In our Dynatrace Perform 2024 guide, we explore some of the key cloud observability trends that organizations should consider, including composite AI, AI observability, platform engineering, and more.
Companies now recognize that technologies such as AI and cloud services have become mandatory to compete successfully.
AI data analysis can help development teams release software faster and at higher quality. AI-enabled chatbots can help service teams triage customer issues more efficiently. And security teams can use AI to proactively address potential threats to their IT environments.
According to the recent Dynatrace report, “The state of AI 2024,” 83% of technology leaders said AI has become mandatory to keep up with the dynamic nature of cloud environments. And according to an IDC report, organizations can now realize a return on AI investments within 14 months.
At the same time, the Dynatrace report revealed that 98% of technology leaders are concerned that some AI could be susceptible to unintentional bias, error, and misinformation.
So how can organizations ensure data quality, reliability, and freshness for AI-driven answers and insights? And how can they take advantage of AI without incurring skyrocketing costs to store, manage, and query data?
These are the goals of AI observability and data observability, a key theme at Dynatrace Perform 2024, the observability provider’s annual conference, which took place in Las Vegas from January 29 to February 1, 2024.
AI observability and data observability
The importance of effective AI data analysis to organizational success places a burden on leaders to better ensure that the data on which algorithms are based is accurate, timely, and unbiased. Increasingly, this focus on data quality will push organizations to adopt data observability technologies, which help organizations to determine the quality of their data.
But organizations also need to balance increasing AI adoption with the risks of runaway costs associated with increasing adoption.
Enter AI observability, which uses AI to understand the performance and cost-effectiveness details of various systems in an IT environment. As organizations adopt more AI technologies, the associated costs are skyrocketing. A key theme at Dynatrace Perform 2024 is the need for AI observability and AI data analysis to minimize the potential for skyrocketing AI costs.
‘Composite’ AI, platform engineering, AI data analysis through custom apps
This focus on data reliability and data quality also highlights the need for organizations to bring a “composite AI” approach to IT operations, security, and DevOps. A composite approach combines predictive, causal, and generative AI to ensure better data reliability. Dynatrace hypermodal AI is a specialization of composite AI for observability, security, and business analytics and automation. As a key component of hypermodal AI, causal AI is critical to feed quality data inputs to the algorithms that underpin generative AI.
This composite approach is also driving organizations to seek a unified observability platform that provides contextualized, centralized data in real time.
Another key theme at Dynatrace Perform 2024 is organizations’ growing adoption of platform engineering, which helps accelerate the delivery of software applications. Platform engineering improves developer productivity by providing self-service capabilities with automated infrastructure operations. How organizations use methodologies such as platform engineering may determine organizations’ success or failure in the year to come.
Speakers at Dynatrace Perform 2024 will also explore how organizations can garner business value from their data by building custom applications that serve core organizational needs. Once organizations can unify their data in a trusted cloud observability platform, they can act on—and trust—the insights they gather. In turn, organizations have the tools to build secure, compliant custom apps that serve their business needs and fit easily into their larger multicloud ecosystems.
In what follows, we explore these key cloud observability trends in 2024. Join us at Dynatrace Perform 2024, either on-site or virtually, to explore these themes further.
Dynatrace Perform 2024 news
At Dynatrace Perform 2024 in Las Vegas, the headliner theme is AI-enabled data. Check back here throughout the event for the latest news, insights, and announcements.
|The benefits of unified observability and security for BizDevSecOps use cases – blog
During a Dynatrace Perform 2024 session, experts demonstrated how unified observability and security can benefit BizDevSecOps use cases.
|Unified observability is key to consolidating tool sprawl and breaking down data silos – blog
Discover how to consolidate tool sprawl and break down data silos with unified observability.
|Unified observability delivers deeper insights with AI-driven analytics and automation – blog
Discover the importance of unified observability, AI-driven analytics, and intelligent automation to get the most value from your data.
|Automating Success: Building a better developer experience with platform engineering – blog
Dynatrace supports platform engineering initiatives, improves developer productivity, and helps teams build and operate software better.
|The future of work: How to zig, zag, and steer your career in the AI era – blog
The ‘Women in Technology’ panelists at Dynatrace Perform 2024 discussed embracing change and continuous learning —key strategies for the future of work in the AI era.
|Cloud observability now mandatory for organizations to thrive amid digital disruption – blog
At Dynatrace Perform 2024, CEO Rick McConnell said that cloud observability and AI-powered strategies are now essential for organizations to compete amid dynamic, disruptive macroenvironments.
|Mitigating risk with AI observability: Dynatrace empowers organizations to embrace AI for all use cases – blog
At Dynatrace Perform 2024, Bernd Greifeneder and Alois Reitbauer discuss AI observability and how organizations can embrace AI properly.
|Cloud cost optimization: Dynatrace helps organizations manage cloud cost – blog
Cloud cost optimization and managing cloud costs are major priorities for every industry to save money and reduce cloud carbon footprint.
|Trace, diagnose, resolve: Introducing the Infrastructure & Operations app for streamlined troubleshooting – product news
The new Dynatrace Infrastructure & Operations app provides ITOps and SRE teams with an up-to-date and comprehensive view of their monitored environments.
|Dynatrace launches Databases app to provide DBA insights across all databases – product news
Introducing Databases, the new observability app for databases from Dynatrace.
|Speed up evidence-driven security investigations and threat hunting with Dynatrace Security Investigator – product news
Dynatrace Security Investigator is a new application on the Dynatrace platform dedicated to security operations and security analysts.
|Dynatrace launches Databases app to provide DBA insights across all databases – product news
Introducing Databases, the new observability app for databases from Dynatrace.
|Observe and optimize multicloud environments with the Dynatrace Clouds app – product news
The new Dynatrace® Clouds app enables seamless management of multicloud environments and provides insights across multiple cloud services in a single, integrated view.
|Kubernetes health at a glance: One experience to rule it all – product news
The new Dynatrace Kubernetes experience enables platform engineers and SREs to better understand and optimize the health and performance of their Kubernetes environments.
|Dynatrace extends AI-powered observability for SAP together with PowerConnect – product news
Dynatrace further extends its capabilities to monitor SAP systems with PowerConnect for enhanced observability across SAP systems.
|Embrace enterprise-wide observability and security with Foundation & Discovery – product news
Announcing Discovery & Coverage, a new app for the Dynatrace® platform, and a new OneAgent® mode called Foundation & Discovery.
|Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and business data at massive scale for analytics and automation in context – product news
Dynatrace addresses data challenges with a single, built-in data ingest functionality: Dynatrace OpenPipeline™, the ultimate addition for data-driven organizations.
|Dynatrace accelerates business transformation with new AI observability solution – product news
Adoption of artificial intelligence (AI) is increasingly imperative for any organization that hopes to remain competitive in the future. However, the benefits of AI are not as straightforward as they might first appear.
|Introducing Dynatrace built-in data observability on Davis AI and Grail – product news
Dynatrace now addresses many issues customers experience around the health, quality, freshness, and general usefulness of data externally sourced into Dynatrace Grail.
|Dynatrace Launches AI Observability for Large Language Models and Generative AI – press release
Enables organizations to embrace AI with confidence by providing unparalleled insights into all layers of AI-powered applications, helping ensure security, reliability, performance, and cost-effectiveness
|Dynatrace Unveils Data Observability for its Analytics and Automation Platform – press release
Davis AI helps ensure all data in the Dynatrace platform is reliable and accurate for business analytics, smart cloud orchestration, and reliable automation
|Dynatrace Releases OpenPipeline for its Analytics and Automation Platform – press release
Enables full control of data at ingest and evaluates data streams five to ten times faster than legacy technologies, helping boost security, ease management, and maximize the value of data
|Dynatrace Teams with Lloyds Banking Group to Reduce IT Carbon Emissions – press release
Real-time insights support leading financial institution to meet its sustainability goals
|Generative AI model observability, cloud modernization take center stage with partners at Dynatrace Perform 2024 – blog
Cloud partners AWS, Azure, and GCP talk generative AI models, cloud modernization, and cloud migration at Dynatrace Perform 2024.
Deriving business value with AI, IT automation, and data reliability
When it comes to increasing business efficiency, boosting productivity, and speeding innovation, artificial intelligence takes center stage. In fact, according to the Dynatrace report, “The state of AI 2024,” nearly three-quarters of IT operations, development, and security teams plan to use AI to become more proactive in executing their work. Further, 62% of organizations have already changed the job roles and skills they are recruiting for to incorporate AI. Because of these trends, AI data analysis and IT automation are front and center at Perform 2024.
But for organizations to maximize the business benefits of AI, they need to continuously evaluate their data to ensure it’s high-quality data, which is the bedrock of solid decision making. This is especially true when taking a composite approach to AI, converging AI types such as causal and generative AI to ensure data reliability. For example, high-quality data and predictive AI enable causal AI to provide precise, continuous, and actionable insights in real time.
The following resources provide more information on how to get the most out of your AI investment, the importance of data quality for business success, and automating manual IT processes to prioritize innovation.
|Why growing AI adoption requires an AI observability strategy – blog
While AI adoption brings operational efficiency and innovation for organizations, it also introduces the potential for runaway AI costs. How can organizations use AI observability to optimize AI costs?
|What is explainable AI? The key to closing the AI confidence gap – blog
As today’s AI models become increasingly complex, explainable AI aims to make AI output more transparent and understandable.
|Responsible AI must-haves for unified observability and security – blog
As organizations turn to AI, how can they ensure that the data and algorithms that fuel AI are based on trusted, unbiased, and responsible AI?
|The state of AI in 2024: Overcoming adoption challenges to unlock organizational success – blog
In the “State of AI” report, respondents outlined the benefits and challenges of AI.
|What is causal AI? Why this deterministic AI approach is critical to business success – blog
Today’s organizations need to go beyond a traditional, correlation-driven approach to identify the underlying causes and effects of an event or behavior and drive better DevOps automation. Enter causal AI.
|Measuring the importance of data quality to causal AI success – blog
Causal AI can accurately pinpoint why an event occurred, but the effectiveness of AI depends on high-quality data. Discover common data quality challenges, how to improve data quality, and more.
Cloud observability is central to platform engineering
The uptick in digital transformation initiatives has created a drive for scalability among global organizations. But the demand for faster delivery speeds and higher-quality software has demonstrated that current software delivery methods are no longer sufficient. Teams face siloed processes and toolsets, vast volumes of data, and redundant manual tasks. To release software at the speed and quality that customers demand, organizations have begun prioritizing automation and creating self-service capabilities, also known as platform engineering.
Platform engineering involves building internal platforms to provide a self-service library to software developers. The goal of the practice is to reduce manual effort and redundant tasks to allow developers to spend more time innovating. The discipline shows promise: According to Gartner, 80% of software engineering organizations “will establish platform teams as internal providers of reusable services, components, and tools for application delivery” by 2026. Recent research also found that 54% of organizations are investing in platforms to enable easier tool integration and collaboration between teams involved in automation projects.
But to achieve the operational efficiency, agility, and optimized developer experience that platform engineering stands to bring, organizations need cloud observability and AI data analysis integrated into their platforms. Building observability-as-code into platform engineering enables automatic service-level objective creation, defined ownership, enriched context, and problem routing to ensure platforms remain available and reliable for developers.
To learn more about platform engineering, explore the following resources.
|Unlock the Power of DevSecOps with Newly Released Kubernetes Experience for Platform Engineering – blog
The development of internal platform teams has taken off in the last three years, primarily in response to the challenges inherent in scaling modern, containerized IT infrastructures.
|What is platform engineering? – blog
Platform engineering enables development teams to deliver frictionless, self-service developer experience with minimum overhead. Learn the importance of platform engineers and more.
|Platform engineering: Empowering key Kubernetes use cases with Dynatrace – blog
Digital transformation continues surging forward. Today, speed and DevOps automation are critical to innovating faster, and platform engineering has emerged as an answer to some of the most significant challenges DevOps teams are facing.
|The platform engineer role: A game-changer or just hype? – blog
The platform engineer role is gaining speed as the newest byproduct of scaling DevOps in the emerging but complex cloud-native world. What is this new discipline, and is it a game-changer or just hype?
|The Observability Guide to Platform Engineering – Part 1: Platform Observability & Success KPIs – webinar
Observability is needed to understand whether the product works as expected, is efficient, is resilient, and provides the desired value to the end users. Join this Observability Clinic to learn more.
Custom apps deliver value for key business needs
More organizations are increasing their reliance on cloud-based technologies. In fact, Gartner forecasts that spending on public cloud services will total $679 billion in 2024 and exceed $1 trillion by 2027. In 2023, organizations mostly used these services as a technology disruptor and capability enabler. But by 2028, these cloud-based services will be a business necessity.
Although the business case for cloud-native technologies is clear, they often require a complex mix of multicloud, hybrid cloud, and on-premises environments. Organizations increasingly struggle with the challenge of monitoring the explosion of microservices and tools that come with these environments.
While app-centric serverless approaches abstract some of the complexities of cloud-native architecture, as the analyst firm Forrester notes, the next frontier for serverless adoption is at the edge. Edge computing brings compute and data storage closer to where data is generated to help reduce costs, boost performance, and improve customer experience.
Organizations are also turning to AI data analysis to enable cloud cost efficiency through FinOps, and to address threat detection, operations automation, and deployment validation use cases. As organizations adopt large language models and generative AI technologies to accelerate efficiency, they introduce yet another dimension of complexity—including security and energy consumption concerns—that needs monitoring.
With this myriad of concerns, organizations need an automated, AI-driven, observability platform approach that can run specialized analysis on a massive scale through custom apps. When accessing observability data for every use case from a schemaless, indexless data lakehouse, out-of-the-box apps provide instant business-critical analysis. And the ability to easily create custom apps enables teams to do any analytics at any time for any use case.
Learn more about what kinds of business questions custom apps can answer from the following resources.
|Dynatrace and Red Hat expand enterprise observability to edge computing – blog
Cloud-native workloads at the edge save costs and boost performance. However, edge observability can be challenging due to distribution, resource limits, and security issues. Continue reading to learn more.
|Sustainable IT: Optimize your hybrid-cloud carbon footprint – blog
As global warming increases, growing IT carbon footprints make energy-efficient, carbon-optimized computing a top priority for many organizations.
|What is FinOps? How to keep cloud spend in check – blog
As cloud spend continues to reach new heights, organizations need a new approach to keep costs in check. Enter FinOps, a public cloud management philosophy that aims to control costs.
|Automated Change Impact Analysis with Site Reliability Guardian – blog
The Dynatrace® Site Reliability Guardian simplifies the adoption of DevOps and SRE best practices to ensure reliable, secure, and high-quality releases.
|Improving customer experience with business process monitoring – blog
Monitoring business processes is one thing organizations can do to help improve the key business processes that enable them to provide great customer experiences.
|AppEngine empowers organizations to create custom apps for better data insights – blog
Learn more about creating custom apps for better data insights.
|Start strong: Words of wisdom for creating Dynatrace Apps – blog
With the release of Dynatrace AppEngine, we revealed how to create your own custom Dynatrace® Apps.
Unified observability and security for business value
As organizations increasingly rely on AI, automation, and cloud operations, data observability and security have never been more vital to business success. As the volume of data grows, organizations urgently seek to ingest and analyze it faster and at a greater scale. However, the costs and risks of poor observability and security of that data are greater than ever. As a result, organizations will increasingly require data observability to enable the rapid and secure ingestion of high-quality and reliable data that is ready to use.
Unified data observability and security are essential to generating insights that users can trust by ensuring the freshness of data, identifying anomalies, and remediating errors. It also supports responsible and accurate AI data analysis, ensures that organizations have the tools to build secure, compliant custom apps, and enables efficient automation, allowing organizations to do more with less—a fast-growing requirement.
Check out the resources below to learn more about unified observability and security for achieving business value.
|Technology predictions for 2024: Dynatrace expectations for observability, security, and AI trends – blog
In our 2024 technology predictions roundup, we explore our expectations for key technologies, such as digital immune systems, generative AI, and more.
|Cloud observability delivers on business value – blog
Cloud observability enables organizations to deliver business value by reducing costs, minimizing IT incidents, and providing better user experiences, as CEO Rick McConnell outlined at the recent Innovate conference.
|What is data observability? – knowledge base
Learn how data observability can help identify, alert, troubleshoot, and resolve data issues in real time.
|Achieving business resilience with modern observability, AI, and automation – blog
Organizations need a technology foundation that promotes business resilience, agility, and flexibility.
|Global Report Reveals DevOps Automation is Becoming a Strategic Imperative for Large Organizations, but Only 38% Have a Clear Strategy for Implementing It – press release
Automation is helping teams improve software quality and reduce costs, yet organizations have only automated 56% of their DevOps lifecycle
|Security by design enhanced by unified observability and security – blog
With Dynatrace, Soldo teams can see what’s happening in a cluster and also correlate among all the applications and workloads. This includes the Kubernetes cluster itself and all the other elements running in their IT environment.
|Dynatrace Grail: The data lakehouse for observability and security analysis and automation – blog
Organizations need an effective way to store, contextualize, and query data to get immediate insights and drive automation.
|Power boundless observability, security, and business analytics with Grail – resource center
Learn more about Dynatrace Grail: The data lakehouse for observability and security analysis and automation.
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