Artificial intelligence adoption is on the rise everywhere—throughout industries and in businesses of all sizes. And while generative AI was much hyped in 2023, the deterministic nature of causal AI—which determines the precise root cause of an issue—is a key foundational requirement to get reliable decisions and recommendations from generative AI technologies.

Further, not every business uses AI in the same way or for the same reasons. So, it’s important for organizations to choose the AI type that best meets their needs. While predictive AI relies on machine learning algorithms that find correlations in data, causal AI aims to determine the precise underlying mechanisms that drive events and outcomes. As a result, causal AI use cases are key to enabling organizations to identify the root cause of problems and determine remediation.

Making the case for causal AI

Most AI today uses machine learning models like neural networks that find correlations and make predictions based on them. However, correlation does not imply causation. So, these models are limited in their ability to explain why outputs occurred or to make reliable decisions in new situations. They’re essentially informed guesses or likelihoods of outcomes. The growing recognition of these limitations is driving increased interest and research into causal AI use cases.

Causal AI use cases for modern observability

Integrating causal AI into observability systems can significantly advance organizations’ understanding of their environments. Traditional monitoring tools can alert organizations to issues, but causal AI can precisely identify the root cause of operational and quality issues. This facilitates quicker and more effective problem solving, reducing downtime and improving reliability through intelligent automation.

More generally, causal AI can contribute to explainable and fair AI systems. That’s important as regulatory scrutiny and demands for responsible AI are growing. According to a recent Dynatrace survey of 1,300 CIOs, CTOs, and other senior technology leaders, 98% of technology leaders are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation. AI systems’ ability to explain the reasons for their recommendations grounded in causal AI could go a long way in resolving these trust issues.

Take causal AI to the next level with a composite approach

The benefits of causal AI are obvious, as it determines the exact underlying causes and effects of a digital system’s events or behaviors based on the system’s topology. The same cannot be said for predictive AI, which makes predictions about future events based on data patterns, and generative AI, which uses training data to create content that reflects its users’ natural language queries. But nothing is perfect, and each AI type has specific capabilities and limitations.

That’s where Dynatrace can help. Dynatrace takes a composite approach, called hypermodal AI, which combines causal, predictive, and generative AI to drive fast, precise, and trustworthy answers and automation.

This hypermodal approach features the following:

Automated root-cause analysis. Dynatrace automated root-cause analysis uses causal AI to rapidly pinpoint the source issues behind user experience, application, and infrastructure performance problems before they result in outages. Through dependency mapping, Dynatrace causal AI can contextualize and explain incident alerts, saving teams substantial time compared with manual troubleshooting across complex, modern IT environments.

Intelligent alert prioritization. By determining the likely business effects of service issues using causal AI, Dynatrace automatically prioritizes issues and alerts the relevant teams while allowing auto-remediation on routine alerts. This reduces alert fatigue and speeds up the restoration of critical systems through auto-remediation.

Failure prediction. Dynatrace uses causality graphs and analysis of the sequence of events to determine how chains of dependent application or infrastructure events will potentially lead to slowdowns, failures, and outages. By predicting failure risk, Dynatrace enables pre-emptive changes such as resource autoscaling, traffic shifting, or preventative rollbacks of bad code deployment ahead of time.

Resource optimization. Dynatrace uses the causal relationships between events across user experience, application, and infrastructure layers and ties them to business KPIs to optimize dynamic policy decisions for cloud resource or container scaling and cloud cost optimization to meet performance and efficiency goals even in highly dynamic and complex environments.

Automated remediation. For well-defined remediation processes, teams can automate remediation tasks, such as server restarts, spinning up new nodes, code rollbacks, and configuration changes based on the determination of causal AI—all without manual intervention in many cases.

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Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. That’s where causal AI can help.

Causal AI is a different approach that goes beyond event correlations to understand the underlying reasons for trends and patterns. It uses fault-tree analysis to identify the component events that cause outcomes at a higher level. Causal AI is particularly effective in observability. It removes much of the guesswork of untangling complex system issues and establishes with certainty why a problem occurred.

Causal AI applies a deterministic approach to anomaly detection and root-cause analysis that yields precise, continuous, and actionable insights in real time. But to be successful, data quality is critical. High-quality data creates the foundation for credible insights organizations can use to make sound decisions.

In what follows, we’ll discuss how to assess data quality, common data quality challenges, how to overcome them, and more.

Key considerations for assessing data quality

Assessing data quality requires organizations to consider several key factors, including the following:

Accuracy. Teams need to ensure the data is accurate and correctly represents real-world scenarios. Additionally, it’s important to consider all variables.

Completeness. Is any information missing from the data set? Omissions can create wrong conclusions and contribute to bias.

Consistency. Ensure there are no discrepancies in the data. Contradictory or inconsistent data confuses AI models and increases the risk of errors.

Timeliness. The data should be up-to-date and relevant to the current context. Timeliness is a critical factor in AI for IT operations (AIOps). Because IT systems change often, AI models trained only on historical data struggle to diagnose novel events. Causal AI requires real-time updates to the training model.

Relevancy. The data needs to be appropriate for the questions asked. In AIOps, this means providing the model with the full range of logs, events, metrics, and traces needed to understand the inner workings of a complex system.

How can organizations improve data quality?

Improving data quality is a strategic process that involves all organizational members who create and use data. It starts with implementing data governance practices, which set standards and policies for data use and management in areas such as quality, security, compliance, storage, stewardship, and integration.

Data stewardship is an increasingly important factor in data quality. It ensures the data people and departments generate and maintain is clean, consistent, and complete. Data mesh is a popular new concept that encourages the people who create data to treat it as a product to be managed like any other product. But it suffers from limitations such as multiple copies of data. High-quality operational data in a central data lakehouse that is available for instant analytics is often teams’ preferred way to get consistent and accurate answers and insights.

Data-cleaning tools and methods are needed to identify and fix errors. Additionally, teams should perform continuous audits to evaluate data against benchmarks and implement best practices for ensuring data quality.

Common data quality challenges to consider

Organizations may encounter numerous barriers to ensuring data quality. For starters, the sheer amount of data can make management daunting. Modern, cloud-native architectures have many moving parts, and identifying them all is a daunting task with human effort alone. Modern observability solutions that automatically and instantly detect all IT assets in an environment — applications, containers, services, processes, and infrastructure — can save time.

Fragmented and siloed data storage can create inconsistencies and redundancies. Stakeholders need to put aside ownership issues and agree to share information about the systems they oversee, including success factors and critical metrics.

Another common impediment is manual data tagging and handling, an error-prone process that teams should minimize. Observability solutions automate much of the task of identifying the variables that go into application performance and availability. Human involvement should be limited to verifying the features or attributes machine learning algorithms use to make predictions or decisions.

Improving data quality management using causal AI

Causal AI can be a powerful tool for improving systems management, observability, and troubleshooting. It can highlight inconsistencies or outliers in data sets that indicate anomalies and pinpoint the root causes. It also enables an AIOps approach with proactive visibility that helps companies improve operational efficiency and reduce false-positive alerts by 95%, according to a Forrester Consulting report.

Causal AI informs better data governance policies by providing insight into how to improve data quality. It improves time management and event prioritization by helping developers, administrators, and site reliability engineers identify the alerts that matter most. Identifying issues before an application or service outage occurs can reduce costs. IT teams can focus on strategic initiatives to drive business success, rather than firefighting — and it accelerates digital transformation through automation and self-maintaining systems.

Unleash the power of causal AI

Dynatrace provides an AI-powered, automated IT performance monitoring platform with advanced observability and analytics capabilities. It enables real-time health and performance tracking, intelligent anomaly detection, data quality controls, and automated issue resolution.

By accurately assessing, managing, and continuously improving data quality, organizations can use causal AI to its full potential. Platforms such as Dynatrace help ensure that data quality rises to the standard required for effective causal analysis.

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Artificial intelligence is rapidly transforming the world around us, with applications based on AI emerging in virtually every industry and sector.

This trend has accelerated with the recent democratization of access to generative AI-driven solutions. However, as AI systems become more complex and sophisticated, organizations are learning that they need to ensure the AI they use is responsible and trustworthy.

Data suggests that organizations are quite concerned about the role of AI in making responsible, well-informed decisions. Indeed, according to the recent Dynatrace report, “The state of AI,” 98% of 1,300 technology leaders are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation.

There is an increased focus on trusted, responsible AI because when the following factors are overlooked, they can cause significant financial, business, and legal repercussions:

• The opacity of algorithms. It can be difficult to understand the basis of AI systems’ decisions, particularly when they are trained on large and complex data sets.
• AI system bias. AI systems and their data can be biased, either intentionally or unintentionally, reflecting the biases of their creators or the data on which they are trained.
• Unauthorized usage of data for AI. Every organization needs to carefully consider how to minimize the risk of AI accessing and using data without authorization—and not just a company’s own data, but also customer and user information.

Responsible AI approach at the core

To support a responsible AI approach, organizations need to consider the integrity of their broader strategy for monitoring IT systems. To this end, they need an approach to IT system monitoring that can promote accurate, unbiased, and timely data inputs.

Organizations need an observability platform that can gather, store, and analyze data in a unified manner and retain proper context. This data context becomes the foundation for training AI algorithms with unbiased, accurate, secure, and timely data.

Moreover, a unified approach to observability enables organizations to ensure a responsible approach to AI by providing transparency into how the algorithms arrive at decisions. This enables organizations to ensure that the data insights are devoid of bias and supported by fact-based inputs.

Dynatrace collects and analyzes large amounts of observability and security data. Then, Dynatrace converts this data into precise answers that customers need to simplify cloud operations and deliver flawless and secure digital experiences using a responsible AI approach.

Transparent and explainable AI. Users get full transparency into how Davis AI derives answers and which techniques it has used. Users are in control of each phase of Davis AI processing to ensure data privacy, eliminate bias, and promote fairness.

Trusted data. Customers have full control over the data that Dynatrace Davis AI uses. They can choose which data to share with Dynatrace that Davis AI can use to generate answers. At any time, they can investigate what system data Davis AI is evaluating. This approach gives users the control they need to ensure the data Davis AI trains on and processes.

Data in context. Davis AI makes sure that data is used in the context set up by Smartscape, a real-time, dynamic dependency map that visualizes all application components, and OneAgent, a single agent that provides a set of specialized services that have been configured specifically for your monitoring environment. All the relevant information collected and the associated real-time topology information is put to use.

Causal AI that’s repeatable. Unlike probabilistic approaches, Davis AI delivers causal, deterministic answers that are repeatable—causal AI can identify precise cause and effect. At Dynatrace, we continually test Davis AI to ensure repeatable and reliable results.

Data privacy and end-to-end security. Dynatrace embeds data privacy principles into the core of the platform. This gives customers the ability to extend protections beyond the minimum legal requirements when it comes to protecting customer data. Independent security certifications (FedRAMP, StateRAMP, ISO2700, and SOC2 Type II) and regular independent penetration testing ensure the data security and privacy controls implemented by Dynatrace meet the stringent compliance requirements.

Choosing responsible AI for hundreds of use cases

Dynatrace enables organizations to use the power of AI responsibly to optimize their IT operations. These capabilities can automate tasks, identify anomalies, and make predictions. Dynatrace AI is easy to use and provides actionable insights that can help organizations improve their IT performance. Some of the most common use cases include the following:

• Anomaly detection. Davis AI uses multidimensional baselining to automatically detect anomalies in the response times and error rates of applications and predictive AI to detect abnormalities in application traffic and service load.
• Root-cause analysis. Davis AI automatically detects customer-facing issues and uses topology, transaction, and code-level information to precisely pinpoint a problem’s root cause. This can help organizations to identify potential problems before they cause outages by proactively remedying them.
• Predictive operations. Davis AI can predict when issues will occur, preempt or resolve these issues, and ensure reliable operations. For example, predictive disk resizing and autoscaling resources. The teams can help organizations to prevent outages and to extend the lifespan of equipment.

Trusted AI from Dynatrace is a powerful solution that provides organizations with the control and transparency they need to use AI safely and ethically for building and running resilient software securely.

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Predictive AI is an important technology that busy technology and operations teams can use to ensure that applications and digital systems work seamlessly and securely.

Because IT teams handle complex infrastructure to maintain service availability, they need to respond swiftly to incidents as they arise. But when these teams work in largely manual ways, they can miss the first signs of incidents as they’re building. As a result, incidents can build into outages, and teams have to respond to crises instead of innovating on strategic projects that have more value.

By integrating predictive artificial intelligence (AI) into team workflows, they can more easily meet service-level objectives, collaborate effectively, and boost productivity.

What is predictive AI?

Predictive AI is a type of machine learning (ML) that uses advanced algorithms to analyze large datasets to identify hidden patterns and relationships between variables. In data analytics, predictive AI trains ML models to learn from historical data and make predictions about future events, scenarios, or outcomes based on patterns from that data.

Organizations in finance, health care, marketing, manufacturing, and other industries widely use predictive AI. It enables organizations to make better data-driven decisions, optimize processes, and anticipate future trends. How accurate those predictions are based on historical data depends on the training data’s quality and relevance. Predictions’ accuracy also depends on the robustness of the classification and ML models, such as decision trees, neural networks, or regression algorithms.

By analyzing patterns and trends, predictive analytics helps identify potential issues or opportunities, enabling proactive actions to prevent problems or capitalize on advantageous situations.

When teams combine predictive AI with a data lakehouse such as Dynatrace Grail, it can deliver even greater value. With access to complete data sets in context, predictive AI can automatically provide prescriptive insights using data from the digital user experience layer to the infrastructure layer. This access provides full data context using supporting data, such as relationships, dependencies, and usability data within entities and events.

While investigative techniques such as root-cause analysis are essential for teams striving to understand issues that have already occurred, predictive AI techniques such as forecasting and anomaly prediction help teams preempt issues. With the advances in causal AI (that is, AI that can explain cause and effect by identifying root-cause issues), teams want to take it to the next level and combine it with predictive AI to create a seamless foresight-to-hindsight continuum of data-driven answers and prescriptive insights.

The importance of predictive AI for ITOps, DevSecOps, and SRE teams

1. Early detection of anomalies. Predictive AI empowers site reliability engineers (SREs) and DevOps engineers to detect anomalies and irregular patterns in their systems long before they escalate into critical incidents. By identifying subtle deviations in system behavior, engineers can take preemptive measures to avert potential downtime, performance issues, or security threats.
2. Proactive resource allocation. Through predictive analytics, SREs and DevOps engineers can accurately forecast resource needs based on historical data. This enables efficient resource allocation, avoiding unnecessary expenses and ensuring optimal performance.
3. Capacity planning. Understanding future capacity requirements is crucial for maintaining system stability. Predictive AI assists engineers in predicting demand fluctuations and adjusting resource capacities accordingly, ensuring seamless user experiences.
4. Enhanced incident response. Predictive analytics can anticipate potential failures and security breaches. SREs and DevOps engineers can implement targeted remediation strategies and prioritize incident response efforts to minimize the impact on systems and users.
5. Continuous improvement. By analyzing past incidents and performance metrics, predictive analytics helps SREs and DevOps engineers identify areas for improvement. This data-driven approach fosters continuous refinement of processes and systems.

Predictive AI-based capacity management and automation

Proactive capacity management is essential for avoiding outages and ensuring that an organization’s applications and services are always available. Operators need to closely observe business-critical resource capacities such as storage, CPU, and memory to avoid outages that are driven by resource shortages. However, traditional capacity management approaches are often reactive and time-consuming. Using Dynatrace Grail and Davis AI, predictive capacity management is straightforward:

• use Notebooks to explore important capacity indicators;
• create workflows to trigger forecast reporting at regular intervals; and
• use Davis AI for Workflows to automate the prediction and remediation of future capacity demands.

Benefits of predictive AI for capacity management

Predictive capacity management is a powerful tool that can help improve the availability and performance of applications and services. By using Dynatrace Grail and Davis AI, you can gain the insights you need to make proactive decisions about capacity planning and gain the following additional benefits:

• Increased visibility into future capacity demands. Predictive capacity management can help you to anticipate what your future capacity demands will likely be. This provides organizations with the ability to make proactive decisions about capacity planning, such as adding additional resources or scaling back resources that are not being used.
• Improved decision making for capacity planning. With predictive capacity management, you can make more informed decisions about capacity planning. This is because you have a better understanding of your future capacity demands and the impact of those demands on applications and services.
• Reduced costs associated with unplanned capacity increases. Unplanned capacity increases are costly. Organizations may need to purchase additional resources or pay for overtime. Predictive capacity management can reduce these costs by enabling organizations to plan for future capacity demands.
• Increased customer satisfaction. When your applications and services are available and performing well, your customers are happy. Predictive capacity management can help you to improve customer satisfaction by reducing the number of outages and performance problems.

Predictive AI helps teams avoid costly problems

This is just one example of predictive AI in action. But for ITOps, DevSecOps, and SRE teams, predictive AI presents numerous use cases for gaining foresight into issues and pre-emptively addressing them before they escalate into costly problems. They see improved efficiency, reduced risks of security breaches, and better compliance with industry regulations.

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Today’s organizations need to solve increasingly complex human problems, making advancements in artificial intelligence (AI) more important than ever. Conventional data science approaches and analytics platforms can predict the correlation between an event and possible sources. But they often fall short when it comes to understanding why an event occurred. That’s where causal AI, also referred to as deterministic AI, makes a crucial difference.

In what follows, we’ll discuss causal AI, how it works, and how it compares to other types of artificial intelligence. We’ll also discuss why it’s essential for business success in the age of generative AI.

What is causal AI?

Causal AI is an artificial intelligence technique used to determine the exact underlying causes and effects of events or behaviors. Unlike correlation-based machine learning, which calculates probabilities based on statistics, causal AI uses fault-tree analysis to determine system-level failures based on component-level failures. With this systematic, top-down approach, causal AI and modern deterministic AIOps provide a determinative basis for automatic anomaly detection, root-cause analysis, security risk ranking, and business impact assessment.

Causal AI draws on supporting data, such as relationships, dependencies, and other context among network entities and events. With this context, causal AI determines the precise root cause of an issue. This approach helps teams to develop effective models or interventions for change while also predicting their potential effectiveness. It can increase confidence in business and IT decision making by clearly connecting events to an intended or unintended outcome.

The deterministic quality of causal AI can also form the foundation for reliable recommendations from emerging generative AI technologies.

Why is causal AI important?

Most AIOps approaches use predictive analytics that apply algorithms and machine learning to historical data to predict future outcomes. Such an outcome could be a CPU spike that progresses into a system failure. Predictive analysis helps an organization manage resources and improve incident response times.

This blind spot between the underlying cause and resulting effect can lead to unwanted bias and poor decision making. Predictive analysis can observe an event and predict an outcome will occur, but it can’t show that the outcome occurred because of the event. In other words, correlation doesn’t equal causation.

Causal AI, on the other hand, identifies the underlying cause of an event and its precise relationship to the outcome. Organizations can use causal AI frameworks and algorithms to ask questions and gain a deeper understanding of their CloudOps, DevOps, and SecOps use cases. For instance, these questions can include the following:

• Why aren’t customers completing their transactions?
• What’s causing customer churn?
• Why is this application sluggish at certain times of the day?

Additionally, the deterministic AI approach of causal AI can determine the cause-and-effect relationship of events from a combination of metrics, traces, and log data, as well as user behavior data and other details. Thus, teams can resolve incidents immediately to prevent disruptions in service and keep an organization in compliance with service-level agreements.

Correlation AI vs. causal AI: Weighing the differences

Correlation-based machine learning models predict outcomes from statistical relationships and are useful in many scenarios. For example, facial recognition, personal shopping, and predictive maintenance.

However, the shortcomings of correlation-based AI become evident when teams need to determine how an action would affect an outcome. While predictive models can identify the likelihood of certain positive or negative events happening, they’re unable to explain how they arrived at that forecast. They’re also unable to identify the underlying factors and cause-and-effect relationships.

Correlation-based AI and causal AI have a few additional differences, including the following:

How does causal AI work?

Causal AI essentially works in two steps. First, it collects information and discovers problems within the data set. Then, it looks for causal relationships that help explain those issues using a plan devised from the collected data.

To better understand how causal AI works, it’s important to understand fault-tree analysis—a data-driven, fault-tree methodology used for causality analysis. Fault-tree analysis uses boolean logic to explore system-level failures. It’s a top-down approach used to identify the component-level failure, or basic event, that caused the system-level failure, or top event.

Causal AI that uses fault-tree analysis works the following way:

1. Defines the scope of the system and what’s considered a failure.
2. Defines top-level faults and the analysis starting point with details of the failure.
3. Identifies precipitating events that could cause the top-level fault to occur, whether alone or with multiple concurring events.
4. Finds the root causes of each precipitating event and event sequence.
5. Analyzes the fault tree by looking for the events that lead to failure or are most likely to fail.

With the certainty of this systematic approach, teams can gain insight into ways to mitigate paths to failure and support system improvements, and automate resolutions.

Applying causal AI to your organization

Dynatrace Davis® AI offers continuous causal analysis to the code level that maps and understands the relationships between all of an organization’s networks, applications, and services. Using fault-tree analysis, this causal AI approach seamlessly combines topological context with metric data to quickly identify observability signals for any behavior of interest. The analysis provides insights into every entity a problem affects, enabling developers to solve problems without having to reproduce errors.

With its deterministic AI approach, causal AI provides the perfect basis for automating responses and supplying facts for reliable generative AI recommendations.

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In today’s digital world, software is everywhere. And it covers more than just applications, application programming interfaces, and microservices. Software is behind most of our human and business interactions. This, in turn, accelerates the need for businesses to implement the practice of software automation to improve and streamline processes.

In what follows, we define software automation as well as software analytics and outline their importance. We also discuss the role of AI for IT operations (AIOps) and more.

What is software automation?

Software automation is the practice of creating software applications to reduce or eliminate human intervention in repetitive, time-consuming IT tasks and cloud operations. Software automation enables digital supply chain stakeholders — such as digital operations, DevSecOps, ITOps, and CloudOps teams — to orchestrate resources across the software development lifecycle to bring innovative, high-quality products and services to market faster.

What is software analytics?

Software analytics offers the ability to gain and share insights from data emitted by software systems and related operational processes to develop higher-quality software faster while operating it efficiently and securely. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. It provides valuable insight into complex public, private, and hybrid cloud IT structures, systems, and frameworks.

Practical applications of software analytics include the following:

• measuring the health of software systems in relation to security, efficiency, complexity, transferability, and cloud readiness;
• determining the functional and technical size of software to be developed according to technical specifications;
• detecting software flaws to prevent potential outages, data corruption or theft, and security breaches;
• visualizing software structure and private, public, and hybrid cloud architecture; and
• assessing performance by implementing software benchmarks.

Additionally, software analytics enhances the digital customer experience by enabling faster service for high-quality offerings.

Software analytics enables software automation

Software analytics enables software automation with targeted machine learning and AI algorithms that mimic the way humans think to perform repetitive tasks. These algorithms analyze massive amounts of complex structured and unstructured data to enhance human decision making and performance.

Intelligent software automation combines cognitive and AI technologies, such as natural language processing, to build smart processes and workflows that learn, adapt, and improve with every IT software instance or digital transaction. The result is increased efficiency, reduced operating costs, and enhanced productivity.

Primary intelligent software automation use cases for any business include the following:

1. Development. Automate DevOps pipelines to create better software faster to free up critical DevOps and IT time for new initiatives and innovation. Consider how AI-enabled chatbots such as ChatGPT and Google Bard help DevOps teams write code snippets or resolve problems in custom code without time-consuming human intervention.
2. Operations. Automatically predict and resolve problems before they affect users with precise, AI-powered insights and real-time remediation.
3. Business. Boost conversions and revenue with machine learning and AI-powered deep understanding to optimize user interactions.

The importance of software automation and analytics

Software automation is the backbone of digital transformation and modern IT environments. Implementing a unified software automation and analytics platform to power observability, security, and business solutions for various practices eliminates tedious, repetitive tasks, reduces human error, and frees up valuable time to focus on high-value initiatives.

Intelligent software automation can give organizations a competitive edge by analyzing historical and compute workload data in real time to automatically provision and deprovision virtual machines and Kubernetes. This way, organizations can meet employee and customer demand 24/7 while staying optimized with respect to budget and resources.

The role of AIOps in software automation and analytics

In software automation and analytics, AIOps’ role is to apply advanced machine learning and AI analytics to automate cloud operations. DevSecOps and ITOps teams can then perform tasks with accuracy at the speed a business requires.

The core benefits of an AIOps-automated software analytics platform include the following:

• Infrastructure monitoring. Instantly analyze massive volumes of observability, security, and business data for precise, AI-powered insights.
• Applications and microservices monitoring. Automatically connect distributed traces with logs for improved application availability, performance, and agility.
• Application security. Investigate network systems and application security incidents quickly for near-real-time remediation.
• Digital experience. Improve user experience with fast, reliable application performance across all digital channels. These include mobile, web, Internet of Things, and application programming interfaces.
• Business observability. Analyze business data contextually from any source to unlock new business use cases.
• Cloud automation. Automate DevSecOps processes at scale.

AIOps also increases the quality and accuracy of the information used to evaluate the performance of IT networks, systems, and cloud-native infrastructure, resulting in faster mean time to detect and mean time to repair.

How to implement software automation

Implementing software automation will be a different process with different goals for each enterprise, depending on where the organization is on its path to digital transformation. There is no one-size-fits-all approach, but there are common goals, such as optimizing automation, self-healing, vulnerability management, and observability across the software development lifecycle.

One way to accomplish this is by implementing AIOps as part of an organization’s larger cloud adoption strategy. AIOps intelligent cloud automation provides self-service cloud observability and monitoring-as-code approaches that allow DevSecOps, ITOps, and business teams to build feedback loops into their applications automatically with just a few clicks.

Upgrade your data insights with a lakehouse for software and business data

Implementing AIOps intelligent software automation as part of any cloud adoption strategy is critical to driving innovation and achieving faster time to market for software products and digital services. Powered by big data, adding a causational data lakehouse approach to the increasing volumes of data provides greater capacity to cost-efficiently unify, store, and contextually analyze this big data in real time and near-real time.

The Dynatrace unified software automation and analytics platform, featuring the Davis AI engine, can help an organization jump-start its automation journey with precise, AI-driven insights, root-cause analysis, automated remediation, and intelligent full-stack observability.

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Cloud environments have become ever more complex, with an increasingly interconnected set of services. To tame this complexity and deliver differentiated digital experiences, IT, development, security, and business teams need automated workflows throughout these cloud ecosystems. But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise.

IT leaders know that managing cloud environments through traditional manual monitoring practices will no longer suffice. According to recent Dynatrace data, 59% of CIOs say the increasing complexity of their technology stack could soon overload their teams without a more automated approach to IT operations and automated workflows.

Further, according to a Gartner® report, by 2025, 95% of decisions that currently use data will be at least partially automated.*

Development teams need automated workflows so they’re not stuck manually monitoring all stages of the software development lifecycle in their cloud environments. Similarly, security teams need to discover and automatically route security vulnerabilities to the right people to ensure prompt action. And operations teams need to forecast cloud infrastructure and compute resource requirements, then automatically provision resources to optimize digital customer experiences.

With the Dynatrace modern observability platform, teams can now use intuitive, low-code/no-code toolsets and causal AI to extend answer-driven automation for business, development and security workflows.

Low-code/no-code AutomationEngine fuels workflow automation

The Dynatrace platform brings modern observability and workflow automation to the fore.

With AutomationEngine, IT teams can now use their observability, security, and business data to automate workflows throughout hybrid and multicloud ecosystems.

For example, with AutomationEngine, teams can automate remediation and progressive delivery to continuously evaluate application performance against specific, measurable service-level objectives.

AutomationEngine also enables automated routing of security vulnerabilities to the proper teams while reducing false positives to ensure prompt action.

But automating workflows requires precise, accurate, and real-time answers from data that’s trustworthy.

This is the role of Dynatrace causal AI, fueled by Davis and topological mapping through Smartscape. With Davis and Smartscape, the Dynatrace platform provides teams with precise answers about the source of problems and incidents. Then, teams can use the AutomationEngine and its easy low-code/no-code interface to create automated workflows to enable various tasks that previously required manual work. An automated workflow, for example, might identify an application issue, send an alert to ServiceNow, generate a help desk ticket, and even trigger a remediation step.

All these tasks can take place seamlessly—ultimately resolving issues or identifying them proactively—and without needing to interrupt an engineer from a more strategic task.

Using Dynatrace AutomationEngine, teams can forecast future requirements and automate the provisioning of cloud infrastructure and compute resources to optimize the user experience. In addition, they can automatically route precise answers about performance and security anomalies to relevant teams to ensure action in a timely and efficient manner.

Bringing precise, answer-driven automation to observability, security, and business

As IT operations teams, security, DevOps, and others look to automate workflows, they need a solid platform foundation that enables data in context as well as provides precise and trustworthy answers.

To date, traditional observability tools ingest and process only partial data in silos which makes them ineffective to truly address application performance or security issues. Others lack causal AI to process data in full context that can actually pinpoint the root cause of problems.

With a data lakehouse, DevSecOps and business teams can aggregate, store, and centralize structured and unstructured data in one cost-efficient repository without predetermining which data is going to be necessary to generate insights in the future.

The Dynatrace Grail data lakehouse enables teams to ingest logs, metrics, traces, business events, and other data to get a full picture of their hybrid and multicloud environments. Because all the data is already contextualized and centralized, teams no longer have to manually sift through data and alerts to identify the team responsible for the problem. In turn, AutomationEngine uses this data in context to eliminate manual tasks and enable data-driven, automated workflows.

The low-code/no-code AutomationEngine brings several benefits to customers.

1. AutomationEngine enables user-friendly low-code/no-code automation

Even non-automation engineers can easily create ad-hoc automated workflows using a low-code/no-code interface. For example, teams can schedule a routine task or routing alerts with a visual drag-and-drop workflow creation experience. Operations teams can visualize their runbook automation as boxes and lines with flow logic for operational tasks. For example, using drag-and-drop on visual elements, teams can create an automation that queries a ticketing system like Jira or sends a Slack message to a specific person upon discovering an issue or vulnerability.

Cloud-native engineers benefit by creating GitOps-style automation-as-code and can evaluate Kubernetes events for faster issue remediation. And, ultimately, platform and site reliability engineers can provide answer-driven automation and higher-quality software with automated security and quality gates.

2. Tool consolidation reduces complexity

AutomationEngine reduces the number of home-grown tools teams need to rely on and improves the interoperability of ecosystem tools. As a result, it becomes easier to automate processes and reduce complexity. Further, AutomationEngine provides a centralized platform for managing and optimizing automation processes. This helps organizations chart a path to automation, improving efficiency and reducing the risk of errors or inconsistencies.

3. Compliance and data privacy are built into automated workflows

With AutomationEngine woven into the Dynatrace platform, it reduces the risk of sensitive data leaving the system. Further, all automated workflows are governed by an audit trail, access control, SSO, and security protection. This unburdens teams so they can concentrate on the actual automation task. Finally, a dedicated EdgeConnect component enables secure integrations for on-premises systems.

4. Answer-driven automation

When automation is based on false positives, it creates a situation of garbage in, garbage out. AutomationEngine is integrated within the Dynatrace platform, including Grail and Davis AI. Trustworthy, accurate answers and insights are a prerequisite for reliable security, business, and IT automation, especially when implementing feedback loop-based automation. This is true for workflow-based automation, as well as modern, event-driven cloud-native automation. With the ability to initiate and orchestrate automated actions throughout the ecosystem of collaboration—including ITSM, DevOps, and security tools–answer-driven automation delivers immediate and significant value to the organization.

In addition to the low-code/no-code AutomationEngine, Dynatrace announced a host of platform enhancements at Perform 2023 in Las Vegas. These enhancements further enable IT, DevOps, and security pros to move from data in context to insights on which they can execute with confidence, including the following:

• expanded Grail data lakehouse capabilities to enable new data types and graph analytics;
• a new user experience and interface and updated dashboards to more easily visualize trends;
• data Notebooks for petabyte-scale data exploration and analytics for real-time insights;
• AppEngine to create custom, compliant data-driven apps for answers and automation.

For more on AutomationEngine, visit our website.

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* ^{Gartner, “Emerging Tech: Venture Capital Growth Insights for Decision Intelligence Platforms,” Aakanksha Bansal, Alys Woodward, Akhil Singh, 10 February 2023.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.}

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Today, IT and development teams have myriad responsibilities in managing and working with cloud environments. They need to create new products and maintain existing ones to deliver customer value at speed and scale while managing risk. Therefore, modern observability platforms are becoming a must-have tool for these teams.

Teams can now use an observability platform to unify operations, business, and development data to gain automatic, precise answers about cloud environments and user experience. Further, developers can build more agility into their processes with an intuitive, low-code approach to create custom, compliant, and intelligent data-driven apps and automation.

The ability to create custom apps becomes increasingly important as organizations strive to innovate and grow. They want to develop secure, compliant custom apps and integrations—whether in the cloud or on premises. Indeed, according to one report, more than 50% of U.S. organizations have over 100 custom applications in development and testing today.

A modern observability platform like Dynatrace is uniquely positioned to serve as a foundation that enables teams to build low-code, custom apps that are secure, compliant, and based on precise answers. And with Dynatrace AppEngine, IT pros have the tools to build custom apps that serve their organization’s business needs and fit in their multicloud ecosystems.

The convergence of observability and security data

Organizations now realize that better understanding their applications and cloud environments is the key to assessing their health from both performance and security perspectives. Therefore, leaders are embracing a converged security and observability approach by unifying all observability and security data in a single location or source of truth.

As a result, these leaders can ask different questions of the same data. And because the data is complete and in context, the answers and insights are always accurate, resulting in an increased trust of these data-backed insights. As development and security teams work more closely together, they also recognize the importance of converging observability and security data to deliver answers and insights through secure, compliant, enterprise-grade applications.

Dynatrace AppEngine supports these goals and empowers customers and partners with an easy way to create and share custom apps for their IT, development, security, and business teams. With consistent answers and insights, organizations can solve their toughest business challenges.

Unifying data, retaining full data context

The Dynatrace platform now uniquely consolidates observability, security, and business data while retaining full context and dependency mapping with its data lakehouse, Grail. Additionally, its modern architecture delivers cost-effective storage and compute. As a result, teams benefit from low-cost cloud storage that provides access to all data and doesn’t require data rehydration. This frees teams from manual approaches to connecting siloed data—using imprecise machine-learning and correlation-only techniques—and the high total costs of using multiple DIY-style solutions.

AppEngine uses this data and simplifies intelligent app creation and integrations for teams throughout an organization. It provides automatic scalability, runtime application security, secure connections and integrations across hybrid and multicloud ecosystems, and full lifecycle support, including security and quality certifications. As a result, for the first time, any team in an organization can create intelligent apps powered by causal AI and build integrations for use cases and technologies specific to their unique business requirements and technology stacks.

Delivering precise answers and insights to the whole enterprise with secure apps

AppEngine is a core technology in the Dynatrace platform that enables anyone in IT, business, or security to create and share inherently secure, custom apps that leverage causal AI and data and scale well. It’s a more productive and secure approach for teams to meet requirements without undermining an organization’s security posture. By using these secure apps, teams are empowered with powerful mechanisms for collaboration and insight-sharing across domains.

AppEngine is built for enterprise software development needs

The custom applications that teams develop run in the Dynatrace environment and automatically meet enterprise requirements, such as scalability, availability, performance, scale, governance, and full lifecycle support.

AppEngine provides a runtime environment that automatically scales the environment, ensuring the application is highly available and performing healthily. Additionally, AppEngine handles security and compliance by leveraging the capabilities of the Dynatrace platform, including access control, secrets management, audit logs, and app signing. Further, the EdgeConnect capability allows secure connections to on-premises systems. This ensures users do not damage the enterprise’s security posture with insecure rogue applications. And, lastly, it provides full lifecycle support with a rich developer experience to create, deploy, and manage applications easily.

Endless possibilities with custom apps powered by observability, security, and business data

Dynatrace customers and partners can now easily bring their business logic to data and create applications on the Dynatrace platform. Dynatrace tames the underlying complexity of managing data in a cost-efficient, performant way, ensuring the integrations with an organization’s ecosystem solutions are inherently secure and everything runs and scales in an automated way. This allows teams to focus on quickly creating apps and sharing with others to promote collaboration and data-backed decision-making.

The following apps showcase the power of AppEngine and the diversity of use cases it can address:

• Smartscape Health View. Teams can visualize an application’s vital signs, including its security posture. This app also showcases AppEngine’s ability to unlock actionable insights from data through enrichment, visualization, and analytics.
• Site Reliability Guardian. This enables teams to proactively maintain service-level objectives by automating quality and security gates. This also exemplifies how apps created using AppEngine fuel answer-driven automation to optimize cloud operations.
• Carbon Impact. Organizations can understand and reduce the carbon footprint of hybrid and multicloud ecosystems. This also demonstrates how AppEngine can help teams measure and optimize the key performance indicators that matter most for business executives or regulatory requirements.

This newfound agility in packaging and delivering data-driven answers and insights in a secure, compliant way is highly beneficial for customers seeking to use data for automating insights and actions.

Taking your data insights to the next level

At Perform 2023 in Las Vegas, Dynatrace announced a host of platform enhancements to enable IT pros to move from data in context to insights on which they can execute with confidence. The platform has been updated with a variety of capabilities to enable teams to discover data insights more easily:

• expanded Grail data lakehouse to support new data types, such as metrics and distributed traces, and enable exploratory analytics with causation powered by topology and dependency mapping;
• a new user experience for collaborative and exploratory data analytics using Notebooks;
• Dynatrace AutomationEngine for automating BizDevSecOps workflows driven by answers from causal AI; and
• Dynatrace AppEngine with a low-code tool set and application programming interfaces for building and sharing custom, compliant, data-driven apps.

To learn more about Dynatrace AppEngine and how to create low-code applications for exploratory analytics, check out our on-demand Perform sessions.

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Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. While the benefits of AIOps are plentiful — including increased automation, improved event prioritization and incident response, and accelerated digital transformation — applying AIOps use cases to an organization’s real-world operations issues can be challenging.

So, before your organization can get the most out of an AIOps platform, it’s critical to understand its potential use cases. By understanding the advantages of deterministic AI, you can choose an AIOps platform that helps you transform faster and achieve autonomous operations.

AIOps use cases

The goal of AIOps is to automate operations across the enterprise. However, as organizations adopt more cloud-native technologies, such as containerized microservices and serverless platforms, operations have become exponentially more complex. This makes developing, operating, and securing modern applications and the environments they run on practically impossible without AI. Thus, modern AIOps solutions encompass observability, AI, and analytics to help teams automate use cases related to cloud operations (CloudOps), software development and operations (DevOps), and securing applications (SecOps).

CloudOps: Applying AIOps to multicloud operations

CloudOps includes processes such as incident management and event management. AIOps reduces the time needed to resolve an incident by automating key steps in the incident response process. This includes identifying an issue’s root cause and automatically responding to those causes.

Logs are a valuable source of information, but that information is often difficult to identify. AIOps can identify events that require some response, but likely would not be detected and acted upon manually.

DevOps: Applying AIOps to development environments

DevOps can benefit from AIOps with support for more capable build-and-deploy pipelines. Teams can address testing and deployment issues automatically, which streamlines continuous integration and continuous delivery pipelines and increases innovation throughput. This increased automation, resilience, and efficiency helps DevOps teams speed up software delivery and accelerate the feedback loop — ultimately allowing them to innovate faster and more confidently.

SecOps: Applying AIOps to secure applications in real time

Organizations are constantly improving, revising, and updating applications with new features. But before that new code can be deployed, it needs to be tested and reviewed from a security perspective. SecOps is responsible for ensuring applications are secure. AIOps supports that with the ability to assess applications during development, delivery, and deployment.

Anomalous behavior in a newly deployed application can easily escape human detection, but AIOps systems complement SecOps engineers by identifying and reporting on potentially exploitable vulnerabilities.

The four stages of data processing

For a deeper understanding of how to effectively apply AIOps use cases, it helps to understand how it processes data to improve your operations. There are four stages of data processing:

1. Collect raw data
2. Aggregate it for alerts
3. Analyze the data
4. Execute an action plan

Teams often follow this approach to achieving AIOps because of its apparent convenience:

1. Start with a second-generation application performance monitoring solution, which covers data collection and aggregation and prepares data for analysis.
2. Introduce a machine-learning-based AIOps platform as a logical evolution in IT management tooling. This second solution picks up at data collection, aggregation, and analysis, preparing it for execution.

This approach introduces another layer that helps to manage a lot of events from different solutions and vendors, with machine learning assistance to reduce the alert flood and focus on the critical issues.

However, the price for that convenience is the potential loss of context when switching tools, which the machine learning needs to achieve automated root-cause analysis and, eventually, fully automated CloudOps.

Think of traditional AIOps solutions as an aid to the status quo. It can help you catch up, better manage incidents, and be more reactive. But this approach breaks down at the scale and complexity of today’s modern multicloud environments.

Ultimately, AIOps should encompass all four stages of data processing in a single product and UI, including the execution phase, by enabling greater automation throughout your IT organization. This includes CloudOps, with a focus on incident management, DevOps for improved application building and testing, and SecOps to ensure applications are secured. Only an approach that encompasses the entire data processing chain using deterministic AI and continuous automation can keep pace with the volume, velocity, and complexity of distributed microservices architectures.

Achieving autonomous operations

The great promise of AIOps is to automate IT operations — or achieve autonomous operations. However, organizations can only achieve autonomous operations when the flow through the four stages can happen without human intervention. While the collection, aggregation, and execution stages of the data processing chain have been solved to some extent, the toughest part is the analysis stage. This involves identifying the root cause of an issue and then, based on the insight, choosing the best remediation action.

Cracking the analysis stage requires a different approach to AI.

Deterministic AI

An alternative to the machine learning approach is deterministic AI, also known as fault-tree analysis.

Let’s say, for example, an application is experiencing a slowdown in receiving its search requests. The deviating metric is response time. It triggers the fault-tree analysis, so you begin analyzing with the monitored entity to which the metric belongs — the application. This is now the starting node in the tree.

Next, you investigate all the app’s dependencies. It may have third-party calls, such as content delivery networks, or more complex requests to a back end or microservice-based application. The AI then analyzes and investigates all those dependent nodes for anomalies. If a node is cleared, it forms a leaf. Nodes showing anomalies will be further investigated down their dependencies.

From there, you look at the web server the application is communicating with, further to the front-end tier and search service. Then, you see search requests are slower than usual on all nodes.

Now, it’s not that simple to just follow the dependencies in one direction. Let’s assume the operating system hosting the search service is also running another process independently that consumes significant CPU. This causes a shortage and slows down the search service. From the search service, you follow the dependency to the process and to the host, and then back up to other processes running on that host.

This process continues until the system identifies a root cause. In this case, it’s a chatty neighbor. On the other end of the tree, you can assess the impact. For example, how many users have been affected by that problem? A huge advantage of this approach is speed. It works without having to identify training data, then training and honing.

The significance of topology information for AIOps use cases

Because it follows a logical fault tree, deterministic AI requires a topology model of your data center or application deployment. Otherwise, you would never be able to walk through the tree like this and find the root cause.

A traditional AIOps platform ingests data and metadata to offer correlational data and dashboards to conduct root-cause analysis. On the other hand, a deterministic AI approach based on fault-tree analysis uses topology data and builds an entity model in real time by incorporating observed raw data — including metrics, logs, events, traces, and contextual information, such as user experience data. This entity modeling with contextual data enables deterministic AI to deliver precise and repeatable root-cause identification.

Two types of root cause

There are two different types of root cause: technical and foundational. The earlier example explains how the system identifies a technical root cause. In this case, it’s a CPU spike of another process. The foundational root cause explains what led to that spike — in this case, a deployment.

To achieve automated foundational root-cause analysis, the AI needs to be able to browse through the history or changelog of the monitored entity that has been identified as the technical root cause. Of course, this information must be available to the AI and, therefore, part of the entity.

How AI helps human operators

Like all AI applications, whether in manufacturing, healthcare, finance, or other industries, AIOps is not about reducing the human factor’s importance. Rather, it’s about helping people work faster and smarter. Modern IT operations involve observing networks, cloud resources and applications, endpoint devices, and more. The amount of data generated by the tools used to collect information on the status and behavior of these systems is simply too much to properly manage, even for teams of IT professionals.

AIOps bolsters the abilities of DevOps workers, security professionals, and administrators by providing them with the data filtering and parsing needed to comprehensively observe their systems. Machine-learning-supported tools provide human operators with only the information relevant to the task at hand, leaving out the noise. Alert fatigue and chasing false positives are not only efficiency problems. They’re also discouraging the people who would rather focus on the important work they are trained to do.

Taking AIOps to the next level

With modern multicloud environments, AIOps must evolve to include the full software delivery lifecycle.

The traditional, machine-learning-based approaches — which still rely heavily on human input — give us event monitoring tools that cannot scale up to meet the demands of modern multicloud microservice-based apps.

However, a deterministic, fault-tree approach to AI allows for precise technical and foundational root-cause identification and impact analysis in real time. The result is more complete automation throughout the entire development and delivery pipeline. Therefore, DevOps staff can innovate and create new solutions to human problems, rather than simply keeping the lights on.

The post Applying real-world AIOps use cases to your operations appeared first on Dynatrace news.

As organizations embrace automation instead of time-consuming, manual processes, many turn to artificial intelligence for IT operations, or AIOps.

AIOps uses machine learning and artificial intelligence, or AI, to cut through the noise in IT operations — specifically incident management. But what is AIOps, exactly? Are all AI and AIOps approaches the same? And how can it support your organization?

What is AIOps?

According to Gartner, “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” A modern approach to AIOps serves the full software delivery lifecycle. It addresses the volume, velocity, and variety of data in complex multicloud environments with advanced AI techniques to provide precise answers and intelligent automation.

Most AIOps tools ingest pre-aggregated data from various technologies across the IT management landscape — including disparate observability tools — and conclude what is relevant for an analyst to focus on. But there are a few caveats to consider. We’ll discuss the current AIOps landscape and an alternative approach that truly integrates AI into the DevOps process.

How does AIOps work?

AIOps is distinct from other IT data collection solutions that process and make inferences from data. While most large organizations already have comprehensive data collection tools, they don’t provide the whole picture. Modern collection and monitoring tools often generate too much data for a human to parse and use, which is where AIOps can help.

AIOps uses AI methods to ingest, sort, and make inferences from data. A full AIOps pipeline often includes several algorithmic processes with different jobs:

• One handles data ingestion and sorting.
• One recognizes patterns.
• One makes inferences from the patterns.

When combined, they significantly reduce alert fatigue and the data-sorting burden.

Equally important is AIOps’ ability to communicate information directly to the right teams. Additionally, AIOps often accompanies an increased focus on incident response automation. AI for IT operations aims to increase efficiency and observability throughout an organization. The building blocks of AIOps — machine learning algorithms and other AI processes — all help to accomplish that goal.

Two approaches to AIOps

There are two overarching AIOps approaches: traditional correlation-based AIOps and modern deterministic AIOps. This modern approach is also referred to as causal AI and uses fault-tree analysis.

Traditional AIOps

Traditional AIOps approaches are designed to reduce alerts and use machine learning models to deliver correlation-focused dashboards. These systems are often difficult to scale because the underlying machine-learning engine doesn’t provide continuous, real-time insight into an issue’s precise root cause. They require extensive training, and analysts must spend valuable time manually tuning the model and filtering out false positives.

Modern AIOps using deterministic, causal AI

A modern AIOps solution, on the other hand, is built for dynamic clouds and software delivery lifecycle automation. It combines full stack observability with a deterministic, or causal, AI engine that can yield precise, continuous, and actionable insights in real-time. This contrasts stochastic (or randomly determined) AIOps approaches that use probability models to infer the state of systems. Only deterministic, causal AIOps technology enables fully automated cloud operations across the entire enterprise development lifecycle.

Is AIOps necessary?

Modern applications are built from hundreds or thousands of interdependent microservices distributed across multiple clouds, creating incredibly complex software environments. This complexity makes it difficult for IT pros to understand the state of these systems, especially when something goes wrong. While AIOps is often presented as a means to reduce the noise of countless alerts, it can do much more. A full-featured, deterministic AIOps solution fosters faster, higher-quality innovation; increased IT staff efficiency; and vastly improved business outcomes.

Humans can’t manually review and analyze the massive amount of data that a modern observability solution processes automatically. Typically, any approach that adds more visualizations, dashboards, and slice-and-dice query tools is more of an unwieldy bandage than a solution to the problem. Disparate interfaces still require manual intervention and analysis. In this way, traditional AIOps solutions have essentially become event monitoring tools.

Modern IT strives for more capable automation, and AI is critical to achieving this goal. Continuous integration and continuous delivery processes provide smart pipelines for rolling out new features and services. Orchestration platforms, such as Kubernetes, are relieving operations teams from error-prone and mundane tasks related to keeping services up and running. This automation enables developers and operations teams to focus on innovation, rather than endless administrative tasks.

The challenges of traditional AIOps

Despite the AIOps benefits, such as improved time management and event prioritization, increased business innovation, enhanced automation, and accelerated digital transformation, correlation-based AIOps solutions have limitations.

AIOps based on correlation does not scale

With a machine learning approach, traditional AIOps solutions must collect a substantial amount of data before they can create a data set — i.e., training data — from which the algorithm can learn. Administrators can reinforce learning through rating and similar means, but it can take weeks or even months until this AI is calibrated to deliver insights into business-critical applications in production.

This approach is hardly “set and forget.” Modern applications undergo frequent changes, and their deployments are highly volatile, which implies an ever-changing data set. Traditional AIOps can’t scale up with frequent changes that occur within complex distributed applications.

Lost and rebuilt context

The second challenge with traditional AIOps centers on the data processing cycle. Traditional AIOps solutions are built for vendor-agnostic data ingestion. This means data sources typically come from disparate infrastructure monitoring tools and older-generation application performance monitoring solutions.

These tool sets first acquire one or more raw data types — such as metrics, logs, traces, events, and code-level details — at different levels of granularity. Then, they process them before finally creating alerts based on a predetermined rule — for example, a threshold, learned baseline, or certain log pattern.

Typically, machine learning can access only the aggregated events, which often exclude additional details. Now, the AI learns similar reoccurring clusters of incoming events for later classification of new events. With that data, it builds and rebuilds context — time- and metadata-based correlation — but has no evidence of actual dependencies. Integrations allow for the system to process more data, such as metrics. But those add more data sets without solving the cause-and-effect problem with certainty.

What are the key capabilities of a modern AIOps solution?

An AIOps solution should be comprehensive to save teams time and manual effort. Here are key capabilities an AIOps solution should provide.

Unified platform. A comprehensive, modern approach to AIOps is a unified platform that encompasses observability, AI, and analytics. This all-in-one approach addresses the complexity of identifying problems in systems, analyzing their context and broader business impact, and automating a response. The best solutions provide real-time, continuous insights into the state of systems and services that are critical to business operations. That way, businesses can focus on innovation rather than responding to inevitable problems with complex systems.

Topology mapping and distributed tracing. A truly modern AIOps solution should include topology-mapping capabilities, perform distributed tracing, and have strong integration capabilities. With strong topology mapping, users immediately gain a comprehensive visualization of all infrastructure, process, and service dependencies. A similarly important visibility requirement is distributed tracing, which should provide DevOps with fine-grained topology and telemetry data and metadata.

Full observability of Kubernetes environments. Kubernetes has abstracted resource management to such a high degree that the platform can be adopted across industries for a wide range of applications. But that adaptability brings complexity. AIOps is an increasingly essential part of DevOps in Kubernetes environments where reliability, scalability, and flexibility are key considerations.

Comprehensive integrations. Finally, integration is critical for the success of any modern IT solution. In addition to supporting fine-grained observability, AIOps solutions should support integration with existing security systems. Most often, the problem with existing security systems is not that they fail to work properly. Rather, it’s that they cannot be used properly due to alert fatigue and false-positive frequency.

Deterministic AI is key to AIOps success

Traditional AIOps is limited in the types of inferences it can make because it depends on metrics, logs, and trace data without a model of how systems’ components are structured. AIOps should instead use deterministic AI to fully map the topology of complex, distributed architectures to reach resolutions significantly faster.

By applying real-world AIOps use cases, businesses can harness the power of advanced analytics, machine learning, and automation to enhance monitoring, detect anomalies, and optimize performance. This transformative approach enables proactive problem resolution, improves efficiency, and empowers IT teams to deliver exceptional user experiences. Learn how AIOps can revolutionize your business by driving efficiency, reliability, and proactive decision-making.

In part two, “Applying real-world AIOps use cases to your operation,” discover how to achieve autonomous operations, and explore AIOps use cases, like applying AIOps to multicloud operations, development environments, and secure applications in real-time.

The post What is AIOps? An insider’s guide to AI for ITOps — and beyond appeared first on Dynatrace news.

Software and data are a company’s competitive advantage. That’s because every company is now a software company. As a result, organizations need software to work perfectly to create customer experiences, deliver innovation, and generate operational efficiency. But for software to work perfectly, organizations need to use data to optimize every phase of the software lifecycle. That’s exactly what our platform does.

Much of the software developed today is cloud native. However, cloud infrastructure has become increasingly complex. This requires hundreds of interdependent services to work perfectly. Organizations must update services and apps dozens of times a day. Further, the delivery infrastructure that makes this happen has also become complex. The only way to address these challenges is through observability data — logs, metrics, and traces. But it doesn’t stop there.

Teams interact with myriad data types. For example, users generate user data, ecommerce sites generate business data, and service portals generate service desk tickets and call volume data. But how is this data connected? That’s where context comes into play.

Organizations need to unify all this observability, business, and security data based on context and generate real-time insights to inform actions taken by automation systems, as well as business, development, operations, and security teams.

Traditionally, though, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs. IT pros want a data and analytics solution that doesn’t require tradeoffs between speed, scale, and cost.

With a data and analytics approach that focuses on performance without sacrificing cost, IT pros can gain access to answers that indicate precisely which service just went down and the root cause.

The next frontier: Data and analytics-centric software intelligence

Modern software intelligence needs a new approach. It should be open by design to accelerate innovation, enable powerful integration with other tools, and purposefully unify data and analytics. Enter Grail-powered data and analytics.

Grail is a purpose-built data lakehouse for observability, security, and AIOps. Grail makes it possible to converge real-time analytics, historical analytics, and predictive analytics on a single platform.

This purpose-built data lakehouse approach for observability, security, and AIOps offers schema-less ingestion of various data types, including logs, metrics, traces, user data, business data, and topology data. Additionally, it provides index-free storage and direct analytics access to source data without requiring data rehydration.

Ultimately, this helps address data scale and access performance constraints that prevent organizations from unlocking data’s full potential.

Here are six steps to creating a modern data stack and AI strategy for observability, AIOps, and application security.

1. Identify the business outcomes

It’s important to understand what business outcomes you want to achieve. Your key business objectives will drive your strategy and metrics. An example is improving customer experience. Customers today expect a very high level of experience when they engage with an organization.

Consider the data needed and its source. Collecting logs, metrics, events, and trace data is great. But for full-stack observability, you also need to bring together the topology data model, code-level details, and user experience data. If you’re using an approach that employs disparate tools to monitor individual components of the stack separately, then you’re leaving value on the table by failing to take a platform approach. Individual tools continue to promote and generate data silos and prevent organizations from using data effectively.

Modern observability platforms make it possible to centralize observability data from even the most complex stacks and get answers that help you achieve your desired business outcomes.

2. Ingest all the data you need from anywhere

A combination of proprietary and open source technology can speed data ingestion from common and long-tail sources. You need data from myriad sources centralized to get the right context to power precise answers. This is where a data lakehouse with software intelligence comes into play. A purpose-built data lakehouse can ingest a variety of data sources without requiring tradeoffs between data storage and performance.

Don’t reinvent the wheel. Between Dynatrace OneAgent and open source observability frameworks such as OpenTelemetry, you are well covered. These two technologies enable you to ingest all types of observability data — logs, metrics, traces, user experience, and business data.

Logs. Systems automatically generate logs, which record events that took place. Log entries usually contain the following:

• Date and time of event;
• System or resource name;
• App name; and
• Event severity.

Logs come in different formats depending on the source system — including key-value pairs, JSON, CSV, and more.

Metrics. This data is aggregated over a period of time. For example, this includes CPU utilization, memory percentage in use, and average load times.

Traces. A trace is the path a transaction took in an application for completion — for example, querying a database or executing a customer transaction. A trace is usually shown from the beginning of the transaction to the end.

User experience data. Web and mobile apps record data about every user interaction. This data includes information about crashes, lags, rage clicks, user interface hangs, and time spent in apps.

Business data. This data is anything generated from business operations. For example, this includes conversion rate, average order value, cart abandonment rate, service desk ticket, and call volume data.

3. Unify and centralize observability, security, and business data

Centralizing all organizational data is unrealistic. However, observability, security, and business data are different. Organizations need to instantly process, enrich, contextualize, and analyze all the data that supports mission-critical operations. All the infrastructure metrics, application performance data, and user experience data contain records of not only performance degradation events, but also security threats, fraudulent activities, and customer behavior.

Advances in data storage technology and architectures make it possible to store huge volumes and a variety of observability data in an efficient way that scales as requirements evolve. Centralizing observability data makes it easy to curate high-quality data. As a result, organizations accelerate the process of identifying relationships between entities, connecting the dots between disparate data sets, and gaining ROI from aggregated data with actionable insights.

If data sets are in siloed tools and systems, it slows down the process of delivering precise answers. In contrast, if you preserve and manage all the relationships between all data, it makes it possible to do more deterministic, causational AI on this data, resulting in precise answers.

4. Use AI to generate answers and insights from data

Once you unify and centralize the relevant data, you can apply real-time data processing to identify the precise root cause of issues and generate actionable insights. Successfully doing so at scale requires AI to identify the relationships and context between data types.

Having a real-time topology map that tracks all entities is useful. It helps derive context between different data slices. Doing so manually is beyond human capability because of data volumes, ingestion speeds, formats, and the dynamic and complex nature of the application environment and infrastructure. This is where AI excels in data and analytics-powered software intelligence. Continuously processing data from every layer of the stack opens the door to numerous possibilities.

Real-time anomaly detection. Real-time monitoring detects issues within infrastructure and applications before they become costly, customer-facing problems. Anomaly detection helps identify issues that deviate from the norm so teams can proactively resolve them. Getting relevant information to the right teams at the right moment is critical. In part, that involves reducing alerts so IT teams aren’t overwhelmed and can identify high-priority issues. Providing context and a prioritized list of issues helps them focus on the most important tasks.

Automated root-cause analysis (RCA). Automated RCA breaks down an issue into its components and identifies the precise root cause. But without full-stack observability, RCA is impossible. This is because, in many cases, an application issue is tied to a microservice on the back end. Having a real-time topology with deterministic AI helps immediately find the root cause. It saves engineers a lot of time by showing exactly what went wrong and how it happened.

Runtime application security. With continuous software intelligence from full-stack observability data, DevSecOps teams are notified if vulnerable code is called in production applications. By enriching data with vulnerability databases, operations engineers can create a risk-weighted priority list of security issues. Additionally, gaining a complete understanding of vulnerability severity and frequency becomes useful for developers.

5. Use exploratory analytics for lightning-fast answers

Sometimes, teams need to drill deeper into an answer, or a question pops into your mind. Exploratory analytics on a data lakehouse architecture with software intelligence makes it possible to write any query and get an instant answer, thanks to distributed query execution.

6. Automate actions and optimizations powered by AIOps

Automated root-cause analysis eliminates guesswork and human effort. Therefore, IT pros know exactly which code in a software release was problematic, or which server had an issue. Rolling back code deployment or restarting a server makes sense, and you can build rollback into an automated remediation workflow. A shift-left approach helps in designing the remediation mechanism during the early stages of software development. Notifying the right teams of remediation activity closes the loops and removes the burden of manual action.

Data and AI are key to making software work perfectly

Assembling, cleaning, combining, and enriching observability data from various systems is key to getting correct answers. Contrary to popular belief, AI systems easily fall prey to “garbage in, garbage out” principles — that is, the systems are only as good as the quality of the data. Controlling the quality of data is key to getting the right data and the right answers to achieve business goals. A data and analytics platform that includes a data lakehouse design and software intelligence facilitates unifying not only data, but also various analytics workloads for what ultimately matters — time to response and action.

The Dynatrace difference, powered by Grail

Dynatrace offers a unified platform that supports your mission to accelerate cloud transformation, eliminate inefficient silos, and streamline processes. By managing observability data in Grail — the Dynatrace data lakehouse with massively parallel processing — all your data is automatically stored with causational context, with no rehydration, indexes, or schemas to maintain.

With Grail, Dynatrace provides unparalleled precision in its ability to cut through the noise and empower you to focus on what is most critical. Thanks to the platform’s automation and AI, Dynatrace helps organizations tame cloud complexity, create operational efficiencies, and deliver better business outcomes.

The post How unified data and analytics offers a new approach to software intelligence appeared first on Dynatrace news.

The sense-think-act model of automation has come to define the capabilities of industrial robots and self-driving cars. But as IT teams increasingly design and manage cloud-native technologies, the tasks IT pros need to accomplish are equally variable and complex. AIOps and observability—or artificial intelligence as applied to IT operations tasks, such as cloud monitoring—work together to automatically identify and respond to issues with cloud-native applications and infrastructure.

In multicloud environments, IT teams often struggle to take timely action given a deluge of data and alerts for issues ranging from system performance, security risks, and application problems. As a result, teams often can’t identify or prioritize cyber threats or performance issues before applications lag or succumb to cyberattacks. Those application issues can translate into severe financial losses and business harm because of compromised data or abandoned customer loyalty.

Indeed, according to Dynatrace data, 61% of IT leaders say observability blind spots in multicloud environments are a greater risk to digital transformation as teams lack an easy way to monitor their infrastructure end to end. But applying the sense-think-act model for IT using AIOps and observability can make the difference.

The sense-think-act model for AIOps and observability

The sense-think-act model takes shape in the real world with self-driving cars and robots of all kinds. By sensing, thinking, and acting, these technologies can complete tasks automatically. The framework forms the basis of the SAE (Society of Automotive Engineers) automation levels 1 through 5 for cars.

This same sense-think-act model is also a useful framework for evolving IT operations practices. Accordingly, a software intelligence platform with sense-think-act capabilities enables self-driving IT operations and DevSecOps in the enterprise.

‘Sense’ with observability

Observability technology is how modern IT teams “sense” what is going on with user experience, application performance, and infrastructure health. Teams sense by collecting—and connecting—the massive data volumes these systems generate in the form of metrics, events, logs, traces, and user experience data.

Modern observability solutions such as Dynatrace observe an application’s complete transactional behavior, from user experience and application performance to infrastructure health, and everything in between. This includes automatically discovering all cloud services, mapping all application and infrastructure dependencies, and continuously learning from them. This is similar to a self-driving car that constantly updates its knowledge of the environment based on the incoming information from cameras, radar, and other sensors.

‘Think’ with artificial intelligence

Cumbersome legacy IT architecture is giving way to modern multicloud architectures where technologies, data, and processes converge to enable innovation. However, the resulting multicloud functionality and flexibility also bring complexity and scale that surpass human capacity to analyze and visualize.

This is where artificial intelligence (AI) comes in. AI in modern software intelligence solutions helps teams analyze volumes of observability data to distill precise answers and actionable insights. This automatic system analysis provides continuous intelligence to IT operations, DevOps, and site reliability engineering (SRE) teams.

‘Act’ with AIOps

The goal of any sense-think-act model is the action. Modern AIOps, or artificial intelligence for IT operations, combines full-stack monitoring with an AI engine that yields precise and continuous data intelligence. The system then turns these insights into automatable actions in real time.

Because AIOps generates precise answers from massive data using AI, it can trigger automation that improves system health and performance. For every task, teams need to ask, “Is this job better executed by a human or automatically using AIOps?” According to Gartner, by 2024, 30% of business leaders will rely on AIOps platforms. The automated insights they generate drive business-related decisions and trigger automatic actions.

An automatic and intelligent AIOps solution accurately pinpoints the root causes of anomalies and provides answers for how to fix them. By creating a thorough mapping of how a problem evolves in real time, such an AIOps solution provides automatic root-cause analysis, eliminating the need for manual investigation.

Self-driving operations at enterprise scale

Modern enterprises have ramped up large-scale projects surrounding application modernization, digital transformation, and cloud migration. Not surprisingly, the projects’ supporting architectures are already complex and become totally unmanageable if teams work with disconnected, manual solutions. There are just too many dynamic scenarios that are constantly changing.

So, what should IT operations and DevOps teams do? Like a self-driving car, modern teams need self-driving operations delivered through a software intelligence platform. A platform that provides a reliable sense-think-act experience can solve myriad use cases. As a result, applying AIOps to observability data can generate precise answers and actionable intelligence. Once constructed, teams can proactively orchestrate problem remediation using full automation or with a “human in the loop”. And all this needs to happen in a way that scales limitlessly to meet the needs of the modern enterprise.

How Dynatrace delivers a sense-think-act model using AIOps and observability

Sense-think-act is best known for self-driving cars and robots. But for organizations adopting cloud-native technologies, the model provides a blueprint for an automated response to IT complexity. AIOps and observability provide the mechanism for detecting, analyzing, and automatically responding to IT anomalies that threaten performance and uptime.

Using deterministic, fault-tree AI, Dynatrace provides end-to-end observability of multicloud environments, delivering precise answers for automated responses. With broad technology support, Dynatrace enables teams to unify even the most complex and dynamic multicloud workloads with out-of-the-box capabilities.

Dynatrace AI is always on, processing billions of dependencies to detect anomalies, deliver precise answers, and automate operations that enable self-healing.

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As organizations look to speed their digital transformation efforts, automating time-consuming, manual tasks is critical for IT teams. Therefore, many organizations are evaluating the benefits of AIOps.

Artificial intelligence for IT operations (AIOps) uses machine learning and AI to help teams manage the increasing size and complexity of IT environments through automation. However, its benefits go beyond increased automation and reduced complexity. There are seven AIOps benefits, in particular, that can transform your business operations.

But first, let’s start with a better understanding of what AIOps is, how it works, and its value to organizations.

What is AIOps, and how does it work?

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. While these functions set the stage for AIOps adoption, they are not enough in isolation. A truly modern AIOps solution also serves the entire software development lifecycle to address the volume, velocity, and complexity of multicloud environments.

AIOps aims to provide actionable insight for IT teams that helps inform DevOps, CloudOps, SecOps, and other operational efforts. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing:

1. Collection
2. Aggregation
3. Analysis
4. Execution

Many companies combine second-generation application performance management (APM) solutions to collect and aggregate data with machine-learning-based AIOps tools that add analysis and execution. But this approach introduces complexity and a potential loss of context. A modern, holistic AIOps platform, meanwhile, encompasses all four stages to deliver an end-to-end operational approach.

Seven benefits of AIOps for operational transformation

Along with reduced complexity, IT teams can transform their operations with seven key benefits of AIOps, including the following:

1. Improved time management and event prioritization

Given the sheer number of software services that organizations develop and use to improve operational processes and meet customer needs, it’s easy for teams to get bogged down in the details. These teams need to know what must be addressed right now and what can wait.

An AIOps platform that uses advanced fault-tree analysis can help companies automatically identify the alerts that matter most and prioritize their responses to reduce mean time to resolution (MTTR).

2. Reduced IT spend

Another AIOps benefit is it can also reduce operational costs. By proactively identifying potential issues, AIOps tools can alert analysts before they lead to a costly outage. A modern AIOps solution can also save teams considerable time and effort by eliminating false positives.

In fact, according to a Forrester Consulting report, implementing an AIOps approach that provides proactive visibility helped companies improve operational efficiency and reduce false-positive alerts by 95%. Additionally, research from Dynatrace found that implementing AIOps could save companies an average of $4.8 million per year by automating key processes.

3. Increased business innovation

The faster companies can respond to changing market conditions, the better. AIOps reduces the time and effort required to keep the lights on by automating key processes. This allows IT teams to focus on what matters — implementing strategic initiatives that drive business success.

For example, consider the adoption of a multicloud framework that enables companies to use best-fit clouds for important operational tasks. If IT teams spend the bulk of their time responding to alerts and dealing with false positives, there’s little time for innovation. By implementing AIOps, teams can free up developers to tackle new projects.

4. Expanded collaboration

AIOps solutions are naturally data- and department-agnostic, which helps organizations expand their collaboration efforts. As such, they ingest and analyze data from multiple sources to produce holistic outputs that aren’t tied to specific use cases or business teams. As a result, AIOps makes it possible for disparate departments to speak the same language and improve collaboration.

5. Streamlined product improvements

Once products and services are live, IT teams must continuously monitor and manage them. These teams need to know how services and software are performing, whether new features or functions are required, and if applications are secure.

Like the development and design phases, these applications generate massive data volumes that offer relevant and actionable insights. Such insights include whether the system can effectively collect, analyze, and report this data. Here, AIOps tools can pinpoint potential areas where teams can improve applications. An AIOps platform can also identify the most cost- and time-effective approaches to make these changes at scale.

6. Enhanced automation

Manual processes are time-consuming and error-prone. However, AIOps makes it possible to automate key tasks, such as error detection, alert analysis, and event reporting. This allows IT operations teams to shift their focus and prioritize outcomes, rather than sorting through initial observations to find relevant reports.

7. Accelerated digital transformation

According to the Dynatrace 2022 Global CIO Report, organizations are under more pressure than ever to keep pace with digital transformation. And efforts are well underway: The report states 99% of large organizations have now adopted a multicloud environment to improve business outcomes. However, 58% of IT leaders say infrastructure management drains resources as cloud use increases. And 56% say traditional monitoring solutions are no longer fit for purpose.

The result is a digital roadblock. For many organizations, adopting new technologies can add to management and monitoring challenges, which can slow the pace of transformation. Another benefit of AIOps tools is their ability to consume and analyze the ever-increasing amount of data. A comprehensive AIOps solution can help companies confidently adopt new digital technologies.

What are the business benefits of AIOps?

Of all the AIOps benefits, however, the ultimate advantage is its business value.

By automatically collecting, analyzing, and executing responses to issues, AIOps enables organizations to reduce overall complexity. But AIOps also improves metrics that matter to the bottom line. For example:

• Greater IT staff efficiency. With greater visibility into systems’ states and a single source of analytical truth, teams can collaborate more efficiently. More reliable, context-based analysis enables teams to make better decisions, take more decisive action, and automate more functions. With better data intelligence across the software development lifecycle, individuals in every role can exercise more autonomy and experience greater job satisfaction.
• The ability to preempt outages. By automating incident detection and response, teams can significantly reduce MTTR, which improves uptime. But a truly modern approach to AIOps can detect and fix issues before they become outages. Such an approach can conduct automatic business impact analysis to prioritize the issues that matter most to the business. This enables teams to work smarter, not harder.
• Improved user experiences. The bottom line for any business’s bottom line is a better user experience. Greater system reliability and uptime improve user experiences. But a modern approach to AIOps also provides real-time insight into digital experiences in context to what users are trying to accomplish. An AIOps platform that can visually replay user sessions to see specific struggles enables teams to better optimize user journeys.
• Maximum ROI on all hybrid cloud technologies. As cloud-native technologies evolve, organizations layer in more tools and open source solutions to solve specific problems and provide specific benefits. A platform approach to AIOps that provides observability and automatic analysis of all these technologies enables teams to optimize outcomes. With greater observability and contextual analysis, teams can maximize their return on investment (ROI) on all their hybrid cloud technologies.

Create a cloud observability strategy with automatic and intelligent AIOps

To realize the full benefits of AIOps, teams need to do more than simply adopt tools that use statistical, correlation-based machine learning. In practice, businesses are best served by adopting a deterministic, fault-tree AIOps platform that provides end-to-end visibility, observability, and accountability.

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