue to deliver for you.

Maximizing The Value Of Observability

At New Relic, we believe strongly that observability can be a driver of business value rather than just a cost. In the 2023 Observability Forecast, we not only identified the state of adoption across a wide range of organization sizes and types, but we were also able to identify patterns from the data for those organizations that got the most value from their observability investments.

In this blog post, I summarize those key findings to help you think about where to prioritize your observability efforts in the years to come to deliver the most value possible.

 

Train and enable teams: Unlock latent value

As a former customer success manager, I’ve seen firsthand the difference in the outcomes for customers who prioritize ongoing enablement and training for their teams vs. those who don’t. In this year’s report, I was pleased to see that 47% of respondents planned to invest in training staff on their observability tools.  Ideally, that number would be even higher as it’s a low-hanging, inexpensive way to tap into more value from the tools you already have.

Without a proper strategy for adoption, individuals tend to fall into their most familiar usage patterns where they use observability in mostly a reactive manner when they’re faced with a problem to resolve. In addition, the habit would be to narrowly focus on just the parts of a service that are their responsibility. Certainly, this is better than not using any observability. Still, it results in missed opportunities to leverage the full benefits of observability data to proactively identify where overall reliability and performance improvements can be realized.

An additional area where expanded use of observability can bring real value to organizations is to increase the level of knowledge all team members have about how the digital services they deliver interoperate to support the business. Team members can elevate their understanding—not to be expert at all of it, but with the benefit of improved context, insight, and collaboration that benefits both reactive and proactive use cases for observability.

Regardless of who you trust for your observability platform, it’s important to build a plan for adoption and ongoing education. At New Relic, our account teams and other subject matter experts can help you develop an adoption plan that gets you proficient quickly. They also partner with you to create a series of continuous engagements and ongoing product education. As the demands on your teams grow and it’s no longer viable to rely on a small group of experts, we can ensure their use of New Relic progresses where they’re delivering quantifiable value to the business every day.

 

Achieve full stack observability: Understand it all

The nature of the technology stack has never been more complicated. Digital services are a combination of often highly specialized technologies. Some are big. Some are small. Some are your own code that you control. Some are third-party services that you don’t control. The net result is a set of interdependent moving parts where failure or issues with any one of the components can hurt your business. It’s not good enough to look at the parts in isolation or worse, leave yourself with blind spots.

One of the most compelling findings from the 2023 Observability Forecast was the improvement in outage frequency and costs for organizations with full-stack observability compared to those without. Nearly two-thirds (65%) of respondents reported an improvement in mean time to resolution (MTTR) since adopting an observability solution. And organizations with full-stack observability had 22–23% fewer outages than those without. On top of that, the outages that did occur cost 37% less to resolve.

Prioritizing full-stack observability also means you can innovate with confidence. As you adopt new technologies such as large language models (LLMs) or other forms of artificial intelligence (AI), it’s not enough to write the code to add these to your services; you must plan for how the new functionality will fit into your observability strategy so the investments in those innovations are not undermined by an inability to support them in production successfully.

 

Reduce data silos: Save money, increase productivity

Under pressure to scale and innovate, the complexity of your key digital services has likely increased. Every new integration or platform you adopt brings its own telemetry data or monitoring tools. Individual teams may have their preferred tools tailored to their needs. Suppose that data lives independently, out of context with the rest of your service. In that case, you’ve created points of friction and toil when you need that data to resolve an incident or eliminate bottlenecks to reduce latency and errors. While the data silos are one problem, any related tool sprawl can also lead to unnecessary spending and budget challenges. The pain now hits on multiple fronts.

Observability platforms like New Relic provide a wide range of essential capabilities in an integrated solution that allows you to eliminate redundant tools while automatically identifying context from a full spectrum of telemetry data. In cases where specialized tools are still needed, the ability to integrate that data can go a long way to eliminate toil and improve productivity.

The 2023 Observability Forecast data show that the reduction in data silos and the number of observability tools is an ongoing challenge for organizations of all sizes. In fact, 41% planned to consolidate tools in the next year to get the most value from their observability investment. Controlling costs of multiple tools along with productivity improvements measured in key performance indicators (KPIs)—such as MTTD and MTTR—will help the denominator portion of any value assessments you perform.  It’s important to do this consolidation and rationalization on a periodic basis to keep unwanted tool sprawl from hurting your operations.

 

Integrate the business perspective: Focusing on your technology is not enough

Let’s say you have a team of super-knowledgeable individuals, complete visibility into all corners of your stack, and tool spending is under control. What’s next? Have you achieved maximum value from your observability strategy? While it is certainly a tremendous accomplishment to achieve all of those best practices and you’re ahead of most organizations, there’s additional value to be realized.

One of the well-worn topics from the last decade-plus is that every business is now a software business. If we apply that idea to observability, we should be aiming to get our technology teams to better understand the true business impact of the software they develop and operate—and not just during a quarterly review or planning session, but also in real time.

Rather than just understanding the common golden signals like latency, throughput, and error rates, they can understand customer journeys, user engagement, order value, and post-sale process flows. It’s tremendously rewarding for engineers to get closer to these key outcomes so they feel a sense of pride and accountability for the results the business truly cares about.

At New Relic we believe strongly enough in this objective that we’ve developed a turnkey app called Pathpoint, which builds on the foundation of the telemetry data we collect, the analysis of that data, and the ability to create compelling visualizations that can be used by stakeholders across the organization, not just the technology teams. Solutions like New Relic Pathpoint make it clear that the right observability strategy delivers a higher level of value to the business.

Conclusion

Practically no organizations would imagine themselves operating their important digital services without some level of observability. But without a clear plan to focus on the most important value-based outcomes, the costs of observability can grow unexpectedly. What I’ve outlined in this blog post is a series of recommendations and goals for how observability can be so much more than just a cost item in your budget. I encourage you to regularly evaluate the current state of observability practice for ways to improve the value you receive from your spend and your level of adoption.

To learn more on how to maximize your Observability value, Click Here to schedule a call with our experts.

The business value of observability: Insights from the 2023 Observability Forecast

1. Business value and ROI

The standout theme from this year’s report is the tangible business value of observability. Organizations are not just adopting observability for the sake of technology—they’re seeing it as a strategic move to achieve core business objectives. The results? Fewer outages, improved service-level metrics, operational cost savings, and increased revenue.

The numbers speak for themselves. For example, survey respondents indicated a 2x median annual ROI. That means for every dollar invested in observability, organizations are seeing a return of two dollars. An impressive 86% of respondents affirmed the value they receive from their observability investments, with 41% reporting over $1 million in total annual value. This ROI isn’t just a number; it’s a testament to the transformative power of observability on business, technology, and revenue streams.

Without observability, organizations risk higher operational costs and significant revenue loss from downtime. In fact, respondents cited improved system uptime and reliability (40%),  increased operational efficiency (38%), and enhanced user experience (27%) as primary benefits.

2. The power of full-stack observability

To accelerate digital transformation initiatives, organizations are increasingly monitoring their tech stack end to end.

While most organizations still don’t monitor their full tech stack, this is changing. Full-stack observability increased 58% year over year (YoY). By mid-2026, at least 82% of respondents expected to deploy each of the 17 different observability capabilities.

The fast adoption of full-stack observability is likely tied to the value it unlocks for organizations. The more capabilities an organization deploys, the greater the value derived from observability. Those with five or more capabilities deployed were 82% more likely to report over $1 million in annual value from their observability investments.

Organizations that achieve full-stack observability improve service-level metrics as well—particularly mean time to resolution (MTTR) and mean time to detection (MTTD). Respondents who said their organization has more than five capabilities currently deployed were 40% more likely to detect high-business-impact outages in 30 minutes or less, compared to those with one to four capabilities currently deployed. Organizations with full-stack observability had median outage costs of $6.17 million per year compared to $9.83 million per year for those without full-stack observability—a cost savings of $3.66 million per year.

3. Boosting performance and productivity

Increasingly, businesses rely on observability to drive workplace efficiencies, innovation, and agility, and meet customer demands with exceptional digital experiences.

For practitioners, observability is a tool that boosts productivity, enabling faster issue detection and resolution. For IT decision makers (ITDMs), it’s a strategic asset, helping achieve both technical and business key performance indicators (KPIs). About a third (35%) of ITDMs said it helps them achieve technical KPIs and/or business KPIs (31%). Almost half (46%) of practitioners said it increases their productivity so they can find and resolve issues faster.

4. The high cost of ignoring observability

The benefits of implementing observability are clear. What happens when organizations forgo this crucial practice? The 2023 Observability Forecast provides some sobering insights into the business outcomes of not having an observability solution.

A staggering 96% of respondents indicated that the absence of an observability solution would have a significant financial impact on their business outcomes. About three in ten (29%) of respondents cited higher operational costs due to increased operational efforts as the most severe consequence. This was closely followed by 23% who pointed to revenue loss from increased downtime.

Only 3% of respondents felt that the absence of an observability solution would have no impact on their business outcomes. The overwhelming majority of technology professionals recognize the critical role that observability plays in modern business operations.

Conclusion

The data is unequivocal: the absence of an observability solution carries hard financial stakes and can have a ripple effect on other aspects of business, from reputation to competitive positioning. For decision makers, the message is even more transparent. Observability is not a luxury or an optional add-on; it’s a necessity. Businesses must empower every engineer to do better work with data at every stage of the software development lifecycle (SDLC) to improve business outcomes and compete in an increasingly complex digital landscape.
By investing in observability, you’re not just avoiding potential pitfalls; you’re actively driving your business towards greater efficiency, security, and profitability. As the 2023 Observability Forecast  shows, the return on this investment is not just beneficial; it’s essential.

To understand how you could generate over 2*ROI on your Observability ops, Click Here  to schedule a free 30-mins call with our experts today.

Webiscope is now part of Aman Group

We are happy to announce that Webiscope is now part of Aman Group. We look forward giving our customers and partners greater value with more complete solutions and outstanding service.