The world has become software-defined, distributed, and complex, creating a widening gap between digital complexity and our ability to manage and govern the systems that run businesses and organizations. To close this gap, we reimagine how observability works. It is no longer enough to collect and analyze telemetry after the fact. Organizations need trusted, intelligent systems that turn real-time data into reliable knowledge, apply advanced AI to reason through that knowledge, and take action to optimize outcomes at every level of the business.

This is the foundation of the Dynatrace 3rd-generation platform. We’ve spent the past two decades shaping the observability market. Today, we are transforming it from a rear-view mirror into a real-time control system for the modern enterprise. Thousands of organizations are already using Dynatrace 3rd generation to turn data into decisions and decisions into action. The result is faster innovation and stronger business results across every layer of the business.

A new model built on knowledge, reasoning, and actioning

Dynatrace 3rd generation introduces a new standard for observability and automation based on three foundational capabilities:

• Knowledge: The Dynatrace platform turns petabytes of real-time data into a continuously updated, queryable knowledge graph. Powered by Grail and Smartscape, it provides trustworthy, fact-based insights with real-time context across dynamic environments.
• Reasoning: Causal, predictive, and generative AI models work together to derive intelligent decisions. These models are context-aware, transparent, and built for enterprise-grade safety and compliance.
• Actioning: Dynatrace enables users to define goals and let intelligent automation determine the best path forward, through innovations like AutomationEngine, AppEngine, and OpenFeature. This shifts operations from reactive remediation to preventive operations and continuous improvement.

Together, these capabilities form the foundation for autonomous intelligence. Dynatrace doesn’t just provide visibility; it enables systems to understand and act. By continuously converting real-time data into trustworthy insights, applying AI to reason through business and technical context, and triggering intelligent, goal-based actions, Dynatrace transforms observability into a real-time engine for automation and impact.

This is not about removing humans from the loop. It’s about empowering teams to define outcomes and rely on the system to carry out the best path forward. As with any leadership decision, autonomy depends on the quality of information and confidence in its context. The same principle applies to AI systems. Dynatrace 3rd generation gives organizations confidence, allowing them to scale decision-making with speed and trust.

Trusted knowledge, not just data

Legacy observability platforms focus on collecting telemetry data. But to support real-time decisions, teams need a trusted knowledge foundation. Dynatrace eliminates silos between metrics, traces, logs, events, user sessions, and security signals by unifying them in Grail, our schema-on-read, massively parallel data lakehouse.

For the first time, users can run any query at any time, with Grail supporting dramatically higher concurrency than traditional observability platforms. There’s no cold storage, no indexing, and no need for rehydration. This unlocks a goldmine of observability data and turns it into reliable, real-time answers.

That data is then contextualized in real time by Smartscape, our dynamic topology engine, and made instantly accessible to AI agents. The result is not just visibility, but deep, evolving system knowledge, providing machine-speed decisions no other platform can match.

AI that reasons with real-time context

Dynatrace has long set the standard for causal AI in observability. With the 3rd generation platform, we expand that foundation by combining causal AI with predictive and generative models. These AI types work together to support decisions at machine speed, with full context. Whether it’s automatically identifying the root cause of a service degradation, forecasting capacity needs, or evaluating how to improve online customer experiences, Dynatrace AI operates with the reliability and transparency required in enterprise environments.

Now, organizations can pursue modernization, transformation, and agentic AI initiatives with greater confidence. Dynatrace helps make AI accessible and actionable by reducing friction, delivering answers precisely when and where they are needed. Davis CoPilot enables natural language queries, workflow generation, and seamless integration into IDEs. It provides intelligent assistance at every step and supports a broad range of use cases across observability, security, and business operations with explainability, precision, and trust.

Automation that adapts to your goals

Traditional automation is limited by what is explicitly scripted. Dynatrace has taken a different approach. With the 3rd generation platform, you define high-level goals, and the platform determines the best way to achieve them. By grounding automation in real-time, high-quality data and precise causal analytics, Dynatrace ensures that actions are driven by accurate understanding, not assumptions.

This goal-based automation can resolve incidents, optimize performance, reduce cost, and even generate pull requests that improve code quality.

For example, preventive cloud operations allow site reliability engineers to move from firefighting to strategic orchestration. Instead of chasing alerts, SREs can focus on managing service-level objectives and improving business outcomes. Dynatrace handles the rest.

Built for the future of cloud and AI

The complexity of cloud-native architectures, Kubernetes deployments, and emerging agentic AI models are already testing the limits of traditional observability. Dynatrace 3rd generation is designed for the future.

By unifying telemetry, security data, and business context into a single real-time graph powered by Grail, Dynatrace provides the AI-powered intelligence required to operate modern systems with confidence. And by embedding automation throughout the platform, teams can scale faster than headcount, without sacrificing control or trust.

Turning observability into intelligent action

Organizations like TELUS and Air France-KLM are already seeing results: faster resolution, improved resiliency, and reduced downtime.

“By combining our Agentic AI initiatives with Dynatrace’s AI Observability capabilities, we’ve successfully optimized our development and operations workflows. We’re driving innovation and delivering measurable business impact while reducing downtime.”

– TELUS

“The AI and predictive capabilities from Dynatrace were a differentiator. We’re confident that any problem that arises can be dealt with quickly, dramatically reducing operational and revenue impact.”

– Air France-KLM

The combined impact of agentic AI initiatives and AI-powered observability extends beyond IT. These outcomes drive business performance, from greater availability and productivity to better customer experiences.

What this means for your organization

The Dynatrace 3rd-generation platform defines the path forward to a future where software can understand, reason, and act. It helps your organization move from reactive to proactive, from fragmented tools to unified intelligence, and from scripted automation to AI-driven operations.

Whether you are focused on cloud modernization, application security, cost optimization, or AI governance, Dynatrace provides a foundation to build and scale with confidence.

This is the evolution of observability. One built on context, driven by reasoning, and capable of taking action.

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I’ve always been intrigued by monitoring the inner workings of technology to better understand its impact on the use cases it enables and supports. Driven by that value, Dynatrace brings real-time observability, security, and business data into context and makes sense of it so our customers can get answers, automate, predict, and prevent.

Executives invest in Dynatrace to enable their IT operations, security, and development teams to maintain visibility into all their digital services and ensure flawless, secure digital interactions.

A gold mine of answers

What may be a surprise for executives is that Dynatrace unearths a wealth of business insights from observability data. Information related to user experience, transaction parameters, and business process parameters has been an unretrieved treasure, now accessible through new and unique AI-powered contextual analytics in Dynatrace. Have you already thought about how you could use the data derived from your digital systems to accelerate your business and improve your ability to make decisions with real-time insights?

Executives drive business growth through strategic decisions, relying on data analytics for crucial insights. However, enabling faster and even automated decision-making is challenging due to a lack of real-time data access.

Several factors limit executives’ ability to get timely results for their business:

• Standard business intelligence (BI) systems don’t have access to the inner workings of digital systems, so teams don’t have access to the data they need.
• Different data types are in different silos, even averaged and generalized with lost information without a possibility for analytics in context.
• Common business analytics incur too much latency. There can even be days of reporting intervals, which hinders real-time business insights.
• Lack of visibility into business processes to improve, optimize, and remediate issues and systems harms business success.
• Different departments have different data sources and different ways to interpret data, causing misalignment.

With Dynatrace, executives can unearth a treasure trove of context-rich data that offers unprecedented insight into their business.

The real-time data in context with AI-driven analysis from Dynatrace provides executives with incomparable value and customer satisfaction to improve their business processes. The following are five examples of many:

• Order to cash processes to ensure timely order processing and revenue recognition.
• Order fulfillment to track the preparation and delivery of goods or services.
• Service provisioning to ensure resources are allocated, configured, and activated properly.
• Trade settlement to track the transfer of securities and funds after a trade is executed.
• Claims processing to ensure timely settlement, from first notice of loss to payment.

Turn business analytics real-time and get answers you couldn’t get before

My core goal was to create new value from automatically captured and enriched observability data and make it more accessible than today’s common BI solutions. That goal also requires eliminating barriers to real-time analytics, such as the many data transformation and preparation steps most that BI solutions need and the need for high-fidelity data in full context so users can find even the unknown unknowns.

Many organizations attempt to apply analytics to available data by making it static through data lakes, rehydrations, schemas, indexing, and warehousing, which seemed backward and complicated to me. This approach creates data silos, drives up costs, complicates contextual analysis, and limits the scope of business analytics.

To achieve my goal with Dynatrace, we had to rethink observability from the ground up. We concluded we needed to build a massively parallel processing data lakehouse at its core, as no existing database solution could overcome those analytics barriers at exabyte scale, especially in the era of AI.

With Dynatrace, we’ve created the only platform that can unify heterogeneous data, including logs, business events, user sessions, metrics, traces, emails, and much more with context and causal dependencies.

Dynatrace treats business processes as observable assets, putting each step in context with business and IT data. This integrated approach fosters mutual understanding and keeps business and technology in close lockstep, empowering everyone to get answers they couldn’t get before.

How executives leverage the newly gained visibility

Executives are change drivers. But change can only be driven with proper visibility and derived conclusions. Therefore, insights into how business growth and customer satisfaction are related to business processes are essential.

Business Observability employs a proven combination of three types of AI for analytics: causal, predictive, and generative. Using this hypermodal, “Power of 3” AI approach, teams can predict potential risks and disruptions. And with Dynatrace AutomationEngine, they can take preventive actions and enable intelligent orchestration and automation with business context. This predictive capability is crucial for business resilience as it allows organizations to anticipate challenges and mitigate their impact. Furthermore, by applying Dynatrace AI to historical data, executives can predict future trends and prepare contingency plans.

Only with this visibility is it possible to detect and fix broken processes, reduce and optimize process steps, steer investment priorities, automate and orchestrate, improve performance and user experiences, and ensure reliability and security.

Drive your business goals more effectively with a single source of truth

Organizations often struggle to align toward common goals, as every department measures them differently. What if you could take real-time data from your digital systems, such as consumption, usage, revenue, adoption, success rates, customer satisfaction, and more?

Dynatrace provides a single, real-time source of truth that eases alignment across departments to work toward joint critical business goals. Dashboards, apps, and reports with insights from digital systems originate from the same full-fidelity sources so that Business Observability becomes the “lingua franca.” As every department needs to place joint KPIs into its own context, Dynatrace makes it easy to expand, augment, and drill down to specifics. Dynatrace’s ability and ease to get answers to any question at any time is unmatched.

Dynatrace eases and increases data privacy by eliminating many steps in typical ETL (extract, transform, and load) and data warehouse procedures. Dynatrace unifies capture, storage, analytics, and visualization into a single platform that ensures consistent and gapless access to information. Dynatrace also certifies SSO access, encryption, filtering, and obfuscation techniques to meet the highest standards, so departments have access to what they need.

Causal AI: Connecting technical signals to business outcomes

At the core of Dynatrace’s business observability is our use of causal AI, one of the multiple AI models employed by Dynatrace, a unique capability that goes beyond correlation to uncover the actual root causes of issues and performance anomalies. Unlike traditional AI models that rely on pattern recognition alone, causal AI understands the why behind system behaviors. This enables business and IT leaders to make faster, more confident decisions by connecting technical signals directly to business outcomes. Whether it’s identifying the cause of a revenue-impacting slowdown or optimizing user journeys in real time, Dynatrace ensures that every insight is both explainable and actionable.

Becoming a data-driven enterprise

Business observability lets you tap incremental value from your observability investments, strengthening executives’ ability to drive businesses and customer satisfaction forward. A clear step towards a more data-driven enterprise, that is more competitive through insights from data of their digital services.

The post Real-Time Business Observability with Dynatrace appeared first on Dynatrace news.

As executives, we drive change, balancing modernization speed with its risks. Technology—both a blessing and a curse—not only propels businesses forward but also adds complexity as developers introduce new innovations to enhance customer services and competitiveness.

Anticipate future customers’ needs

Anticipating customer needs three to five years ahead helps to reduce wasted investments into “wants” and directs them toward “needs” that future-proof the business.

This mentality has driven me to continuously innovate and reinvent Dynatrace®. My ongoing evaluation of how technology changes the way digital services are architected allowed me to recognize early on that change is on the horizon. The rise of cloud-native technologies, the convergence of observability and security, and the demand for actionable insights required a new approach to managing data at an exabyte scale, as existing databases could no longer keep up.

Change is constant

In our fast-paced world, success requires thinking big but acting small to create value quickly and sustainably. For cloud modernization, this means executives must change how software is built, operated, and secured; improve collaboration processes; and increase automation.

Dynatrace gives executives an indispensable platform for driving this change in the following three ways:

• Enabling a modern AIOps strategy,
• Accelerating software delivery, and
• Making scarce engineering resources more productive.

1. Go beyond traditional AIOps

The first wave of AIOps investment was about “noise reduction.” This has been helpful but falls short of the potential offered by the preventive NoOps and NoSOC approaches that many executives seek AI to enable.

With current hype causing a resurrection in AI investment, it is tempting to believe that this time, machine learning and generative AI will fulfill the promises of the past. However, while the advances in machine learning-based AI are a huge step up for many use cases, it is still problematic to apply it to prevent incidents and errors in IT systems. Why? Because training an AI requires errors, failures, and behaviors to occur many times to ‘learn’. While the exact numbers may have been reduced by the advances in generative AI, which executive wants to have service outages just to train AI to prevent them in the future? Even if it was possible to arrive at a trained model, it would quickly become obsolete as services get updated and new features introduced.

As we consider a way forward, I urge all executives to recognize that we are in the trough of disillusionment in the AI hype cycle. This is good news, as it allows us to think more rationally. We need to understand that there are multiple types of AI, each suited for different purposes.

Dynatrace is uniquely designed to help executives elevate their AIOps – and AISecOps strategy – to a different level by combining multiple types of AI in a single framework known as hypermodal AI: the power of predictive AI, causal AI, and generative AI for observability, security, and business use cases. Proven by thousands of customers in large-scale IT deployments, this approach delivers greater speed, automation, and precision.

Our hypermodal AI automatically infers the root cause of issues based on a real-time updated graph without needing to learn. Now, it is more feasible than ever to automate workflows for self-healing, security investigation, and preventive operations to deliver great software with confidence, all while enhancing security measures and boosting productivity.

2. Accelerate software delivery

One of the best features of the cloud and Kubernetes® is achieving most availability needs with minimal effort, a major improvement over the classic datacenter model. This allows executives to focus on accelerating software delivery. However, the inverse Pareto principle applies: achieving the final 20% of flawless, secure services requires 80% of the effort.

That’s why APIs have become my favorite feature of the cloud as the key to automate and orchestrate. This is where Dynatrace comes in. Dynatrace integrates with the cloud ecosystem and DevOps toolchain to enhance automation across software delivery, resilience, and security throughout the software lifecycle.<

Throughout the ten years since we embraced NoOps at Dynatrace, I understood the temptation to favor releasing new features over investing in automation. Automation always paid off. We have since developed over 100 ready-made use cases to support platform engineering across the software delivery lifecycle. From development and release to operation and flaw prevention, prediction, and resolution, Dynatrace offers a robust data analytics-driven automation platform.

We’ve seen the many benefits of investing in automation, including the following capabilities:

• Releasing faster and securely with automated quality and security gates
• Catching bugs earlier, before customers experience them
• Preventing issues with predictive operations
• Avoiding unnecessary high consumption and cost with causal and predictive auto-scaling
• Empowering developers with context-rich insights derived from self-service observability and security
• Orchestrating more intelligently with real-time user behavior and business data

In a nutshell, Dynatrace allows executives to accelerate software delivery with confidence.

3. Increase teams’ productivity

As Dynatrace CTO, one of the questions constantly on my mind is: how can I enable my team to be more productive?

Over the past 15 years, most of us have embraced the “shift left” ethos to empower software developers. The earliest iteration of this was the “you build it, you run it” mentality. However, given the responsibilities of creating enterprise-scale and secure software, the “extend left” ethos proves to be more successful and fitting for cloud modernization.

“Extend left” refers to sharing responsibility amongst developers and operations teams, through adding more self-service for developers while retaining consistency, tooling and knowledge management with central teams.

As neither full decentralization nor full centralization will be effective, a hybrid model, supported by platform engineering approaches, is much more likely to succeed. Centralizing the necessary expert knowledge within a platform engineering team enables rapid, secure, and safe software delivery. At the same time, this approach decentralizes innovation, making it accessible to many.

Dynatrace was created to enable precisely this approach, leveling up developer experience by providing self-service capabilities while allowing central safety and oversight maintenance. This gives executives the best of both worlds: decentralized autonomy supported by centralized governance and control.

Armed with the use cases across the three areas outlined here, executives can modernize their cloud operations faster and equip their teams with the capabilities they need to accelerate innovation confidently. As a result, they will be better placed to anticipate change and continuously reinvent their organization to stay ahead of the market.

The post Three ways Dynatrace can help to drive innovation through cloud modernization appeared first on Dynatrace news.

Cloud-native technologies are driving the need for organizations to adopt a more sophisticated IT monitoring approach to satisfy the competitive demands of modern business. Business observability is emerging as the answer.

The ongoing drive for digital transformation has led to a dramatic shift in the role of IT departments. They’ve gone from just maintaining their organization’s hardware and software to becoming an essential function for meeting strategic business objectives. Today, IT services have a direct impact on almost every key business performance indicator, from revenue and conversions to customer satisfaction and operational efficiency.

As a result, organizations have been forced to reevaluate what success looks like for the modern IT department and how they monitor and manage the performance of IT services.

Seeking insights from data

Every organization depends on data to make decisions. However, too often teams are forced to rely on disjointed data that lacks context, which leads to poor decision-making and wasted resources.

This problem has worsened as enterprise operational complexity has grown. In today’s digital-first world, data resides across dozens of different IT systems, from critical business applications to the modern cloud platforms that underpin them. Connecting the dots between these various silos of data to understand the relationship between the health of IT services and the business outcomes they enable has become a particular challenge.

The journey toward business observability

Traditional IT monitoring that relies on a multitude of tools to collect, index, and correlate logs from IT infrastructure, networks, applications, and security systems is no longer effective at supporting the need of the wider organization for business insights. This traditional approach presents key performance metrics in an isolated and static way, providing little or no insight into the business impact or progress toward the goals systems support. Often, these metrics are unable to even identify trends from past to present, never mind helping teams to predict future trends.

As a result, organizations need to shift toward more sophisticated models of monitoring and managing IT operations. With hybrid and multi-cloud architectures rendering organizations’ environments more complex and distributed, cloud observability has become increasingly important. Likewise, integrating metrics and traces with log data helps to identify crucial context that reveals the interconnections among and importance of signals from all levels of the network. These capabilities are essential to providing real-time oversight of the infrastructure and applications that support modern business processes. With cloud observability, organizations can make data-driven decisions to improve the health of their IT services and proactively mitigate potential risks.

Partners such as Deloitte provide key expertise in cloud observability and are instrumental for many organizations embarking on digital transformation. By leveraging Deloitte’s strategic insights, businesses can align their IT investments more closely with their overarching business objectives, driving both efficiency and growth.

However, the journey doesn’t end there. The final stage is developing true business observability. Business observability ensures that all IT activity and investment is aligned with an organization’s strategic business objectives by enabling superior data-driven decision-making. It provides insights to help address not only operational issues such as cost reduction and risk mitigation but also customer-centric issues such as optimizing user journeys and creating personalized experiences.

Five ways business observability drives impact

There are several key advantages to making the transition to business observability, from mitigating the risks of adopting new cloud architectures and the challenges of data sovereignty, to rightsizing the IT estate and implementing greener technology. Five of the most important benefits of modern business observability are identified below.

1. Operational optimization. Across all sectors, system performance, infrastructure reliability, and transaction speeds are essential, whether it’s grid management for the energy industry or supply chain integration for retailers. An effective business observability strategy can help to meet these requirements by stabilizing the entire application stack, reducing operational expenditure, and preventing downtime in critical business systems.
2. Security and compliance. Organizations need to continually track transactions, user behaviors, and alerts to maintain security and compliance by detecting anomalies and indicators of potentially fraudulent activity or breaches. Business observability provides a cohesive approach to meeting these goals by offering a complete end-to-end view of application threats and vulnerabilities, assessed according to the level of potential risk to the enterprise.
3. Optimized experiences. By integrating data on user interactions, omnichannel behaviors, customer journeys, and purchasing patterns, organizations can take more effective action to deliver consistent and personalized experiences.
4. Resource optimization. Tracking and analyzing data from multiple sources enables businesses to optimize the performance of their IT services and prevent unnecessary downtime, while also reducing unnecessary resource consumption and carbon emissions through efficient asset utilization.
5. Agility and innovation. Organizations need oversight of the entire innovation pipeline, from ideation to implementation, to identify bottlenecks and streamline development and testing processes. Mature business observability capabilities allow businesses to reduce time-to-market for innovation by streamlining the product development cycle, while also providing key insights into user needs and the feasibility of potential feature additions.

Ultimately, organizations with mature business observability capabilities are better placed to use IT as a catalyst to drive better outcomes, streamline their operations, and mitigate risks, while unlocking greater customer satisfaction. This will help to place them at the forefront of the digital transformation landscape.

The post Business observability: From IT monitoring to driving digital transformation appeared first on Dynatrace news.

As a technology executive, you’re aware that observability has become an imperative for managing the health of cloud and IT services. You may not be aware of how much untapped value is waiting to be unlocked through the right observability platform. Data with context can improve your ability to deliver on your goals, modernize your organization, and accelerate business transformation.

The Dynatrace platform enables executives to drive change faster, increase IT and R&D productivity, reduce business risks, optimize costs, and decrease carbon footprint. These outcomes are made easy through the platform’s unique ability to turn data into answers and action, in contextual, real-time, and cost-effective ways that were previously impossible.

Unearthing a goldmine of value

As founder and CTO of Dynatrace, I must constantly drive change. I also have the privilege of being “customer zero” for our platform, which enables me to continually discover where Dynatrace can deliver on more use cases to drive my team’s productivity and innovation. Change is my only constant.

Realizing that executives from other organizations are in a similar situation to my own, I want to outline three key objectives that Dynatrace’s powerful analytics can help you deliver, featuring nine use cases that you might not have thought possible.

Drive innovation

To remain competitive, executives are seeking productivity gains while simultaneously driving modernization initiatives. Observability data presents executives with new opportunities to achieve this, by creating incremental value for cloud modernization, improved business analytics, and enhanced customer experience.

However, technology executives face a significant challenge getting answers in time, as their needs have evolved to real-time business insights that enable faster decision-making and business automation. Exploding volumes of data must be prepared, catalogued, stored in multiple, disconnected tools. The data must then be retrieved from data lakes and converted into rigid schemas. It can take data analysts months to extract insights and answer executives’ questions using these approaches.

With the latest advances from Dynatrace, this process is instantaneous. Unlike anything before, contextual analytics in Dynatrace provides answers to any question at any time, instantaneously. That’s because it does not require any pre-prepared schemas, and access to cold/hot storage is fully automatic and with zero latency. Moreover, it is fast, powered by its massively parallel processing data lakehouse.

As a result, organizations can reduce complexity, effort, and processing time to run powerful business analytics on exabytes of data in real time. Dynatrace enables executives to drive a stronger, data-driven organization by increasing automation and productivity.

Mitigate risk

To cope with serious business risks —including major outages, security breaches, or missing out on realizing AI’s value — executives require a modern, proactive approach. Dynatrace analytics capabilities, powered by hypermodal AI, enable executives to drive improved availability, strengthened security compliance, and heightened confidence in AI initiatives.

Executives are shifting to proactive risk management, aiming to prevent availability issues and expedite remediation. However, AI introduces new risks, such as increased software complexity, accelerated cyber-attacks, and potential regressions from rapid releases. Siloed teams and the reliance on disparate tools lead to manual intervention and delays, which are unsustainable given tightening regulations including DORA, NIS2, and the SEC’s four-day reporting rule.

Dynatrace uniquely solves this conundrum, enabling executives to use a new generation of AIOps and SecOps to predict and mitigate risk, rather than reacting to availability and security incidents. It does this by combining causal, predictive, and generative AI to uncover the deep context of issues using a unified source of observability and security data. Automated root-cause analysis and real-time risk analysis are only two examples that help executives get closer to the vision of self-healing operations and security.

Optimize cost

With the constant pressure to do more with less — or much more, much faster — executives must control cost and complexity. Dynatrace can help executives to achieve these goals by reducing tool sprawl, driving cost optimization, and meeting their sustainability goals.

Optimizing costs is a proven way to free up budgets for innovation. Young talent (our future executives) has a valid interest beyond making more money, as sustainability and green coding are vital to protecting both their own and our future.

As new waves of technology roll over us, executives are struggling to keep tool sprawl under control. Tool sprawl not only goes deep into our pockets, but also hampers consistency and productivity. Tens or even hundreds of DIY and commercial tools are being used to handle logs, metrics, traces, security events, and vulnerabilities all in their own way.

Insights are therefore dispersed in a multitude of data lakes, storage systems, and reporting platforms. This is inefficient and creates avoidable risks. The principle of “keep it simple, stupid” is more important than ever, translating to consolidating tools and making processes more consistent at higher grades of scalability and automation.

Dynatrace is uniquely placed to meet this need as it consolidates tools, storage, data, processing, and automation capabilities together in a single, unified platform. This reduces the number of moving parts and eliminates process inconsistencies, driving team productivity and increasing software delivery quality and security.

As a result, organizations can streamline processes by moving towards platform engineering and developer self-service portals to unburden engineers while increasing software quality and security at a higher consistency.

The post Nine ways technology executives can get significant business value with the right observability platform appeared first on Dynatrace news.

Lest readers believe that business digital transformation has fallen out of fashion, recent data suggests that digital transformation initiatives are still high on the agenda for today’s leaders.

According to a recent Thomson Reuters survey, nearly 50% of C-level executives said that business digital transformation was their top priority over the next 18 months, followed by reducing costs (44%) and increasing customer satisfaction (44%). Business digital transformation and modernization are enduring objectives, despite the rise of generative AI as a priority over the past year.

That’s why, according to a LinkedIn article, business digital transformation is no longer a nice-to-have.

In fact, business digital transformation may now be mandatory for organizations to survive and thrive in increasingly volatile and competitive macro-economic environments:

“If you consider the pre-covid [sic] era, digital transformation was a bonus,” the article argues. “Now, however, with the way the pandemic has caused a major disruption in businesses on a global level, digital transformation has become a thing of necessity.”

Not surprisingly, business digital transformation is also a growing market.

According to Markets and Markets, for example, the digital transformation market is projected to grow at a compound annual growth rate of 19.1%, from $521.5 billion in 2021 to $127.5 billion in 2026.

Digitally transformed organizations are expected to contribute to more than half of the GDP by 2023, accounting for $53.3 trillion. And according to Statista, $2.4 trillion will be spent on digital transformation in 2024.

Which technology trends are fueling business digital transformation? AI and DevOps, of course

The C suite is also betting on certain technology trends to drive the next chapter of digital transformation: artificial intelligence and DevOps.

Generative AI benefits from a composite AI approach

According to the Thomson Reuters survey, nearly 40% of respondents say they are using generative AI* to fuel business digital transformation.

While generative AI has received much of the attention since 2022 for enabling innovation and efficiency, various forms of AI—generative, causal**, and predictive AI***—will work together to automate processes, introduce innovation, and other activities in service of digital transformation.

According to IDC, AI technology will be inserted into the processes and products of at least 90% of new enterprise apps by 2025. And according to S&P Global’s 2023 Global Trends, 69% of surveyed organizations have at least one AI project in production, while 28% are achieving enterprise scale.

DevOps maturity follows business digital transformation

DevOps methodology—which brings development and ITOps teams together—also forwards digital transformation.

By bringing developers and IT operations teams together, teams can collaborate to accelerate software development times, increase workflow efficiency, automate manual tasks, and create higher-quality software. DevOps can also reduce human error throughout the software deployment process.

For one Dynatrace customer, a hardware and software provider, introducing automation into DevOps processes was a game-changer.

In the past, administrative tasks bogged down the developers at this Dynatrace customer, so they spent only 20% of their time writing software to meet their digital transformation goals. Today, with a greater focus on DevOps and developer observability, engineers spend 70%-75% of their time writing code and increasing product innovation. It has effectively flipped its time spent on manual and administrative tasks vs. its strategic software development efforts.

Another Dynatrace customer, a federal agency, used DevOps automation to reduce mean time to repair (MTTR) so teams could resolve issues before they affected users or compromised systems.

Business digital transformation at the intersection of AI and DevOps

In 2024, the C suite clearly has its eye on AI and DevOps as core enabling technology trends for business digital transformation. Ultimately, these tech trends can elevate organizations’ competitive advantage and accelerate modernization.

Today, they are paving the way for greater automation, more strategic and high-quality product development, and less error-prone activity. And of course, these goals overlap with the objectives of digital transformation, including product innovation, cost optimization, and risk mitigation.

Ultimately, buisness digital transformation, AI, and DevOps are interwoven. Each trend is enabling the others, and forward-leaning organizations are steering their modernization efforts in accordance with this notion.

For more on DevOps automation, check out our “DevOps automation is becoming a strategic imperative.”

And for more on digital transformation, check out “Master your digital transformation.”

________________________

*Generative AI—artificial intelligence that generates content, such as text, programming code, images, audio, videos, and more.

**Causal AI is an AI technique that identifies the precise cause and effect of events or behavior.

***Predictive AI uses machine learning, data analysis, statistical models, and AI methods to predict anomalies, identify patterns, and create forecasts about future events.

The post Why business digital transformation is still a key C-level priority today appeared first on Dynatrace news.

Today’s macroeconomic environment is dynamic and uncertain, generating many questions about the future of work.

New technologies are disrupting the landscape, while company mergers, acquisitions, and economic volatility abound. As artificial intelligence becomes more pervasive in organizations, the workforce senses that the future of work is undergoing massive shifts.

Proactive workforce members are acclimating to these fluid conditions through a variety of strategies, such as career “zigzagging” (a less linear career path that involves diverse roles), career upskilling, and mentoring.

For women in technology, these strategies have never been more important to help them survive and thrive as they embark on a new era of AI-enabled work, agreed panelists at the “Women in Tech” panel at Dynatrace Perform 2024.

According to Laura Heisman, Dynatrace chief marketing officer and panelist, women should embrace sudden career shifts as opportunities.

Heisman joined Dynatrace in January 2024, with an enduring career to date in the technology sector. She has held positions at Citrix Systems, GitHub, and most recently, VMware. At the outset of her career, she worked in consumer products in Southern California. Then, after being introduced to the “World Wide Web” and new types of businesses and marketing opportunities, she shifted her career course to focus on technology.

“That was a huge zig and zag for me,” Heisman recalled. She compared that moment in her career with the present picture for the workforce, as artificial intelligence matures and has a massive impact on the future of work.

“We are in a similar moment with AI,” Heisman emphasized. “Take the opportunity to learn everything you can. It is this huge moment for all of us in our careers and in how we do our jobs,” she said.

The future of work with generative AI

Data suggests that this inflection point in the future of work—spurred by generative AI (a type of artificial intelligence technology that can produce various types of content, including text, images, audio, and more)—is encouraging excitement and apprehension about job prospects in the era of AI.

According to the report, “Work, workforce, workers: Reinvented in the age of generative AI,” 95% of workers see value in working with generative AI. But approximately 60% are concerned about job loss, stress, and other issues, given the impact of AI on the workforce.

Data also suggests that the workforce is receptive to the coming tsunami of changes AI will bring, particularly in the form of upskilling and reskilling. According to a recent survey, for example, 68% of workers are aware of coming disruptions in their industries and are willing to reskill to remain competitively employed.

That’s why Heisman and other members of the “Women in Tech” panel stressed the importance of continued learning to nurture one’s career. This strategy is becoming essential to thrive in the future of work.

According to Jolly Mishra, director of partner development at Microsoft, it’s also critical for women to encourage the next generation to pursue STEM disciplines (science, technology, engineering, and math)—and to recognize that these disciplines are “cool” for women to engage with—even if younger women are not easily accepted in school as “nerds.”

Inspiring inclusion—and bucking exclusion

Sue Quackenbush, Dynatrace chief people office and panel moderator, asked the panelists about methods to inspire inclusion for women in technology. It sparked a conversation about embracing diverse work styles as key to the future of work.

Indeed, while technology companies often recognize the most assertive people in the room, those with quieter styles may have important contributions to make and that true inclusion is about bringing all good ideas to the table.

“You have to give everyone a voice,” Terese Pate, director of product development at GXC Technology, said. “Sometimes the quietest voice has the best ideas.”

At the same time, panelists noted that inclusion may not always be offered readily, and women still have to steer their own path to be recognized for their talents, not their gender, in the technology sector.

Microsoft’s Mishra recalled trying to pursue opportunities in the sector, then being told, “Don’t waste your time. They don’t accept women in technology.”

Rather than accepting a closed door to opportunity, Mishra said, the lack of inclusion became motivation for her. She was even more determined to pursue the role. “It was essentially a kind of superpower for me,” she said.

Pate agreed. “It was about learning my craft to be as good or better than anyone else in the room. They had to listen to me because I was the one who had the answers.”

Intentional—and unintentional—mentoring

Quackenbush also asked panelists about their mentors during their careers and whether mentorship was a formal process.

Pate noted that she used her entrée into technology as an opportunity to learn from her peers.

Early on, Pate sought out her mentors to teach her and help her elevate her skillset. “I said, ‘Elevate me, work with me. That is huge—and that’s how I learned,” Pate recalled.

Heisman noted that her mentoring process was more informal, with two male mentors who organically served as soundboards and guidance during her career.

“It was not sponsorship, it was mentorship, and it’s a two-way conversation, where we are building a relationship and understanding each other together,” Heisman said. This nontraditional way of thinking about mentorship breaks the barriers of hierarchy and brings parity and equal exchange to the process.

Building a career with grit

Panelists also recognized that pursuing a career in technology requires determination and grit. In some cases, women may need to believe in themselves to pursue worthy opportunities.

Heisman noted that research shows that men will apply to a job with about 60% of the qualifications, while women believe they need 100% of the skills required.  That may discourage them from even applying when in fact they are qualified. Knowing this, job descriptions and hiring methods need to adjust to build diverse teams.

“Be comfortable being a woman in tech. Wear it as a badge of honor. Don’t doubt yourself and your skills,” Heisman encouraged.

But just as the future of work requires persistence, it also brings new opportunities for women in technology that may not have been as rich prior to the emergence of the COVID-19 pandemic.

As Microsoft’s Mishra noted, the normalization of hybrid work has provided all workers opportunities to participate in the sector without following traditional schedules in the office. This flexibility particularly helps women navigating caregiving responsibilities stay in the workforce.

The future of work is about diversity and inclusion

Mishra noted that as the AI era unfolds, diversity and inclusion are approaches that reflect the cornerstone of reliable, unbiased AI. Responsible, reliable AI requires a diversity of data and perspectives to enable information accuracy, freshness, and unbiased outcomes.

Mishra emphasized, “Diversity and inclusion are so important for innovation.”

For all Perform coverage, check out the Dynatrace Perform 2024 guide.

For more on digital transformation and AI, check out our report “The state of AI 2024.”

The post The future of work: How to zig, zag, and steer your career in the AI era appeared first on Dynatrace news.

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?
	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.

Join us for Dynatrace Perform 2024.

Ready to try Dynatrace for free? Learn more about our trial.

The post Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis appeared first on Dynatrace news.

Every year at our annual user conference, Dynatrace Perform, we recognize the most inspiring success stories from our most innovative, transformative customers and partners.

At Dynatrace Perform 2024, we had the honor of again hosting our awards ceremony to publicly acknowledge the organizations that use observability, AI, and analytics to manage modern cloud complexity, secure their business, and drive meaningful impact across their industries. We’re proud to announce the following winners:

These awards recognize the industry game changers whose outstanding contributions have helped push the boundaries of software intelligence. Dynatrace shares these stories to inspire innovation, empower change, and enable the confidence necessary for organizations to accelerate their digital transformation.

Congratulations to the winners!

The post Dynatrace Perform 2024: Recognizing customer and partner digital gamechangers appeared first on Dynatrace news.

With our annual user conference, Dynatrace Perform 2024 rapidly approaching on January 29 through February 1, 2024, our teams, partners, and customers are buzzing with excitement and anticipation. Perform serves yearly as the marquis Dynatrace event to unveil new announcements, learn about new uses and best practices, and meet with peers and partners alike. At this year’s Perform, we are thrilled to have our three strategic cloud partners, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), returning as both sponsors and presenters to share their expertise about cloud modernization and observability of generative AI models.

Dynatrace unified observability and security is critical to not only keeping systems high performing and risk-free, but also to accelerating customer migration, adoption, and efficient usage of their cloud of choice. More so than ever before, organizations are investing in cloud migration and cloud modernization to lower total cost of ownership (TCO). These investments, in turn, extend to unifying observability with context and intelligence across increasingly dynamic and complex cloud environments, and ensuring that their cloud ecosystems are not only reliable and secure but also optimized to realize resource savings and accelerate release delivery.

At this year’s Perform, all three of our cloud partners will take the stage to speak about these benefits—and how to achieve them—in their expert-led sessions. Takeaways will be immediately applicable for both technologists and business stakeholders alike, helping attendees put into place the right observability and security practices with Dynatrace to further advance their cloud modernization journey (and make it a smooth one at that).

Read on to learn what you can look forward to hearing about from each of our cloud partners at Perform. If you’re unable to join us in Las Vegas, be sure to register to attend virtually—or view sessions on-demand afterward—so you don’t miss out!

Accelerating AWS migration and optimizing efficiency with Dynatrace

As many companies embrace cloud modernization and begin migrating to the AWS cloud (or continuing to move existing workloads), complexity can introduce uncertainty into the process. What can we move? What will the new architecture be? How can we ensure we see performance gains once migrated? These are the big questions that have slowed, or prevented, many teams from migrating.

In an upcoming partner session at Dynatrace Perform 2024, Mark Jaggers, AWS technical program manager, will detail the key steps of the migration process and showcase where Dynatrace is integral in not only answering these questions, but providing the technical ability and automation to migrate more quickly and confidently.

Session attendees will learn first-hand how Dynatrace natively integrates into the AWS Migration Hub to provide a full topology of on-prem workloads and dependencies in order to generate the ideal cloud-based architecture in the AWS cloud. Additionally, discover how Dynatrace is easily deployed on newly migrated workloads to get instant insights into performance, utilization, security, and efficiency post-migration. Finally, Mark will take attendees a step further to demonstrate how Dynatrace underpins the AWS Well-Architected pillars of cost optimization and operational excellence by helping enterprises to right-size AWS resources with utilization metrics and configuration for continuous efficiency in the cloud.

Learn more about Dynatrace and AWS in the whitepaper, Why modern, well-architected AWS clouds demand AI-powered observability.

Microsoft and Dynatrace solve cloud modernization complexities with generative AI models

In the cacophony of digital noise, where every buzzword promises to revolutionize businesses, how do organizations discern the transformative from the trivial? The struggle to prioritize digital transformation is real, and in this ever-evolving landscape, standing still is not an option. Innovation and cloud modernization aren’t luxuries; they’re the heartbeat of progress.

In this upcoming partner session at Dynatrace Perform 2024, Peter Laudati, Microsoft Cloud solution architect – GPS US, and Jay Gurbani, Dynatrace senior technical partner manager, will share how Microsoft and Dynatrace are helping enterprises solve the complexities introduced by cloud modernization and how organizations can use tools and innovations to unlock the true potential of the digital ecosystem. The session will explore leveraging AI for real-time business decisions and real-world applications.

Learn more about Dynatrace and Microsoft in the whitepaper, Why modern, well-architected Azure clouds demand AI-powered observability.

Enhancing generative AI models in real-world production settings with GCP and Dynatrace

In the ever-evolving landscape of artificial intelligence, the fusion of leading technologies can yield unparalleled results. This partner Perform session will delve into the exploration of Dynatrace, a leading observability and security platform, and Vertex AI Generative AI, a suite of tools within Google Cloud designed for constructing and deploying generative AI models. Attendees can anticipate an examination of the technical intricacies involved in both platforms, offering insights into the possibilities of both systems’ power together. The focus will be on empowering users with the knowledge of how monitoring, analyzing, and optimizing tools can help enhance generative AI models in real-world production settings.

Led by Merlin Yamssi, lead solutions consultant at Google Cloud’s AI/ML CoE partner engineering, and Mike Villiger, senior manager of technical alliances at Dynatrace, this partner session at Perform 2024 will explore Site Reliability Engineering (SRE) and the utilization of the Four Golden Signals for AI Observability. Attendees will gain valuable insight on how Dynatrace observability can complement key Google Cloud AI tools like Duet AI and Vertex AI through its Google Cloud integration and automated discovery processes. Learn more about enhancing system reliability by proactively detecting issues and understanding the capability of Dynatrace and Google Cloud in generative AI models and observability.

Learn more about Dynatrace and GCP from the ebook 5 Key Considerations for Monitoring Google Cloud.

From Las Vegas to the enterprise cloud

The learnings from Perform will be as vast and robust as the enterprise cloud itself and illuminate how Dynatrace is the (not so) secret weapon to accelerating your cloud modernization journey. Beyond the three breakout sessions from our cloud partners, as an attendee, you can also visit them in our expo hall and talk through unique challenges and use cases to further empower you to supercharge your own digital transformation in the cloud.

Don’t miss your chance to learn from and meet with these cloud powerhouses at Dynatrace Perform 2024. Register now to attend in person (or virtually). We’ll see you in Las Vegas and follow you to the cloud!

The post Generative AI model observability, cloud modernization take center stage with partners at Dynatrace Perform 2024 appeared first on Dynatrace news.

Transforming an application from monolith to microservices-based architecture can be daunting, and knowing where to start can be difficult. Because monolithic applications combine database, client-side interfaces, and server-side application elements in a single executable, they’re difficult to understand, even for their own administrators.

Today’s customer expectations can’t tolerate tightly coupled dependencies, difficulties deploying changes, or long release cycles. Unsurprisingly, organizations are breaking away from monolithic architectures and moving toward event-driven microservices. However, the move to microservices comes with its own challenges and complexities.

Limits of a lift-and-shift approach

A traditional lift-and-shift approach, where teams migrate a monolithic application directly onto hardware hosted in the cloud, may seem like the logical first step toward application transformation. However, it’s common that teams need to refactor an application to help with performance after the lift-and-shift operation. It is better to consider refactoring as part of the application transformation process before migrating, if possible.

Although lifting and shifting to the cloud may provide some cost advantages at the outset, applications refactored for a microservices architecture can take full advantage of a cloud-first approach and provide cost savings in the long run. Microservices applications comprise independent services that teams develop, deploy, and maintain separately. Because they’re separate, they allow for faster release cycles, greater scalability, and the flexibility to test new methodologies and technologies.

However, the distributed system of a microservices architecture comes with its own cost: increased application complexity and convoluted testing. Migration is time-consuming and involved. Likewise, refactoring and rewriting code takes a lot of time and effort. Therefore, it’s important to do it right.

In the past, we’ve covered various topics on how to break the monolith, define monolithic architecture, and identify the advantages and disadvantages of microservices. Since the ultimate goal is to migrate to the cloud and refactor applications to a microservices architecture, here are 10 tips for getting started on the path to a successful migration.

10 tips for migrating from monolith to microservices

1. Understand the monolith

Monolithic applications take work to understand. In fact, it can be difficult to make code changes that won’t disrupt the entire system. This is also true of splitting the application into microservices. It’s important not to disrupt a running application and user experience. Start by evaluating the monolithic application components to understand all the dependencies and their business functions. Teams can gain this understanding through topology mapping, with telemetry data from request traces, and understanding how the frontend ties to backend functions. While this sounds simple, teams often get stuck trying to map out dependencies accurately when they try to do it manually. Automatic discovery and intelligent observability are the keys to overcoming this hurdle.

2. Find the right candidates for refactoring

There will inevitably be parts of a monolith application that teams can’t fully understand. When it comes to refactoring, teams should start with what they can understand. Components that are already loosely coupled and have few dependencies will be easier to migrate, with less chance of impacting application performance.

Use domain-driven design when creating new microservices by separating microservices via their underlying business functions. This allows individual teams to own components and makes it easier to pinpoint problems with mission-critical functions. Start with components that have high business value, such as a webpage checkout action.

3. Incrementally refactor

The saying “Rome wasn’t built in a day” rings true when it comes to refactoring microservices. It is important to remember that refactoring is essentially re-architecting and rebuilding your application. This is something that will take time. The best approach is incremental, using the Strangler Fig pattern: Gradually replacing parts of the monolithic application until only the microservices architecture remains.

The approach takes place in three stages: 1. create a microservice; 2. use both the microservice and monolith for the same functionality; 3. remove the dependency on the monolith after all testing is successful.

4. Ensure the microservices architecture is loosely coupled

Monolithic applications are traditionally tightly coupled, meaning dependencies among services within the app are intertwined. Because teams often can’t know or understand all dependencies, it can be difficult for them to make changes. When creating new microservices, it is better to keep them loosely coupled with minimal dependencies to allow for flexible changes and ease of deployment. One way to minimize dependencies is to use asynchronous messaging and message queues wherever possible.

5. Choose the right technology for each service

One advantage of using a microservices architecture is being able to choose different technologies for the application function at hand. Microservices can operate independently of each other. As a result, teams can leverage a polyglot architecture that uses the language and technology best suited for the job. However, there can be drawbacks to using too many different languages and technologies. It may make sense to use fewer languages depending on team bandwidth, capabilities, and size.

6. Instrument for end-to-end observability

One challenge that comes with migrating to microservices from a monolithic architecture is an increase in application complexity. Many microservices and different supporting technologies run independently of each other to support the application. With so many different components, it is easy to lose track of what is happening in which component when potential problems arise. End-to-end observability starts with tracking logs, metrics, and traces of all the components, providing a better understanding of service relationships and application dependencies.

This visibility is essential to understanding if the application is performing well and identifying where problems arise. Visibility is also the key to remediating problems and implementing automation.

There are many ways to instrument microservices for observability, including automatic instrumentation using a unified observability and security platform. Many organizations also find it useful to use an open source observability tool, such as OpenTelemetry. An observability platform approach makes it easy to combine both methods. Such an approach also considers additional important details, such as business impact and security implications.

7. Factor in security at every stage

With the increasing number and sophistication of security breaches, such as Log4Shell, integrating security into all application changes is more important than ever. This includes when teams refactor applications for microservices architecture. In fact, security risks differ among microservices. Security should be an integral part of each stage of the software delivery lifecycle, from development to monitoring in real time. Real-time security monitoring—evaluating continuously updated security data about systems, processes, and events—and runtime security monitoring—analyzing security information from a running system—can be particularly useful during migration since there will be an immediate alert if there is a security risk with this new microservices architecture.

8. Monitor the application before, during, and after migration

Migrating and changing code can be a tricky business. To ensure that the migration doesn’t affect user experience, teams should monitor application performance before, during, and after migration. Use SLAs, SLOs, and SLIs as performance benchmarks for newly migrated microservices. Repeat this process throughout the different environments before development, staging, release, and production. As each service migration is complete, continuously validate the existing code base as the team releases new code. Intelligent dependency mapping and automated baselining can help easily identify performance degradation and problems caused by new releases.

9. Automate wherever possible

Creating a system and a flow that teams can replicate and automate is a desirable objective. An automated CI/CD pipeline allows for an extremely smooth release process, accomplishing one of the goals of migrating to microservices. Every step of the way, define checks based on SLAs, SLOs, SLIs, and security scans, and automate the transitions from continuous integration, delivery, and deployment. teams can even build auto-remediation into a CI/CD pipeline, so if a problem arises, the system can trigger a fix or roll back to a previous version.

10. Optimize performance and user experience using observability data

Once the application has successfully migrated, it’s time to take advantage of all the newfound benefits a microservices architecture offers. Deploy changes fast to optimize performance and eliminate bottlenecks. Continuously update, improve, and easily add new features to provide an exceptional user experience. Utilize observability data to monitor and improve digital experiences and analyze data that can affect the business.

A unified observability and security platform approach to migrating from monolith to microservices

Migrating from monolith to microservices can be difficult, complex, and time consuming. However, making this architecture change is often the best way to take advantage of the agility of the cloud. For a simplified and smooth migration, teams need a way to assess a monolithic application’s starting state. They then need to track progress during migration to identify any drops in application performance.

As an AI-driven, unified observability and security platform, Dynatrace uses topology and dependency mapping and artificial intelligence to automatically identify all entities and their dependencies. This comprehensive view helps teams gain an initial understanding of a monolithic application so they can develop a migration strategy.

Once teams start introducing microservices, unified observability enables teams to visualize changes and the real-time performance of all the services in context. This AI platform approach automatically discovers all entities and identifies any issues developing among them. The observability extends to on-premises environments, Kubernetes infrastructure, multicloud platforms, and the multitude of proprietary and open source tools they depend on.

Keeping track of the migration stages, phases, and environments is not always easy. With real-time observability, teams can easily plan their migration and fine-tune performance as they migrate microservices. Since core observability with Dynatrace includes logs, traces, metrics, security, and user experience, teams can make decisions using these details in context.

After migration, observability continues to help teams monitor and optimize their microservices, resulting in better user experiences and, as an extension, better business outcomes.

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As global warming advances, growing IT carbon footprints are pushing energy-efficient computing to the top of many organizations’ priority lists. Energy efficiency is a key reason why organizations are migrating workloads from energy-intensive on-premises environments to more efficient cloud platforms. But while moving workloads to the cloud brings overall carbon emissions down, the cloud computing carbon footprint itself is growing.

“The cloud now has a greater carbon footprint than the airline industry,” wrote anthropologist Steven Gonzalez Monserrate in a 2022 article from MIT. “A single data center can consume the equivalent electricity of 50,000 homes.” The growing adoption of innovations like generative AI, based on large-language models (LLMs), will only increase demand for cloud computing. This adoption will further impact carbon emissions. Research from 2020 suggests that training a single LLM generates around 300,000 kg of carbon dioxide emissions—equal to 125 round-trip flights from New York to London.

Does that mean the answer is to slow the growth of AI or cloud technologies more broadly? Given the benefits of these innovations, organizations can’t afford to pull back on their efforts to build AI and shift more workloads to the cloud. However, organizations can turn to innovative solutions to improve their energy efficiency by mitigating their cloud computing carbon footprint.

How Dynatrace tracks and mitigates its own IT carbon footprint

Like many tech companies, Dynatrace is experiencing increased demand for its SaaS-based Dynatrace platform, which we host on cloud infrastructure. As we onboard more customers, the platform requires more infrastructure, leading to increased carbon emissions. At the same time, many existing customers are migrating from Dynatrace Managed, our on-premises solution, to our SaaS offering. These migrations add to the Dynatrace cloud computing carbon footprint as we onboard more customers’ observability and security workloads. However, since moving on-premises workloads to the cloud can lower the overall carbon footprint by 80% or more, the result is a net reduction in carbon emissions.

Nonetheless, to help mitigate climate change, it’s critically important for organizations to measure, monitor, and reduce their IT carbon footprints. Certainly, this is true for us. We also recognize that many of our customers have the same need. Many cloud service providers offer tools that measure a subscriber’s cloud computing carbon footprint when using their service. But they don’t measure the carbon footprint of the many apps and infrastructure resources running across that subscriber’s multicloud environments. They also can’t assess the IT carbon footprint of a subscriber’s on-premises apps and infrastructure. That’s why we developed Carbon Impact.

The Carbon Impact app assesses carbon emissions and energy consumption from all monitored hosts. It also provides organizations with actionable guidance for how to reduce their overall IT carbon footprint. Developed using guidance from the Sustainable Digital Infrastructure Alliance (SDIA) and expanding on formulas from the open source project Cloud Carbon Footprint, Carbon Impact measures and reports the IT carbon footprint of all Dynatrace-monitored hosts across an organization’s entire hybrid and multicloud environment in a single interface.

Assessing our baseline cloud computing carbon footprint

Using Carbon Impact, we can assess our baseline carbon footprint with accuracy and granularity that’s nearly impossible to glean from other sources. The app’s advanced algorithms and real-time data analytics translate utilization metrics into their CO2 equivalent (CO2e). These metrics include CPU, memory, disk, and network I/O. This analysis provides us with a holistic view of our multicloud environment’s carbon emissions and identifies major emissions sources. As a result, this baseline measurement has become an important component of our sustainability strategy. It increases our awareness across IT and business stakeholders as we use these insights to build action plans to reduce our emissions and track the results of those efforts.

Tracking cloud computing carbon footprint by host

The ‘Hosts’ view details energy and CO2e consumption per host with filters to help narrow the focus to high-impact areas. For example, Dynatrace has been able to view underutilized instances in a specific AWS data center along with top CO2e emitters within a specific host group.

Optimizing host idling and scaling to reduce IT carbon footprint

Carbon Impact automatically reports idle and under-utilized instances as targets for optimization. Because Carbon Impact is integrated with Dynatrace Smartscape® topology modeling, it’s easy to drill into host and process details. Or open a Notebook for ad hoc analysis, giving us insights so we can safely scale down or retire underutilized instances. Using these recommendations, we focused our reduction goals on instances with the highest potential impact, shifting workloads and resizing instances where appropriate.

Helping organizations track their IT carbon footprint to forge a greener future

At Dynatrace, we’re committed to measuring and reducing our own greenhouse gas emissions. By extension, we want to enable our customers to do the same. The Dynatrace unified observability and security platform makes this possible. Carbon Impact is an example of our contribution to making IT more energy-efficient and sustainable for everyone, even as AI is fueling the data explosion.

We built Carbon Impact using Dynatrace AppEngine, which customers and partners can also use to create additional custom, compliant, and intelligent data-driven apps. The AppEngine uses an easy, low-code approach to unlock the wealth of insights available in modern cloud ecosystems, including revealing where organizations consume their energy.

”As Dynatrace looks to the future, considering our environmental impact is more important than ever,” says Thomas Reisenbichler, VP of Site Reliability Engineering at Dynatrace. “We’re confident that Dynatrace will be able to optimize our cloud infrastructure carbon emissions by leveraging the Dynatrace platform for proactive management and orchestration, utilizing our Carbon Impact app to unlock greater optimization potential, and using green coding initiatives to improve performance.”

Using Carbon Impact, we can now implement efficiency measures driven by the app’s benchmarks and recommendations. Because it facilitates ongoing monitoring and tracks progress toward our sustainability goals, we can adjust our strategy to reduce our IT carbon footprint. By integrating sustainability into our growth strategy—and our product offering—Dynatrace is deepening its commitment to responsible business practices, transparency, and a greener future.

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Organizations in every industry are engaged in some form of digital transformation, integrating technology into all areas of the business. Digital transformation strategies are fundamentally changing how organizations operate and deliver value to customers. Some of the benefits organizations seek from digital transformation journeys include the following:

• Increased DevOps automation and efficiency. Digital tools and technologies provide a more efficient way of doing things. They help organizations streamline and automate complex and time-consuming procedures and improve overall performance.
• Improved customer experience. Consumers expect personalized, proactive, and convenient service, which traditional application architectures often struggle to provide.
• Competitive advantage. A comprehensive digital transformation strategy can help organizations better understand the market, reach customers more effectively, and respond to changing demand more quickly.
• Enhanced business operations. Digitizing internal processes can improve information flow and enhance collaboration among employees. With teams all using the same data, it’s easier for the organization to make data-driven decisions.

However, digital transformation requires significant investment in technology infrastructure and processes. It often involves replacing legacy systems and workflows that have been in place for years or even decades. Because it’s an ongoing process, digital transformation requires that teams continually adapt and evolve as new technologies emerge and customer expectations change. As a result, a successful digital transformation strategy requires that organizations make changes to organizational culture and provide employee training.

In a panel discussion at Perform 2023, Debbie Umbach, vice president of corporate marketing at Dynatrace, explored the successful digital transformation strategies of three Dynatrace customers: Best Buy, a multinational consumer electronics retailer; a top global retail and corporate finance service provider in the banking industry; and the Department of Veterans Affairs (VA), the second largest U.S. federal agency.

Customer Panel: Digital Transformation

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Digital transformation challenges: Goals for a successful digital transformation journey

Every organization faces unique challenges. Finding the solutions to these challenges becomes the goals for each panelist’s successful digital transformation strategy.

The global banking leader is seeking better ways to detect and manage alerts from multiple sources, improve platform resilience, and to better understand the business cost of a technical failure.

The goals of VA are to streamline operations and become more proactive so they can head off problems before their users find them, thus creating delightful user experiences for their diverse community.

Best Buy is designing its journey to cut through the noise of its multicloud and multi-tool environments to immediately pinpoint the root causes of issues during peak traffic loads.

Digital transformation security risks

Increasingly, observability of DevOps workflows is converging with application security. The proof point for one organization is Log4Shell, a critical zero-day vulnerability discovered in a popular Java library in 2021.

With Dynatrace Application Security, VA was able to immediately detect whether the vulnerability was present in any of its systems. Additionally, contextualized real-time dependency mapping and automatic analysis enabled them to quickly assess the risk and take action to protect user data and critical infrastructure.

Digital transformation success stories: Operational benefits

In addition to immediate threat response, VA teams were also able to unlock more automation and reduce mean time to repair (MTTR) so they could resolve issues before they affected users or compromised systems.

Best Buy automates service level objectives (SLOs) for milestones such as staging gates and version control, which enables them to streamline DevOps workflows and cut red tape.

An additional game changer for Best Buy is Dynatrace anomaly detection and tool consolidation. Previously, they had 12 tools with different traffic thresholds. With Dynatrace, they now have a single unified platform that measures all the thresholds looking for differences, which makes it easy to find a performance spike in a torrent of data from different sources.

Advice for a successful digital transformation strategy

In the process of their digital transformation journeys, the panelists learned some lessons they wanted to pass on. After getting the right observability and analytics platform in place, the primary key to success is enabling teams to access it en masse. Their advice includes the following practices:

• Get technical and platform teams on board. With the key players advocating organizational change, teams can adopt new technologies and methods faster.
• Enable teams with ongoing education so they get the most out of Dynatrace capabilities. This keeps teams from falling into old habits and enables them to continually innovate and expand.
• Turn off the tools you no longer need. Although teams often get attached to old tools and methods, it’s easier, in the long run, to stop using them.
• Take advantage of Dynatrace University. The 3–5-minute videos make it easy for teams to quickly learn how to do things at their own pace as they’re solving problems.
• Don’t let perfect get in the way of good. Whether it’s cloud migration or monitoring, don’t be afraid to try something. It’s better to get some traction and fail fast, then recover and continue to build.

Watch the full session to learn more about how these organizations use Dynatrace to execute their digital transformation strategies. Customer panel: Successful digital transformation journeys

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“As we continue to accelerate our digital transformation, access to best-in-class solutions like Dynatrace and AWS are critical to our success,” said Alex Hibbitt, Group SRE Director at albelli-Photobox Group. “Extending Dynatrace’s AI-powered insights and automation to AWS Compute Optimizer will help to further enhance our digital services. It also enables us to maximize the impact of our resources and technology investments, fueling innovation and driving measurable impact on our company’s bottom line.”  

“Dynatrace is proud to work with AWS to help customers modernize and automate cloud operations,” said Bob Wambach, Vice President of Product Marketing at Dynatrace. “Extending our advanced observability, AIOps, and automation capabilities to power AWS Compute Optimizer enables our joint customers to prevent performance issues, avoid costly over-provisioning, and operate more efficiently so they can focus on what matters most – accelerating innovation and delivering exceptional digital experiences.” 

“To achieve an elastic, well-architected cloud environment, organizations must be able to optimize their usage of cloud resources,” said Rick Ochs, Principal Product Manager of Optimizations at AWS. “With the Dynatrace platform’s support of AWS Compute Optimizer, we provide our customers with memory-aware rightsizing so they can increase operational efficiency and get the most out of AWS.” 

Dynatrace’s service is generally available today. Please visit the Dynatrace blog for additional details.

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“The cloud and Dynatrace have transformed the way our business operates and how our teams work in this modern era,” said Christian Palomino, Vice President of Global IT, Meliá Hotels International. “Before Dynatrace, we used to spend hours manually searching through metrics, logs, and traces to piece together insights about user experience. Now, this takes minutes or seconds. If guests experience a problem using any of our digital services, our contact center teams know precisely what’s causing the issue and are empowered to provide faster, more personalized resolutions, and ultimately deliver a greater standard of care. This has enabled our teams to focus more time on driving business and customer value, and to ensure our ongoing success during what has been a challenging time in our industry.”

With Dynatrace, Meliá’s teams are rededicating their focus to optimizing digital services and finding new ways to accommodate the rapidly evolving preferences of the modern traveler. This has helped the hotel chain reduce the reliance on in-person interactions, which has led to an increase in the volume of transactions handled through its digital channels from around 40% at the end of 2019, to more than 80% during the pandemic, which was a major asset through those difficult times.

“We developed our Stay Safe with Meliá Program to achieve our goal of maintaining frictionless relationships between staff and guests, while also reducing in-person contact,” continued Palomino. “Dynatrace has been critical to this effort, enabling our teams to accelerate the delivery of new digital services that allow our guests to do things like check-in or book a table in our restaurants via our mobile app, reducing the need for person-to-person contact across our hotels. Dynatrace delivers the precise, AI-powered insights we need to understand exactly how our customers interact with our applications, and how their experiences impact our business. This has helped our teams discover where our guests are struggling, and what we need to do to improve our digital services, so they have a great experience and can fully relax during their stay with us.”

Visit our Customer Stories page for more details on how Meliá is accelerating digital innovation and delivering frictionless guest experiences across its hotels with Dynatrace.

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As organizations expand their cloud footprints, they are combining public, private, and on-premises infrastructures. But modern cloud infrastructure is large, complex, and dynamic — and over time, this cloud complexity can impede innovation. As operational complexity increases exponentially, conventional tools and operations approaches hit a cloud observability wall.

This typically happens when organizations graduate from lift-and-shift cloud migrations and begin creating truly cloud-native applications. Organizations hit this cloud operations wall when replacing static virtual machines with dynamic container orchestration and expanding to multicloud environments. However, shifting from reactive to proactive cloud operations helps organizations stay ahead of the game.

At Dynatrace Perform, David Catanoso, acting director of cloud and edge solutions at the U.S. Department of Veterans Affairs (VA), explains how Dynatrace not only solves this cloud complexity, but gives his team confidence they can fulfill VA’s future cloud requirements.

Joining Catanoso are Dynatrace’s Peter Putz, senior technical marketing manager, and Michael Kopp, director of product management. Putz and Kopp share insight into the cloud operations challenges facing organizations today and how teams can overcome these obstacles to become more proactive and transform the way they work.

VA’s journey into the cloud

When the federal CIO Cloud-First Initiative came out in 2011, VA decided to start migrating applications to the cloud. “First of all, we wanted to provide better service to our veterans,” Catanoso says. “Second, we wanted to improve our rollout of the DevSecOps capability, as well as improve our agility and our ability to innovate faster.”

VA’s cloud journey was a long one. The agency executed one of the largest email migrations from on-premises Exchange servers to Microsoft Office 365 — moving almost 480,000 mailboxes to the cloud.

“A lot of consultants will tell you to move some small applications first and then get some experience. And then, at some point, you can move some big, complicated applications,” Catanoso says. “We kind of did the opposite. Right out of the gate, we moved some of our biggest, most mission-critical applications to the cloud.”

Despite many challenges, when the project concluded in 2019, the team had the confidence and experience to migrate anything to the cloud.

How Dynatrace solved VA’s cloud complexity

Today, VA uses Dynatrace to monitor over 150 different cloud instances — even hybrid instances of applications.

Dynatrace addresses several of VA’s cloud complexity challenges, such as finding and fixing telework bottlenecks. When the COVID-19 pandemic hit, VA’s remote workforce grew by over 100,000 people almost overnight, creating a new series of problems to quickly diagnose and solve.

As a result, VA had to rapidly scale its on-premises Citrix environment. “The team did a two-part attack on that, where we rapidly added more physical infrastructure, but also expanded the Citrix environment into all five CSP regions that we had available to us in the government clouds from Azure and AWS,” Catanoso explains. “We used Dynatrace to monitor that large increase in servers. We started out by instrumenting 2,000 servers overnight. In 48 hours, we had a total of 6,500 servers monitored.”

Since then, VA solved many cloud performance problems and kept its complex hybrid environment running without interrupting remote work. Dynatrace’s advanced monitoring capability enabled VA to get through the pandemic successfully while providing services to the nation’s veterans.

VA also used Dynatrace to instrument its Consolidated Mail Outpatient Pharmacy Application, a mission-critical app with a heavily distributed database hosted in seven locations across the U.S. In the past, severe database issues seriously hurt system performance, causing delayed shipping times for prescriptions.

“That team installed Dynatrace to monitor its applications across that environment in preparation, wanting to understand the application today and its current utilization,” Catanoso says. “This enabled us to fundamentally improve the application, remove performance bottlenecks, and also give us the data we needed to understand how to migrate into the cloud.”

Foundational observability paves the way for proactive cloud operations

While modern cloud systems simplify tasks — such as deploying apps and provisioning new hardware and servers — cloud environments can be surprisingly complex.

Kopp explains how Dynatrace brings all the observability data into context and automatically derives its Smartscape topology using signals — such as logs, events, metrics, and application traces. This allows customers to extend the topology from there as they see fit.

Comprehensive observability across all cloud environments is key. “Your solution needs to give you the tools to monitor all of these different clouds with ease,” Kopp says. “We recently made great strides to improve our analysis views to give our customers even more value.”

This allows Dynatrace to present relevant data, in context, for applications and operations — delivering the best observability possible.

How Davis delivers proactive cloud operations

To reach truly proactive cloud operations, you need AI. Davis, Dynatrace’s explainable AI, offers domain-specific answers to cloud-native problems and provides the necessary context to understand how it made that determination.

Davis can also detect problems before they affect your organization. It provides automatic threshold models, so you don’t have to set them yourself. On top of that, Dynatrace has structured models, such as seasonal, trends, and autoregressive component models.

“Tying it all together is the topology that is automatically detected and updated all the time,” Kopp explains. “This allows for a causation reasoning engine that isn’t prone to false positives and offers analysis you can easily understand.”

Dynatrace is also adding forecasting alerts that will allow Davis to detect problems before users notice the impact. This enables a site reliability engineering approach to cloud operations, in which organizations improve reliability using service-level objectives (SLOs) and error budgets. For example, you can use an SLO to define quality gates that trigger alerts when system health is expected to degrade.

“We have a rich metric expression language. This allows you to define any SLO you can imagine based on data that’s in the system,” Kopp says. “This facilitates what’s known as configuration as code or monitoring as code. And it allows you to shift left, giving responsibility for things to the application owners as opposed to the ops team — which is typically overburdened with work.” Dynatrace will soon make it possible to bring SLOs back into the context of observability and AI.

Modernize cloud operations with Dynatrace

Dynatrace customers, such as SAP and Kroger, are already achieving proactive cloud operations and delivering unparalleled value across stakeholders. Development teams can innovate faster with higher quality. Operations teams can run more efficiently. And the organization can consistently drive better outcomes.

To learn more, check out the session, “Modernize your cloud operations from reactive to proactive.”

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Other benefits include faster software innovation, continuously improved user experiences, and increased operational efficiency, achieved with automatic and intelligent observability.

We are excited to share the results of a commissioned study conducted by Forrester Consulting on the Total Economic Impact (TEI) of the Dynatrace® platform. The study examines the potential return on investment (ROI) and business benefits organizations may realize by deploying Dynatrace. Through customer interviews and financial analysis, Forrester found that a composite organization will experience benefits of $20.16 million and a 274% ROI over three years, with a payback period of under six months.

Here’s a look at what interviewed customers had to say about Dynatrace:

• “Before, we could wait a year for a new feature. Now, we can have it within a month. Dynatrace gives us more releases, and more confidence in those releases.” – Chief Cloud and Delivery Officer, Financial Services
• “The big difference in Dynatrace, and the selling point for me was the intelligence, root-cause analysis, and that Dynatrace is automatically analyzing the whole stack.” – Director of IT Operations, Healthcare Technology
• “Other tools lack the AI that Dynatrace has. There are times [when] Dynatrace catches issues before other systems do. Dynatrace helps other tools work better by showing them the right direction.” – Chief Cloud and Delivery Officer, Financial Services
• “It just makes sense that Dynatrace monitors your security for you since it already has all the information. If [Dynatrace] can apply [its Davis AI] with the same capabilities in terms of security, that’s a huge benefit.” – Head of Tech, Healthcare
• “We do more and more with Dynatrace because it minimizes our effort. We build it into pipelines and test automation, and that automation gets us to a position where our services auto-heal.” – Head of Tech, Healthcare

The study identified a range of key benefits of the Dynatrace® platform, including:

• Faster software innovation. Dynatrace enabled a 40% increase in DevOps efficiency by significantly reducing the time required to successfully deploy applications and updates. Over three years, this provides more than $5.2 million in value.
• More efficient operations of modern-cloud workloads and infrastructure. Interviewees described rising complexity across their cloud environments, prior to investing in Dynatrace. The deep observability and advanced AIOps brought by the Dynatrace platform increased operational efficiency and reduced false-positive alerts by 95%, resulting in an additional value of more than $1.8 million over the three-year period.
• Consistently better business outcomes. Dynatrace enabled teams to provide a better experience for customers, which increased both retention rates and net promoter scores. This resulted in a 4% reduction in customer churn rate, providing an additional value of nearly $4.9  million over three years.

Dynatrace Chief Marketing Officer Mike Maciag shared his perspective on the research, noting: “Organizations are facing pressure to digitally transform faster than ever before, but without an intelligent solution, providing end-to-end observability, precise insights, and continuous automation, it’s impossible to keep up. We believe this research affirms the breadth of capabilities, use cases, and industries the Dynatrace platform supports. It also underlines Dynatrace’s ability to enable the world’s largest organizations to overcome the biggest hurdles to innovation and give teams time back to focus on driving their transformation faster, and with greater confidence.”

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