This edition of Release Radar covers the Dynatrace releases from May, 2026. Here are the changes that should matter right away to practitioners:

• AI tooling
• AI coding agent monitoring
• Jira integration
• Dashboard productivity
• Pipeline grouping

To see them in action, head over to our release radar launchpad on the Dynatrace Playground.

Dynatrace Assist: At your side with more context

Dynatrace Assist gets four upgrades in sprint 1.338 that deepen its usefulness during active investigations.

Side-by-side mode puts the chat interface in a collapsible panel alongside your current view — dashboard, notebook, or any other app page stays visible while you work with Assist. Chat and investigate at the same time without losing your place.

Reference files and skills give Assist access to a curated knowledge base built on Dynatrace documentation and product expertise, following the Anthropic Claude Agent Skills format. Responses are designed to draw on structured Dynatrace knowledge, so answers are grounded in how the platform actually works.

Anthropic Claude Sonnet 4.6 as the foundation model can help bring stronger multi-step reasoning and improved performance on complex, multi-tool investigations — the same model used in the latest Anthropic API and Claude Code.

A purpose-built NL2DQL model makes natural-language-to-DQL generation more accurate. The capability now uses a fine-tuned foundation model based on Llama 3.1 8B, trained specifically on Dynatrace query patterns. Write a question in plain language and get a working DQL query with fewer iterations.

AI coding agents get unified monitoring

Dynatrace now provides observability for five major AI coding agents: Claude Code, Google Gemini CLI, OpenAI Codex CLI, OpenCode, and GitHub Copilot SDK.

As  your team adopts multiple AI agents in parallel, you need shared visibility into what they cost, how they behave, and what they produce. This release gives platform teams, engineering leaders, and security teams a  unified observability across all five agents, all built on OpenTelemetry.

What teams get across the supported agents:

• Adoption and token tracking — session counts, token consumption, and cost trends across agents and teams.
• Tool behavior visibility — which tools each agent calls, how often, and where runs slow down or fail.
• Production context in the IDE — engineers can query live Dynatrace data through the Dynatrace MCP Server without leaving their coding environment.
• Engineering outcome correlation — connect agent activity to downstream delivery signals like commits and pull requests (available for Claude Code).

Pre-configured dashboards are available for each agent. For the full breakdown by agent and setup details, see Dynatrace expands AI coding agent monitoring.

Investigate production problems without leaving Jira

The Dynatrace MCP Server now integrates with Atlassian Rovo, bringing observability context directly into Jira and JSM tickets.

When an incident or issue is open in Jira or JSM, Rovo can now call Dynatrace tools in natural language — querying metrics, traces, logs, and topology — and post the results as ticket comments. Root cause analysis and dependency mapping happen inside the ticket, so engineers stay in context instead of switching between platforms.

Key points for practitioners:

• No context switching — investigate and document findings without leaving Jira or JSM.
• Per-user OAuth 2.1 — every Dynatrace call runs as the requesting user, with a full audit trail across both platforms.
• Admin-controlled tool exposure — administrators choose which Dynatrace tools Rovo can access.
• Included with Dynatrace SaaS — no additional cost, and adding users doesn’t change the pricing.

It’s designed for fast setup: authenticate via the Rovo admin UI, select the tools to expose, and the integration is live across Jira, JSM, and Confluence.

For the full walkthrough, see Dynatrace MCP Server for Atlassian Rovo.

Dashboards: build faster, navigate better

Releases 1.338 and 1.339 bring a focused set of dashboard improvements that add up across a day of analysis work.

Ready-made tiles and sections expand the dashboard and notebook library with pre-configured visualizations and built-in drill-downs to other Dynatrace apps. Browse or search the tile library to find components that are ready to use, and customize from there rather than starting from a blank canvas.

Direct JSON editing lets power users open and edit the full dashboard definition as JSON from the dashboard Actions menu. The format matches the Dashboard API, so configuration changes, bulk tile edits, and version-controlled workflows are all faster in the editor than in the visual UI.

URL-driven variables make it possible to configure hidden dashboard variables through URL parameters, enabling pre-configured views to be linked directly with filters already applied — useful for sharing context-specific dashboards with specific teams or stakeholders.

Launcher link reordering lets users drag links between sections in the launcher, making personal navigation layouts easy to maintain.

Active tile tab persistence keeps the last-active editing tab visible when switching between tiles, so the configuration state is preserved while navigating across a dashboard.

Pipeline groups get a configuration UI

Pipeline groups, which let central teams enforce shared policies across multiple OpenPipeline pipelines, now have a dedicated configuration interface in Early Access (sprint 1.339).

Platform teams can now configure group-level policies in the UI, including cost allocation and sensitive data scanning. Pipeline teams still control their own parsing and extraction logic, so central governance doesn’t come at the cost of local flexibility. No direct API access required.

This UI makes the feature accessible to a wider set of platform operators and can help reduce setup costs for organizations running large, multi-team pipeline environments.

For background on pipeline groups and the governance model they enable, see Pipeline Groups in Dynatrace OpenPipeline.

Latest UX improvements

Investigation workflows in logs get sharper. Join the Log pattern analysis preview and see how selected log patterns now open a dedicated deep-dive panel showing behavior over time and associated records — inspect pattern-level context without losing the overview, and drill into individual log records from the same panel without navigating away. Log attributes open in full-screen mode for reading long or nested JSON in place. When Logs is opened from a contextual link in a dashboard or alert, the query runs automatically — no extra click required.

Navigation, filtering, and performance also improve across several surfaces. In the Session List, a tooltip on the Session Replay icon shows replay availability — full, partial, or none — so you can triage which sessions have usable replay before opening them. The Services explorer gains dynamic tag filters for Kubernetes namespace annotations and labels, expanding filter coverage for k8s-heavy environments. Infrastructure inventory in large network environments now loads in 3–5 seconds in typical environments, with status and reachability columns loading progressively so the view is immediately usable at scale. Press Shift + ? anywhere in the platform to open the keyboard shortcut reference.

Why these changes matter

The May releases extend capabilities across the workflows that practitioners use most. AI tooling stays present during investigations. Coding agents become observable assets, not black boxes. Dashboards get faster to build and easier to manage. Cost data supports annual planning. And pipeline governance reaches more teams through a UI.

These are the kinds of changes that add up across a week of real work.

Check out the updates in action on our Release Radar Launchpad.

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The business observability gap in cloud-based transactions

Business observability, at its best, delivers real-time insights into business health, reporting, and interpretation of the metrics and KPIs that business and IT leaders need to make decisive and effective decisions.

But how can you access this data when cloud-based solutions deliver your critical services? One of the more common examples is payment gateways, or payment service providers (PSPs)—hosted services that verify account details, check for available funds, and transfer money into your business merchant account.

There’s a wealth of business data in your systems’ transaction requests to these PSPs and other SaaS solutions. Without visibility into this data, you’re left with an incomplete and delayed understanding of transaction success, sales data, and business health. This is particularly problematic since the transaction success or failure status is often embedded within the response body.

Here are some of the challenges our customers have identified:

• Delays in resolving issues. Failed transactions are only discovered in scheduled reports or when customers complain.
• Reconciliation discrepancies. Inconsistencies between internal records and PSP reports lead to frustrating, time-consuming, and error-prone manual work.
• Inability to understand short-term trends. Identifying failure patterns for specific providers or transaction types is nearly impossible without timely and detailed transaction data.
• Compliance risks. Lack of granular detail makes auditing and demonstrating compliance with payment regulations more challenging.

Business events from outgoing web requests

Dynatrace business events are in a special class designed to support even the most demanding business use cases. Whether derived from OneAgent, log files, RUM sessions, or APIs, business events deliver real-time lossless access to critical business data—wherever it lies. In particular, OneAgent’s unique capability to extract business data from in-flight application payloads—without writing any code—has proven invaluable to hundreds of our customers.

OneAgent now supports capturing business events from outgoing web requests that are specifically designed to illuminate these blind spots. You can now transform critical business information within your outgoing web requests and their corresponding responses into structured business events, which can then be analyzed, correlated, and acted upon within Dynatrace.

Use case: Business events and payment gateways

Here’s how it works, and what it means for our payment gateway use case.

Granular configuration for targeted capture

• Define endpoint-specific rules to capture data from requests to specific URLs or IP addresses (for example, api.stripe.com/payments or paypal.com/checkout).
• Define method-specific rules to capture data from relevant HTTP methods like POST or PUT that are used to initiate transactions.

Deep insight into request and response payloads

Support for business-event capture from outgoing web requests adds compelling new value and expands on current OneAgent capabilities for extracting business data from in-flight application payloads.

• Extract data from the request body. Imagine you’re sending payment requests that include customer account numbers, transaction amounts, and transaction identifiers. You can now define rules to extract these key pieces of information directly from your JSON or XML request bodies. This is crucial for linking your internal transaction IDs to the business data sent to the provider.
• Extract data from the response body. Potentially greater value lies in the response body. Often, PSPs return transaction status messages (for example, transactionStatus:success, authorizationResult:approved, or responseCode:1954) within the response body. Extracting these status messages allows you to report, alert, and act on failures or anomalies as they occur.

Transform raw data into meaningful business insights

Business data increases in value when enriched with business and IT attributes. For example, you could map extracted fields to meaningful attributes of a “Payment Transaction” business event. These attributes might include payment_provider_id, transaction_amount, and customer_id, extracted from the request body, and payment_status extracted from the response body.

These events are automatically enriched with Dynatrace observability data, such as the request’s originating host, service, user journey, or geographical source, facilitating business and IT collaboration.

Real-time visibility and proactive alerts

• Real-time business transaction monitoring. Know each PSP transaction’s success, failure, and value as it happens, not hours later.
• Custom dashboards. Dashboards make it easy to visualize different PSPs’ success rates and revenue, exposing bottlenecks and highlighting business and failure trends.
• Proactive alerting. Set up alerts based on real-time payment status messages. Trigger alerts on failed transactions over a certain amount, or when the success rate SLA degrades.

Enhanced traceability and root cause analysis

• End-to-end transaction tracing. Trace the entire lifecycle of a payment, from the user initiating the purchase through your internal services to the outgoing request to the PSP and the response status indicating success or failure.
• Accelerated root cause analysis. Quickly determine the source of payment transaction failures, bottlenecks, and anomalies to speed resolution and improve business outcomes.

Payment gateway example: From opaque to transparent

Here’s what this could mean for your organization:

1. A customer attempts a major purchase. Your e-commerce system sends the request to your PSP, but the transaction is declined.
2. OneAgent captures relevant business data from the outgoing request, including customer ID and transaction amount attributes. Dynatrace also captures the relevant response fields, including the status:declined message.
3. A “Payment Failure” business event is automatically generated in Dynatrace. This event triggers an alert to notify your operations and customer support teams.
4. Your customer support team can now proactively contact the customer while your operations team works to resolve the issue, knowing exactly why the transaction failed and which provider was involved.

This is possible without any code changes. Extracting business data from outgoing requests and responses turns opaque customer interactions into transparent, actionable business insights, empowering you to improve customer experience, optimize payment workflows, and reduce operational risk.

Initial support for outgoing web request capture is available for Java; see Supported Technologies for details. Additional technology support will be available soon.

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While Dynatrace and Microsoft Power BI offer significant value on their own, connecting the two can exponentially enhance the insights they unlock. Here, we explore how companies that use both platforms can benefit from the Microsoft Power BI Connector from Dynatrace.

The impact of Dynatrace and Microsoft Power BI

Dynatrace is the leading AI-powered observability and security platform. It helps organizations understand their business like never before through AI-driven analytics and automation and offers deep insights into IT and business health based on real-time data from logs, metrics, traces, and business events.

Microsoft Power BI provides interactive visualizations and business intelligence capabilities with an intuitive user interface. It allows end users to create reports and dashboards easily. It integrates seamlessly with various data sources and offers robust manipulation and visualization features to help organizations accelerate the time to value from business analytics.

The synergy of integration

Combining the analytical prowess of Microsoft Power BI with the comprehensive observability data in Dynatrace creates a powerful synergy. Key benefits of this integration include the following:

Complementary data visualizations

Dynatrace captures a wealth of data across critical IT and business metrics like application and infrastructure performance, user experience, security, and business health. Additionally, Dynatrace offers a rich and expanding set of dashboards, charting, and analytics capabilities.

With the Microsoft Power BI Connector, joint customers can now transform complex Dynatrace data sets into intuitive, interactive visualizations within Microsoft Power BI to maximize the value of their investment in both solutions.

Integrated reporting

Microsoft Power BI analysts often model information from various sources, including extra categorical dimensional data from local databases or spreadsheets. Now, joint customers can bring rich data from Dynatrace into Microsoft Power BI and model it alongside other data sources that would typically not go into an observability platform.

Microsoft Power BI analysts are accustomed to creating powerful reports from a variety of databases and Microsoft Excel sources. Now, joint customers can bring observability-driven insights directly into Microsoft Power BI and combine them with external data sources to provide an even richer view. The seamless integration offered by the Microsoft Power BI Connector allows analysts to delve deeper into the effects of software and infrastructure performance, application security, and user experience metrics on business outcomes. This, in turn, helps them to maximize the value of their investments in business intelligence and analytics.

Improved collaboration

The Microsoft Power BI Connector makes it easy for teams to collaborate on projects that are informed by integrated Dynatrace business and observability data. Therefore, teams across different business and technology departments can access and analyze the same performance metrics in a single platform, fostering a collaborative approach to problem-solving and decision-making. This shared understanding is crucial for aligning objectives and strategies across the organization, with cross-functional teams leveraging enormous amounts of observability data and business intelligence to achieve better outcomes.

Additionally, Dynatrace data observability capabilities can verify the quality and security of data, giving analysts a rich, trustworthy, and secure source of truth they can rely on for reporting.

How to implement the Connector

The Microsoft Power BI Connector allows Power BI to access data stored in Dynatrace Grail™ data lakehouse. Implementation follows a standard set of steps:

• Define the Dynatrace environment to be used as a data source. The environment’s user information will be authenticated, and Dynatrace Grail permissions will be verified.
• Run Dynatrace Query Language (DQL) queries to load data into Microsoft Power BI, or further transform the data using the Power Query editor.

Dynatrace uses the integration for cost analysis

The Dynatrace FinOps team uses the Microsoft Power BI Connector to derive deeper insights into our cloud costs, making it easier to report this information to senior-level executives.

The Dynatrace platform ingests details of our cloud provider costs along with infrastructure and application consumption data. The FinOps team combines this with dynamic categorical information stored in Microsoft Excel and relational models within a Microsoft Power BI model. Dynatrace provides all the necessary backend and scale, while Microsoft Power BI provides the reporting, and Microsoft Excel provides the required ingestion, ultimately providing the best of both worlds.

Apply data-driven decision making for success

The Microsoft Power BI Connector offers a significant advantage to organizations that prioritize data-driven decision-making. By leveraging the strengths of both platforms, analysts can achieve a comprehensive, real-time understanding of their IT ecosystem, enabling them to optimize performance, improve user experience, and drive strategic initiatives.

For those already using Dynatrace and Microsoft Power BI, the Connector is a logical step toward maximizing the value of their data assets and enhancing their operational efficiency.

Watch this Dynatrace Observability lab video for a closer look at the Microsoft Power BI Connector in action.

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Dynatrace Business Flow simplifies business process observability, connecting top-level process KPIs with detailed flow analytics. The app tracks the progress of every process instance, reporting individual and aggregated process performance, throughput, exceptions, and business outcomes.

With this update, Davis® AI can track and alert on KPI threshold violations to assure end-to-end process efficiency and reliability.

Anomalies are inevitable

Despite the deep IT observability you might have deployed, many of your business processes will likely encounter failures, intermittent delays, or ongoing inefficiencies. These issues can impact a wide range of business outcomes, from a direct reduction in revenue to a decrease in Net Promoter Score (NPS), from idle production lines to departmental budget overruns. Each business unit relies on a collection of processes, and each process has metrics and KPIs that can be affected by delays, exceptions, or failures. Examples include:

• A stalled connection between two services delays the process of validating food orders, resulting in dissatisfied customers, idle delivery drivers, and canceled orders.
• A failure in a third-party API breaks a loan validation process, reducing the number of applications approved and directly impacting the company’s revenue.
• A delay in warehouse order processing results in late deliveries, impacting customer experience.

Business process observability

Real-time business process observability can minimize the impact of these and other incidents by ensuring anomalies are detected quickly and include the IT context required for rapid—even automated—remediation. Business process observability is a new approach to ensuring process efficiency and reliability, quickly detecting and remediating anomalies, and identifying optimization opportunities. Are detected business exceptions specific to one host in a cluster? Which services does the credit check API depend on? Is there a fund transfer bottleneck at the source or destination bank?

Business process observability becomes increasingly important as new regulatory frameworks like the Digital Operational Resilience Act (DORA) impose strict operational resiliency requirements on financial and other institutions.

Dynatrace Business Flow

Business Flow is a Dynatrace® App that delivers on the promise of business process observability, connecting top-level process KPIs with supporting step- and instance-level analytics. Some of the questions Business Flow answers include:

• What is the average end-to-end process delay? How does this change over time?
• Are the business goals achieved? (For example, average loan amount, service response time, or close rate.)
• What is the business exception rate? Is there a trend?
• Which process steps are most problematic? Which steps take longer than normal?
• Were optimizations successful in their goal of improving performance or reducing exceptions?
• How does one process path differ from another regarding exceptions and throughput?
• What is the current status of a specific customer order?

Business Flow: KPI alerts

The latest version of Business Flow introduces a significant enhancement to support anomaly detection and alerting. App users can configure alerts for any of the four reported business process KPIs, leveraging Davis AI Anomaly Detection for continuous KPI analysis.

Alerting can be configured for each of the app’s four business process KPIs:

• Conversions or fulfillments. How many process instances—claims, deliveries, payments, etc—have successfully completed the final step?
• Errors or business exceptions. Distinct from IT errors, these are process-specific exceptions, usually defined in the application and reported within the application payload.
• Average duration. This is the cycle time of process instances, the elapsed time from start to finish.
• Custom business KPI. This can be any user-defined metric extracted from process instances. Examples include revenue, order quantity, loan amount, and approval rate.

Configure anomaly detection

Use the time series charts from the KPIs over time view to configure Davis AI Anomaly Detection for any of the four key business process KPIs. Each KPI graph includes a Create alert button.

To create an alert for a KPI, select Create alert to open the Davis anomaly detection configuration page. Choose the analyzer type: auto-adaptive, seasonal, or static threshold.

The configuration window is from the Davis AI Anomaly Detection app, adapted for use with Business Flow KPIs. Since Business Flow automatically defines the DQL for the KPIs, the anomaly detection configuration is quite simple.

There are three Davis AI Analyzers to choose from:

• Static thresholds are fixed and don’t change over time
• Auto-adaptative thresholds are calculated by Dynatrace automatically and adapt dynamically to your data’s behavior
• Seasonal baseline thresholds adapt to observed seasonal baselines and are charted with a confidence band

Alerts can be triggered for any of the three Davis Analyzers whenever the metric falls above, below, or outside the defined thresholds. Dynatrace Documentation provides configuration guidance for Davis AI Anomaly Detection.

The actor—or simulated Dynatrace end-user operated by the Davis AI Anomaly Detector in automated workflows—can be a service user or an interactive user with privileges to run the anomaly detector and execute the DQL queries for the time series chart. For more details, see service users.

Once configured, the new Davis AI Anomaly Detector evaluates a certain metric each minute, creating problem events when alert conditions are met. The time series charts add alert annotations to facilitate further analytics in such cases.

What’s next

For more information about alerting on business process KPIs, please visit Dynatrace Documentation.

You can also try out the new Business Flow alerting feature in the Dynatrace Playground.

Note that anomaly detection currently supports process flows of less than 60 minutes. Support for longer-running processes will be added in a future release.

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Globally, organizations are increasingly prioritizing sustainability. Board-level mandates and executive-level sponsorship have become the norm. Motivations include regulatory pressures, favorable investment potential, cost savings, competitive advantage, talent acquisition, and moral imperative. While a dynamic political climate can disrupt how these drivers are prioritized, the goal remains important, and momentum remains strong.

IT carbon footprint is top of mind for observability professionals in many industries. The explosion of AI models shines a new spotlight on the issue, with a recent study showing that using AI to generate an image takes as much energy as a full smartphone charge. For IT, a natural focus should be reducing carbon emissions from electricity consumption from on-premises, hybrid, and multicloud computing.

The challenge along the path

Well-understood within IT are the coarse reduction levers used to reduce emissions; shifting workloads to the cloud and choosing green energy sources are two prime examples. To continue down the carbon reduction path, IT leaders must drive carbon optimization initiatives into the hands of IT operations teams, arming them with the tools needed to support analytics and optimization.

Cloud service providers (CSPs) share carbon footprint data with their customers, but the focus of these tools is on reporting and trending, effectively targeting sustainability officers and business leaders. Unfortunately, they lack the detailed IT context practitioners need to identify carbon reduction opportunities and analyze optimization solutions. While stand-alone tools attempt to complement CSP solutions, they face similar challenges:

• Data granularity and accuracy: Tools struggle with providing detailed and accurate data on carbon emissions. This is partly due to the complexity of instrumenting and analyzing emissions across diverse cloud and on-premises infrastructures.
• Real-time monitoring: The periodic reports from cloud service providers lack real-time monitoring and actionable insights, limiting IT teams’ ability to make immediate adjustments to reduce carbon footprints.
• Integration with existing systems and processes: Integration with existing IT infrastructure, observability solutions, and workflows often requires significant investment and customization.
• Quantifying impact: Quantifying the effectiveness of various emission reduction methods can be challenging, making it difficult for IT leaders to prioritize actions and justify sustainability investments.

The Dynatrace carbon optimization solution

Introduced in 2023, Carbon Impact helps practitioners identify and evaluate meaningful opportunities to optimize their cloud and on-premises infrastructures. The Carbon Impact app directly supports our customers’ sustainability efforts through granular real-time emissions reporting and analytics, translating host utilization metrics into their CO2 equivalent (CO2e). By leveraging existing OneAgent® instrumentation, customers can get started in minutes with no new instrumentation hurdles.

Today, Carbon Impact has a new name: Cost & Carbon Optimization. The name change reflects the app’s expansion into cloud cost analytics, driven by the understanding that optimization approaches for both cost and carbon mostly overlap. You’ll be able to read more about our approach to cloud cost optimization in an upcoming blog post.

Industry certification for Dynatrace Cost & Carbon Optimization

To enhance the trust our customers and partners have in our approach, we commissioned the Sustainable Digital Infrastructure Alliance (SDIA) to test and certify the Cost & Carbon Optimization app. The certification focuses on accuracy and transparency in calculating greenhouse gas (GHG) emissions for AWS, Azure, GCP, and on-premises host instances.

The certification results are now publicly available.

“The SDIA has certified Dynatrace Cost & Carbon Optimization app, ensuring its accuracy and adherence to the environmental management principles outlined in ISO 14004.

“Dynatrace Cost & Carbon Optimization is a reliable estimation system to calculate the operational GHG emissions of IT infrastructure both in cloud and on-premises environments. The calculations and methodology used are in line with the best available scientific approach, as well as with relevant reporting requirements. Further, Dynatrace meets the requirements on transparency, allowing customers to have insight into the way these metrics are calculated, as well as recreating the measurements themselves for independent verification.”

Actions resulting from the evaluation

The certification process surfaced a few recommendations for improving the app. These are the outcomes:

• We replaced GCP’s emissions estimations with more accurate data from Ember, a global not-for-profit clean energy think tank.
• We implemented a wasted energy metric in the app to enhance practitioner actionability.
• We are updating product documentation to include underlying static assumptions.

Transparency breeds confidence

In the spirit of transparency, we’re publishing details of the data sources and assumptions the app makes. Some highlights:

• Data conversion: To derive CO2e and wasted energy metrics, we convert OneAgent data using external sources:
  • Energy emission data is sourced from the European Energy Agency and the Cloud Carbon Footprint tool to determine emission factors specific to cloud data center locations.
  • Thermal design power (TDP) values are derived from AMD and Intel to calculate CPU power consumption.
  • Power usage effectiveness (PUE) is derived from data provided by the cloud providers and data center operators.
• Static assumptions: To complete the calculations, we apply these assumptions:
  • Memory power calculations assume a static draw value of 3 W for each 8 GB memory module.
  • Network traffic power calculations rely on static power estimations for both public and private networks. These estimates are converted using the emission factor for the data center location. Static assumptions are:
    • Local network traffic uses 0.12 W per GB.
    • Public network traffic uses 1.0 W per GB.
  • CPU calculations apply these assumptions:
    • A virtual CPU (vCPU) on any cloud host equals one thread of a physical CPU core, with two threads per core.
    • A CPU operating at 100% utilization consumes power equal to its TDP.
  • Storage calculations assume that one terabyte consumes 1.2 Wh.
  • Cloud storage is replicated twice, which doubles the energy consumption per terabyte.
  • Annual country-level emission factors from the European Environmental Agency are used as input to the calculations.

Connecting carbon reduction to cloud cost optimization through the FinOps Framework

As we merge cost and carbon analytics into a single solution, we expect to reduce redundant or conflicting optimization efforts. Approaches to optimizing IT carbon emissions overlap significantly with those applied to cloud cost management (CCM). Optimizing resource utilization and reducing waste benefit each of these goals. The FinOps Foundation includes sustainability in its framework, stating, “FinOps and cloud sustainability mutually support each other through a similar approach to conscientious and responsible technology usage that enables workload efficiency. If you’re doing one of these – you’re amplifying the other.”

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Business processes support virtually all aspects of an organization’s operations. They’re often categorized by their function; core processes directly create customer value, support processes increase departmental efficiency, and management processes drive strategic goals and compliance. Sometimes overlooked is a fourth category we might call “long-tail processes;” these are the ad hoc or custom workflows that develop in response to gaps between systems, applications, departments, or workflows. Regardless of their role, every business process is designed to improve business outcomes.

Most organizations have hundreds of business processes across these four categories, supported by IT systems through a mix of on-premises, cloud, and SaaS solutions. Business processes are valuable assets, and the lack of visibility is alarming, representing both risk and opportunity.

The whole is other than the sum of its parts

Processes are typically quite complex, involving multiple disparate systems, external interfaces, and human interactions. Despite the deep IT observability you may have deployed, you still can’t infer process health from system status; problems occur even when the underlying infrastructure is healthy. Many of your business processes likely encounter failures, intermittent delays, or ongoing inefficiencies, yet your observability solution likely remains oblivious to these issues.

Given the business-critical nature of many of these processes, it’s clear that the risk from unobserved processes is substantial, tangible, and an unfortunate shared experience.

Is business process management the answer?

Cost and complexity are two primary factors that limit the adoption of business process management and mining solutions. License fees are only the tip of the cost iceberg; it’s not uncommon for a process mining implementation to take many months and cost over $100K per process. Complex data integrations require significant skilled work and coordination from multiple teams and require ongoing maintenance. These factors often limit deployment to a few mission-critical processes, usually managed by a business process management (BPM) center of excellence. This centralized approach can compel organizations to prioritize process monitoring and optimization initiatives to just a few mission-critical processes while neglecting those with less obvious—though significant—impact on business outcomes.

At its best, business process management embraces collaboration between business teams responsible for optimizing business outcomes and IT teams responsible for system observability. But even the best BPM solutions lack the IT context to support actionable process analytics; this is the opportunity for observability platforms.

Enter business process observability

Business process observability determines process health and performance through data gathered at each step, leveraging appropriate levels of system monitoring or interfaces. Log files and APIs are the most common business data sources, and software agents may offer a simpler no-code option.

• Transaction data (such as purchase confirmations, loan approvals, or service requests) is used to calculate inter-step timings and capture the step’s outcome (for example, success, failure, or conditional branch).
• Transaction metadata (such as a product SKU, loan amount, or service address) enriches process insights and increases analytic granularity.
• Transaction context (such as the responsible host, service, or system process) connects each step to its supporting IT infrastructure for troubleshooting and optimization.
• Correlation IDs (such as a transaction ID, customer number, or order number) are used to reassemble discrete transactions into a sequenced end-to-end flow to support detailed process analytics.
• Process health is determined through calculated metrics and process-specific KPIs such as throughput, revenue, cycle time, and exception rate.

To summarize, business process observability:

• Treats the process as a business asset or entity with unique discernable flows.
• Determines process health via data collected at each step, reported as process-specific business KPIs.
• Includes end-to-end and step-specific details for every process instance.

Process observability benefits include enhanced operational efficiency, reduced costs, increased productivity, compliance visibility, and improved customer satisfaction. These benefits come from robust process analytics, often augmented by AI. They’re initially realized reactively and, as organizations mature, proactively, in two key categories:

• Remediation: When process KPIs degrade, process analytics uncover the root cause to inform or automate service recovery.
• Optimization: Whether an ad-hoc initiative or a continuous improvement function, process analytics provide the data-driven foundation for process optimization.

The Dynatrace advantage

Few observability platforms offer business process observability. At best, they group metrics from isolated process steps into a linear process-like view, ignoring the complexity of real-world process flows and lacking end-to-end flow details to support effective analytics and optimization.

Dynatrace treats your business processes as true business assets, connecting business KPIs with end-to-end process analytics to meet the collaboration and optimization needs of business and IT teams. Dynatrace makes it easy to get started and cost-effective to implement broadly.

Dynatrace business process observability leverages key platform capabilities, including:

Business events, which provide easy access to business data, are automatically enriched with IT context. Business events can come from:

• OneAgent – a unique capability offering configurable no-code access to in-flight application payload.
• Log files – using OpenPipeline to extract and transform business data while reducing log management and storage overhead.
• API – to ingest data from relevant business systems.
• RUM – for high-precision user journey analytics.

Business Flow is a Dynatrace® App that simplifies business process configuration, reporting, and analytics.

• Easy configuration of process flows
• Automatic KPI reporting
• Granular flow details

Simplified access to business data coupled with a purpose-built app makes it easy to get started with business process observability. It’s not uncommon for Dynatrace customers to implement Business Flow in just a few weeks, often without any development effort, with only incremental subscription costs. To learn first-hand about some of their successes, check out a few of these Business Analytics customer breakouts from our recent Perform user conference. You can also try it yourself in our Dynatrace Playground (registration is required).

Wherever you might be on your transformation journey, business processes form the framework that drives better business outcomes. This is an opportune time to expand the value IT delivers to your business teams.

The post Business process observability: An IT solution to a business challenge appeared first on Dynatrace news.

There’s a goldmine of business data traversing your IT systems, yet most of it remains untapped. To unlock business value, the data must be:

• Accessible from anywhere. Data has value only when you can access it, no matter where it lies.
• Easy to access. Simplicity accelerates time-to-value and reduces implementation and maintenance costs.
• Real time. Agile business decisions rely on fresh data.
• Precise. Accuracy provides the confidence needed for business automation.
• Contextualized. Metadata enrichment improves collaboration and increases analytic value.

The Dynatrace® platform continues to increase the value of your data—broadening and simplifying real-time access, enriching context, and delivering insightful, AI-augmented analytics. Our Business Observability solution is a prominent beneficiary of this commitment.

Business events: Delivering the best data

It’s been two years since we introduced business events, a special class of events designed to support even the most demanding business use cases. Since then, many of our customers have embraced the opportunity to explore and adopt new business observability use cases. Most of these leverage the unique capability of Dynatrace OneAgent® to extract business data from in-flight application payloads—without writing any code. Other data sources, including APIs and log files—are used to expand access, often to external or proprietary systems. Enhancing access to business data from log files is an important priority, and OpenPipeline makes this a reality.

Dynatrace OpenPipeline is a new stream processing technology that ingests and contextualizes data from any source. You can now use OpenPipeline to extract business events from log files, complementing OneAgent as a primary source of business data. This is especially important when legacy or proprietary systems don’t meet OneAgent’s environmental prerequisites; in these cases, log files are usually the preferred source of business data.

In fact, it’s likely that some of your critical business systems already write business data to log files. For years, logs have been the dominant approach many observability vendors have taken to report business metrics on dashboards. You might still be using one of these tools despite the drawbacks, which include a lack of IT context, constraints on data privacy and data retention, and, as the only convenient source of business data, a limiting lack of breadth. OpenPipeline makes migrating these use cases to Dynatrace easy, helping you overcome these limitations to realize greater value.

Use case categories

It’s helpful to categorize the nearly unlimited use cases covered by Business Observability. Two of the most important categories are:

• Business reporting, analytics, and automation. Track business metrics, key performance indicators (KPIs), and service level objectives (SLOs)—automatically and in context with IT infrastructure and services—to promote collaboration between business and IT teams.
• Business process monitoring and optimization. Monitor and optimize business processes with real-time visibility into process KPIs and detailed analytics for each step to improve customer satisfaction, increase operational efficiency, and reduce cost.

Most of the use cases in these two broad categories benefit from the flexibility that comes from multiple available sources of business data. For example:

• An airline’s reporting and analytics dashboard includes data showing flights, passengers, available seats, passenger load, revenue per passenger, flight crew staffing, arrival delays, and customer satisfaction metrics. The data may come from a mix of systems, including a departure control system (DCS), an airline reservation system (ARS), a legacy inventory control system, and a SaaS-based Voice of the Customer (VoC) solution.
• A financial institution’s loan origination business process includes a series of process milestones, including loan application, credit scoring, application review, contract generation, CRM, and loan funding. All of these steps are critical components of the process, likely to be implemented using different systems. Furthermore, to understand process health, individual steps must be viewed in the context of the entire process. For this, we use Business Flow to track, analyze, and optimize complex business processes, treating the process as an observable IT and business asset.

Use OpenPipeline to extract business events from logs

Using OpenPipeline, you can ingest log data into Dynatrace from a wide range of sources, including OneAgent, Extensions, the Log ingest API, and OpenTelemetry. The pipeline includes a configurable business event processor as part of the data extraction stage; this processor is used to extract and convert relevant business data into business events.

There are many benefits to extracting business events from log files. These include:

• Improved data privacy. Sensitive business data is separated from IT observability data.
• Improved data management. Fine-grained permission and retention policies can be tailored to individual business use cases.
• Reduced storage and query overhead for business use cases. Business events are a small, often negligible subset of log data.
• Simplified and enhanced analytics efficiency. Business events from log files are unified with business events from OneAgent, RUM, and API.

Extracting business events from logs: Configuring OpenPipeline

The OpenPipeline app guides you through the three configuration steps required to extract business events from log files:

1. Identify the source of the log file.
2. Define OpenPipeline’s dynamic routing matching criteria used to route the log lines of interest to a second pipeline.
   
3. Create the target pipeline. Use the Data extraction tab to define the matching condition that creates the business event. You need to include static or dynamic Event type and Event provider fields.
   

Within the target pipeline, you can also define processing rules, extract metrics, set the security context, and define retention periods. Log data is then processed accordingly, stored in Dynatrace Grail™ causational data lakehouse, and available for your Business Observability use cases.

The value is in the data

OpenPipeline broadens Dynatrace’s unified access to the best data to deliver the best value. Now’s the time to see how it can benefit your organization.

For more details about OpenPipeline read this Dynatrace OpenPipeline blog post.

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Take your monitoring, data exploration, and storytelling to the next level with outstanding data visualization

All your applications and underlying infrastructure produce vast volumes of data that you need to monitor or analyze for insights. Visualizations help to curate data into a form that is more accessible to understand, highlighting trends and outliers, gaps, clusters, or patterns. At a single glance, visualizations can raise questions that stimulate further exploration or indicate problems.

Each type of visualization tells a different story and is best suited for a particular use case. If you want your data to speak to its audience, you need a comprehensive toolkit of visualizations and customization options. Good visualizations are not just static, unintelligent data presentations; they enable interaction and ideally serve as a starting point for subsequent analysis.

Dynatrace unified analytics capabilities for observability are top-of-the-class (Gartner Magic Quadrant 2024), enabling you to query and analyze all your observability data across your enterprise. The Dynatrace Notebooks and Dashboards apps are the perfect starting point for visualizing and understanding your data for monitoring or in-depth analysis.

Over the last year, we introduced many new functionalities and updated our visual presentation to provide you with an all-new experience:

• Optimized for exploring large data sets: We’ve optimized our entire interface, components, and visualizations to present large volumes of data while providing all the required flexibility.
• Visualize with a single click: Even inexperienced users can visualize data sets and create graphs in seconds.
• Broad range of visualizations: Our curated catalog provides numerous visualization types and hundreds of customization options, including the newly added honeycomb and histogram visualizations.
• Interact with your data: We integrated additional data interaction methods to provide more information immediately.
• Quick analysis with Davis CoPilot™: Explore your data through natural language by translating your conversational prompts directly into DQL queries.

Now, let’s introduce you to our two newest entries to our visualization catalog and tell you about the great things you can do with them.

New: identify hotspots with the honeycomb visualization

Honeycombs are great for visualizing health in complex and distributed systems, enabling you to visualize countless entities effectively and at scale. They have become a quasi-standard in the industry, especially for infrastructure monitoring visualizations.

The new honeycomb visualization in Dynatrace enhances your health dashboards and offers many customization options to tailor it to your needs. For example, it supports string and numerical values, enabling a multitude of different use cases. There are many practical applications of honeycombs; here is a small sample of just a few of them:

• Problem visualization: The new, ready-made dashboard for the Problems app features two honeycomb visualizations. At one glance, you see which entities are particularly affected by problems, and you can also identify the blast radius of a single problem to understand how many entities have been affected. Have a look at them on our Dynatrace Playground.
• Infrastructure health: A honeycomb chart is often used to visualize infrastructure health. You can use it to visualize CPU utilization across your hosts, disk space used, server-side response time, web request/service failure rates, or any other area where you need to spot outliers immediately.
• Service Level Objectives (SLO) tracking: Honeycomb charts can visualize SLOs, helping you monitor whether your services meet performance and reliability targets. Based on the color, you immediately see if any SLOs are off track. This can guide you in prioritizing issues that impact user experience.

How you get the best results with honeycombs

Honeycombs highlight hotspots that require attention. To achieve the best visual outcome, we recommend experimenting with the available customization options.

• Try different cell shapes. The honeycomb visualization also supports circles and square variants, allowing you to differentiate between use cases clearly.
• Use color coding to tell a story. Use different diverging and sequential color palettes to highlight patterns and insights in your data. The chart also supports conditional coloring for both string and numeric values.
• Min and max limits. Set an applicable, expected value range in the honeycomb visualization to ensure effective coloring and highlighting of hotspots and outliers. For example, set the value range for CPU consumption from 0% to 100%. In other use cases, you want to ensure a consistent zero-baseline (that is min = 0), but with an automatic scaling max value (max = auto).

Go to our documentation to learn more about implementing honeycomb visualizations on your dashboards or notebooks.

New: explore your data with histograms to identify patterns

The histogram chart is a crucial visualization for understanding the distribution patterns of values within a given dataset. It shows where the peaks of the distribution are, whether the distribution is skewed or symmetric, and whether there are any outliers.

While histograms look much like time-series bar charts, they’re different in that each bar represents a count (often termed frequency) of metric values. These bars are called bins or buckets; their width represents a value range. The height of the bar reflects the frequency or count of data points within a bucket.

The use cases and underlying metrics analyzed via histograms are extremely broad:

• Latency distribution: Histograms can show the distribution of request latencies, helping you understand how many requests fall into different latency buckets. This is useful for identifying performance bottlenecks and understanding the overall user experience.
• Resource utilization: Use histograms to visualize the distribution of CPU or memory usage across different instances or containers. This helps identify outliers and understand the overall resource consumption patterns.
• Distributed tracing: Histograms can analyze the response times of different endpoints or services, allowing you to pinpoint which parts of your system are slower and need optimization.
• User behavior tracking: Track user interactions, such as login or page load times, to understand how users are experiencing your application. This can help identify areas for improvement in user experience.

How you get the best results with histogram charts

Experiment with bucket sizes. Use DQL’s built-in range function, together with the summarize command, to bucketize your data. The choice of bin size has an inverse relationship with the number of bins. The larger the bin sizes, the fewer bins will be needed to cover the whole range of data. With a smaller bin size, you’ll get more bins.

It is worth taking some time to test out different bin sizes to see how the distribution looks in each one, then choose the best plot that represents the data. Try out different range sizes or bin ranges (“widths”) with your data set – doing so can help you identify distinct patterns in the data.

If you have too many bins, the data distribution will look rough, making it difficult to discern the signal from the noise. On the other hand, with too few bins, the histogram will lack the details needed to discern any helpful patterns from the data. Identify common distribution patterns such as bimodal, comb, edge peak, normal, skewed, and uniform.

Add split by parameter. This can help compare sub-distributions; however, it is ideally limited to only two sub-divisions, such as A vs. B, North vs. South, Open vs. Closed, Male vs. Female, etc.

If you want to learn more about how to best use histograms for OTel observability, check out Mikko Viitanen’s OpenTelemetry histograms blog post. In this post, you’ll learn how to define and monitor service-level objectives with histograms that can be used to set up alerts.

After introducing the new visualizations, we will now look at our new custom settings.

Optimize your visualizations with many new configuration options

We elevated the handling of visualization settings across Dashboards and Notebooks in three powerful ways:

1. We extended the customization options for all data visualizations by offering an additional 35+ configuration options, such as custom column types in the table, so you can better tailor your visualizations to your specific requirements.
2. We improved the usability of all visualization settings by introducing new unified UI controls across both apps. Now, it’s even easier for users to customize visualizations as they see fit for any use case.
3. We introduced a visualization settings search so that you can instantly search all settings and find the exact configuration or customization option you’re looking for.

Get more details immediately

In our recent release, we added more functionality to enable you to interact with your data. You can now zoom into your data or pan left and right in all time-series visualizations. This allows you to zoom in on a particular data set, see underlying details, or change the displayed data altogether. This allows you to dive deeper into your data anytime without needing to modify/rewrite your original query.

Taking this interaction a step further, in Dashboards, if you zoom in on one visualization, it will automatically apply to all other visualizations on the same dashboard.

The functionality is automatically available in all time-series charts in the Dashboards and Notebook apps.

Zoom-in

Click and drag any time series chart in your Dashboard or Notebook to zoom in on your data. The interaction will re-fetch data for the selected timeframe (in most cases, with a more fine-grained interval).

Alternatively, you can zoom in and out with the integrated chart toolbar, located in the top right corner of the chart, keyboard shortcuts, or touch gestures.

In Dashboards, the zoom interaction adapts the timeframe for the current chart and updates the entire dashboard, automatically synchronizing all other tiles and visualizations.

^{Figure 5. Zoom-in}

Panning

You can click and drag the chart’s x-axis to pan it to the left or right while maintaining the current zoom level. Alternatively, you can pan left and right using the middle mouse button, activating the chart’s pan mode (via the integrated chart toolbar), keyboard shortcuts, or touch gestures.

Keyboard accessibility

If you prefer interacting with your visualizations via the keyboard, we also have you covered: all interactions are also available via the keyboard. Switch between modes with “e” to explore your data or “p” to pan the chart left and right. Holding down the “CMD/CTRL” key while pressing “arrow up” or “arrow down” will let you zoom in or out of the chart. Pressing “r” will quickly let you reset all the zoom and pan changes.

What’s next

World map and heatmap visualization

We constantly exchange with our community and add further visualizations to our curated catalog where it makes sense. We’re currently working on introducing multiple world map visualizations and a heatmap, which you can expect to be released in the upcoming quarters.

Synchronized crosshair

In addition to the interactions described above, we will soon support automatically synchronizing all crosshairs across all time-series charts. That way, you can compare multiple charts more easily, regardless of the metric or time span.

Try our new visualizations now

If you’re an existing Dynatrace customer, visit your Dashboards app and check out our ready-made dashboards. Our new Getting Started document explains how to create and customize honeycomb and histogram visualizations. Also, have a look at our new Problems Dashboard, which you can access and download directly from the Dynatrace Playground.

To learn more about how data visualizations can enhance your app insights, check out our latest episode of Inside Dynatrace Apps with Mikele Hasson and Penny Scully

The post Next-level interaction and customization of data visualizations in Dynatrace Dashboards and Notebooks appeared first on Dynatrace news.

Carbon Impact leverages business events, a special data type designed to support the real-time accuracy and long-term granularity demands common to business use cases. For Carbon Impact, these business events come from an automation workflow that translates host utilization metrics into energy consumption in watt hours (Wh) and into greenhouse gas emissions in carbon dioxide equivalent (CO2e). These metrics are automatically enriched with Smartscape® topology context, connecting them to their source systems to enable flexible and granular analysis.

The methodology and algorithms were designed by Dynatrace with guidance from the Sustainable Digital Infrastructure Alliance (SDIA), expanding on formulas from the open source project Cloud Carbon Footprint. Carbon Impact measures and reports IT energy consumption and the carbon footprint of all OneAgent-instrumented hosts across an organization’s entire hybrid and multicloud environment in a single interface.

The Carbon Impact app

Carbon Impact provides a unified interface to explore and analyze energy consumption and carbon emissions. Carbon Impact uses host utilization metrics from OneAgents to report the estimated energy consumption for CPU, storage I/O, memory, and network. Energy consumption is then translated to CO2e based on host geolocation. This is important because each region uses a different mix of energy sources to generate electricity, and each source has a different environmental impact. In other words, the CO2e from 1 kWh of energy produced from solar or wind is less than that produced from coal or gas.

Carbon Impact reports two main KPIs:

• Energy consumed by the IT stack (Wh)
• Emissions generated by that energy (CO2e grams)

Carbon Impact provides two different types of optimization recommendations based on discovered idling and underutilized hosts, as these are often good candidates for downsizing or shutting down. To evaluate the impact of implementing these recommendations, Carbon Impact connects directly to the Hosts view to explore host details, including running services.

To get started with Carbon Impact, add the app from Dynatrace Hub and follow the setup guide.

Use DQL to perform ad-hoc analysis of energy consumption and carbon emissions

Carbon Impact simplifies evaluating your carbon footprint at data center and host levels. The app automatically builds baselines, important reference points for analyzing the environmental impact of individual hardware or software instances. Optimization initiatives can then be measured against these baselines to report progress toward defined or aspirational reduction goals.

Some use cases benefit from dashboards or ad-hoc analytics, complementing the insights from Carbon Impact. In the following example, we use DQL to answer questions not directly covered by Carbon Impact. We provide a Notebook for you to follow along in your Dynatrace tenant; you can run or download the Notebook from the Dynatrace Playground and upload it to your tenant. This Notebook uses data already generated by Carbon Impact, leveraging the simple and clear common schema to facilitate ad-hoc analysis.

Understanding the schema and how to leverage topology and entity metadata is the starting point for deriving greater insights into the energy consumption and carbon emissions generated by your IT stack. The schema of the data generated is defined in the Dynatrace Semantic Dictionary.

The Dynatrace Query Language (DQL) statement we use to explore the raw data that Carbon Impact generates is quite simple: We filter by the event type carbon.measurement defined by Carbon Impact.

Know your data before starting ad-hoc analysis

The schema of a carbon data point generated by Carbon Impact contains data specific to the host components contributing to energy consumption: CPU, memory, storage IO, and network. In the same data point, based on the host cloud.region, energy consumption is transformed into grams of CO2e using localized energy carbon intensity. Carbon intensity is a measure of how much CO2 emissions are produced per kilowatt hour of electricity consumed, used to transform watt hours into grams of CO2e. The mix of energy sources determines the localized carbon intensity.

With this understanding, let’s generate a list of the main carbon contributors to focus our optimization initiatives where the impact would be most significant.

Start with the main contributors as candidates for optimization while greening your IT

The DQL for this analysis is relatively simple. We use dt.entity.host to group two summarizations: one for total energy consumption (Wh) and one for total emissions (CO2e). We can calculate an average carbon intensity for every host and cloud region with those two summarized values. With this information, we can evaluate the impact of moving a workload to a different location on carbon emissions.

This is an example of what we call level 1 optimization, described in this blog post. Decisions at this level focus on minimizing emissions, as measured by carbon intensity or power usage effectiveness (PUE).

To provide contextual topology information, we connect the attribute dt.entity.host with host properties to extract the custom name of the host using a lookup command against the host entity model. In an upcoming release, we will use that attribute to navigate through Smartscape topology to answer questions about the host location and its processes.

To continue our level 1 optimization analytics, we can examine the distribution of our carbon footprint among the providers of our hybrid cloud. Based on these insights, we can make data-driven decisions to move our workloads from on-premises to the cloud or to distribute them among different cloud vendors.

Distribution of emissions and hardware usage distributed by cloud type

Again, without complex DQL, we can extract emissions data from Carbon Impact, enriching the results with the cloudType host property and the total number of cores that the host contains. DQL enriches the data line by line, allowing us to distribute energy consumption, emissions, and average CPU core emissions across cloud providers and private data centers.

We can use the same approach to distribute energy and emissions at the VMware hypervisor level. Since that information is included in the topology metadata, enriching the dataset with the hypervisor running the virtualized hosts is simple.

Detailed energy consumption and carbon emissions by VMware hypervisors

Green coding: Add emissions to classic APM

The above examples focus on analyzing hosts and data centers to evaluate the benefits of shifting workloads or re-sizing hosts.

Organizations should begin their carbon optimization efforts by evaluating these options first, as they often result in significant quick wins. For ongoing optimization, carbon reduction practices can be embedded in coding principles.

The optimization approach varies and is democratized across development teams in green coding initiatives. Coding best practices that optimize process communications, remove redundant loops, or reduce data transmission volumes result in reduced energy consumption and carbon emissions. These optimizations might sound similar if you’re acquainted with Application Performance Management (APM) best practices. In fact, most of the proposed optimizations for computational efficiency and improved performance will also reduce energy consumption. In other words, APM best practices are close to Green Coding best practices.

And the good news is that by reducing your energy consumption, you’re also reducing your carbon emissions.

Green coding focuses on the software that is running on our digital infrastructure. Any approach to make our software greener benefits from the foundational step of measuring energy and carbon emissions baselines.

Moving up through a technology stack, on top of hosts, processes are run. The challenge then becomes measuring the energy consumed by a process by a specific piece of software. If you think this was difficult at the hardware level, it’s significantly more difficult for software. Let’s take some of the mystery out of it.

We can follow the same approach to evaluating the carbon emissions of hosts and hypervisors. Again, utilization metrics are the key to distributing emissions by individual software processes.

Navigating through Smartscape and allocating emissions by software components that run on the instrumented host

Here, the DQL query is a bit more complex as it navigates through the list of processes running on each host, enriching the data with emissions data from those hosts, including the CPU utilization of each process.

Instead of directly querying the emissions data, we now generate a list of all the running processes. From there, we enrich the data with the associated hosts and process group.

To get process-level emissions and CPU utilization, use the following sequence of lookup queries:

• The first lookup collects the total emissions generated for each host in the calculation time frame.
• The second lookup collects CPU utilization, querying the process.cpu.usage metric for each process instance.
• The third lookup collects a summarization at the host level of the process.cpu.usage metric, so it is possible to have the total amount of CPU used to build a ratio for each process of CPU usage.

With these three new attributes collected for each process, it’s easy to build a ratio that simply distributes emissions per host to individual processes, comparing the CPU usage of one process with the overall CPU usage for all processes on a single host.

Once we allocate emissions for each process, it’s easy to prioritize the best candidates for green coding attention, focusing on reducing energy consumption and the resulting emissions.

Conclusion

Sustainability goals and the efforts required to meet those goals are varied and broad. For most organizations, the digital infrastructure that supports the business is fast becoming a key focus area. Embedding optimization best practices and the tools to support them will help meet these goals. Dynatrace Carbon Impact combines granular emissions metrics with comprehensive application and infrastructure observability to jumpstart your hybrid cloud carbon reduction initiatives.

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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.

The business process observability challenge

Increasingly dynamic business conditions demand business agility; reacting to a supply chain disruption and optimizing order fulfillment are simple but illustrative examples. Business agility requires real-time visibility into process health and performance, measured by business Key Performance Indicators (KPIs) that are shared between business stakeholders and the supporting IT operations teams. However, business processes can be inefficient, broken, or violate Service Level Objectives (SLOs) even when the underlying system’s health is good; a process is greater than the sum of its parts. A business process only becomes observable when a) it is defined as a unique entity or asset and b) process-specific business KPIs are used to gauge health.

Business Process Management (BPM) solutions might seem like the answer, but they too often suffer from legacy technical constraints. Many are platform-centric, unable to expand beyond proprietary borders to embrace more common heterogeneous environments. They can be expensive to implement and maintain, rely on fragile data pipelines, and require highly skilled data analysts to ensure ongoing relevance. BPM solutions lack IT context, making them ill-suited for collaboration between business and IT operations teams.

Most business processes are not monitored. Why?

First and foremost, it’s a data problem. Locked away in disparate systems, difficult to access, and with different formats, business data can be days or weeks old. As a result, it takes significant effort to build and maintain these often fragile data pipelines. Undue reliance on log files as the primary business data source adds development overhead for implementation and maintenance, further limiting agility.

If you can collect the relevant data (and that’s a big if), the problem shifts to analytics. Business processes can be quite complex, often including conditional branches and loops; many business process monitoring initiatives are abandoned or simplified after attempting to map the process flow. Connecting data from different systems, stitching process steps together, calculating delays between steps, alerting on business exceptions and technical issues, and tracking SLOs are just some of the requirements for an effective analytics solution.

As a result, most business processes remain unmonitored or under-monitored, leaving business leaders and IT operations teams in the dark. Business health and IT health remain disconnected in separate silos, limiting opportunities for effective collaboration. The resulting business process blind spots delay response to business disruptions, leading to dissatisfied customers, BizOps friction, and inefficient IT resource allocation.

Business events: Addressing the data challenge

Dynatrace business events address the data challenge by making it easy to access real-time business data. Business events can come from anywhere—OneAgent^®, log files, Real User Monitoring (RUM) sessions, or external systems through an API. They deliver the real-time precision needed for confident data-driven business decisions. Uniquely, OneAgent can capture business data from in-flight application payloads, eliminating the need for application changes (for example, to write business data to a log file). Regardless of the source, business events are unified in Grail^® and are automatically enriched with Smartscape^® topology context, connecting business data directly to the supporting IT infrastructure.

Business Flow: Addressing the analytics challenge

Dynatrace addresses the analytics challenge with Business Flow (available on Dynatrace Hub), which was built using Dynatrace AppEngine. Initially released in April 2023, Business Flow masks analytics complexity through simple business process configuration and an intuitive interface. With Business Flow, you can:

• Connect business events from any source into an end-to-end process view.
• Report end-to-end process delays and measure delays between each step.
• Identify drops at each step.
• Report business exceptions at each step.
• Drill into the details of any step or process flow.
• Report process KPIs, including completed flows, average flow completion time, business exceptions, and a customizable business KPI.

Business Flow

The newest release of Business Flow introduces significant enhancements to cover more complex business processes and increase the depth of analysis.

To reduce the complexity of business process monitoring, it’s good practice to evaluate whether some process steps can be abstracted by monitoring only key milestones. For cases where process abstraction is not desirable, Business Flow adds two new features:

• Process branches – Business Flow now supports process branching. A branch can be conditional—only active for certain flows—as in the case of a document flagged for manual auditing. Branches can also represent alternate paths, one of which must be followed. A step may have up to five branches.
• Increased number of process steps – Business Flow now supports up to 20 steps in a single flow. Note that each branch is counted as a step when calculating the total number of steps in a process.

Business events are automatically connected into an end-to-end flow using a correlation ID such as order_number. Using consistent labels at each process step across all systems is good practice. For cases where the labels differ between systems (order_number at one step and order_id at another), Business Flow now supports step-specific local correlation IDs, overriding the global ID configured for the process.

New detailed flow views enhance process analysis. At each step, you can view a list of unique flows that a) pass through that step, b) are dropped at that step, or c) have been classified as in-flight (not yet complete). Each unique flow can be examined in its entirety to view the timestamps and attributes of the associated business events.

Greater business process complexity increases the potential for configuration errors. Business Flow now alerts on two conditions:

• Missing correlation ID – When a business event lacks a correlation ID, Business Flow can’t connect it to a unique end-to-end flow. Some use cases might benefit from isolated step metrics, but these are rare.
• Out-of-sequence flows – Business Flow generates an alert when an individual business flow skips a step. This condition can occur if a conditional process branch exists but has not been configured in Business Flow. A skipped step may also indicate a process anomaly worthy of investigation.

Business processes are the heart of modern organizations. IT operations and business teams benefit from a shared view of process health, using business KPIs as primary health indicators. Business Flow makes monitoring, analyzing, and optimizing complex business processes easier than ever.

The post Business Flow: Why IT operations teams should monitor business processes appeared first on Dynatrace news.

In the previous blog post of this series, we discussed the crucial role of Dynatrace as an orchestrator that steps in to stop the testing phase in case of any errors. Additionally, Dynatrace equips SREs and application teams with valuable insights powered by Davis^® AI. In this blog post of the series, we will explore the use of Site Reliability Guardian (SRG) in more detail.

SRG is a potent tool that automates the analysis of release impacts, ensuring validation of service availability, performance, and capacity objectives throughout the application ecosystem by examining the effect of advanced test suites executed earlier in the testing phase.

Validation stage overview

The validation stage is a crucial step in the CI/CD (Continuous Integration/Continuous Deployment) process. It involves carefully examining the test results from the previous testing phase. The main goal of this stage is to identify and address any issues or problems that were detected. Doing so reduces the risk of production disruptions and instills confidence in both SREs (Site Reliability Engineers) and end-users. Depending on the outcome of the examination, the build is either approved for deployment to the production environment or rejected.

Challenges of the validation stage

In the Validation phase, SREs face specific challenges that significantly slow down the CI/CD pipeline. Foremost among these is the complexity associated with data gathering and analysis. The burgeoning reliance on cloud technology stacks amplifies this challenge, creating hurdles due to budgetary constraints, time limitations, and the potential risk of human errors. Additionally, another pivotal challenge arises from the time spent on issue identification. Both SREs and application teams invest substantial time and effort in locating and rectifying software glitches within their local environments. These prolonged processes not only strain resources but also introduce delays within the CI/CD pipeline, hampering the timely release of new features to end-users.

Mitigate challenges with Dynatrace

With the support of Dynatrace Grail™, AutomationEngine, and the Site Reliability Guardian, SREs and application teams are assisted in making informed release decisions by utilizing telemetry observability and other insights. Additionally, the Visual Resolution Path within generated problem reports helps in reproducing issues in their environments. The Visual Resolution Path offers a chronological overview of events detected by Dynatrace across all components linked to the underlying issue. It incorporates the automatic discovery of newly generated compute resources and any static resources that are in play. This view seamlessly correlates crucial events across all affected components, eliminating the manual effort of sifting through various monitoring tools for infrastructure, process, or service metrics. As a result, businesses and SREs can redirect their manual diagnostic efforts toward fostering innovation.

1. Configure an action for the Site Reliability Guardian in the workflow. The action should focus on validating the guardian’s adherence to the application ecosystem’s specific objectives (SLOs). Additionally, align the action’s validation window with the timeframe derived from the recently completed test events.
   
2. As the action begins, the Site Reliability Guardian (SRG) evaluates the set objective by analyzing the telemetry data produced during advanced test runs. At the same time, SRG uses DAVIS_EVENTS to identify any potential problems which could result in one of two outcomes.
   
   Outcome #1: Build promotion
   Once the newly developed code is in line with the objectives outlined in the Guardian—and assuming that Davis AI doesn’t generate any new events—the SRG  action activates the successful path in the workflow. This path includes a JavaScript action called promote_jenkins_build, which triggers an API call to approve the build being considered, leading to the promotion of the build deployment to production.
   
   Outcome #2: Build rejection
   If Davis AI generates any issue events related to the wider application ecosystem or if any of the objectives configured from the defined guardian are not met, the build rejection workflow is automatically initiated. This triggers the disapprove_jenkins_build  JavaScript action, which leads to the rejection of the build.
   
   Moreover, by utilizing helpful service analysis tools such as Response Time Hotspots and Outliers, SREs can easily identify the root cause of any issues and save considerable time that would otherwise be spent on debugging or taking necessary actions.  SREs can also make use of the Visual Resolution Path to recreate the issues on their setup or identify the events for different components that led to the issue. In both scenarios, a Slack message is sent to the SREs and the impacted app team, capturing the build promotion or rejection.The telemetry data’s automated analytics, powered by SRG and Davis AI, simplify the process of promoting builds. This approach effectively tackles the challenges that come with complex application ecosystems. Additionally, the integration of service tools and Visual Resolution Path helps to identify and fix issues more quickly, resulting in an improved mean time to repair (MTTR).

Validation in the platform engineering context

Dynatrace—essential within the realm of platform engineering—streamlines the validation process, providing critical insights into performance metrics and automating the identification of build failures. By leveraging SRG and Visual Resolution Path, along with Davis AI causal analysis, development teams can quickly pinpoint issues, and further rectify them ensuring a fail-smart approach. The integration of service analysis tools further enhances the validation phase by automating code-level inspections and facilitating timely resolutions. Through these orchestrated efforts, platform engineering promotes a collaborative environment, enabling more efficient validation cycles and fostering continuous enhancement in software quality and delivery.

In conclusion, the integration of Dynatrace observability provides several advantages for SREs and DevOps, enabling them to enhance the key DORA metrics:

• Deployment Frequency: Improved deployment rate through faster and more informed decision-making. SREs gain visibility into each stage, allowing them to build faster and promptly address issues using the Dynatrace feature set.
• Change Lead Time: Enhanced efficiency across stages with Dynatrace observability and security tools, leading to quicker postmortems and fewer interruption calls for SREs.
• Change Failure Rate: Reduction in incidents and rollbacks achieved by utilizing “Configuration Change” events or deployment and annotation events in Dynatrace. This enables SREs to allocate their time more effectively to proactively address actual issues instead of debugging underlying problems.
• Time to restore service: While these proactive approaches can help improve Deployment Frequency and Change Lead Time, telemetry observability data with Dynatrace AI causation engine Davis AI can aid in improving Time to restore service.

In addition, Dynatrace can leverage the events and telemetry data that it receives during the Continuous Integration/Continuous Deployment (CI/CD) pipeline to construct dashboards. By using JavaScript and DQL, these dashboards can help generate reports on the current DORA metrics. This method can be expanded to gain a better understanding of the SRG executions, enabling us to pinpoint the responsible guardians and the SLOs managed by various teams and identify any instances of failure. Addressing such failures can lead to improvements and further enhance the DORA metrics. Below is a sample dashboard that provides insights into DORA and SRG execution.

In the next blog post, we’ll discuss the integration of security modules into the DevOps process with the aim of achieving DevSecOps. Additionally, we’ ll explore the incorporation of Chaos Engineering during the testing stage to enhance the overall reliability of the DevSecOps cycle. We’ll ensure that these efforts don’t affect the Time to Restore Service turnaround build time and examine how we can improve the fifth key DORA metric, Reliability.

What’s next?

Curious to see how it all works? Contact us to schedule a demo and we’ll walk you through the various workflows, JavaScript tasks, and the dashboards discussed in this blog series.

If you’re an existing Dynatrace Managed customer looking to upgrade to Dynatrace SaaS, see How to start your journey to Dynatrace SaaS.

The post Automate CI/CD pipelines with Dynatrace: Part 4, Validation stage appeared first on Dynatrace news.

Recent platform enhancements in the latest Dynatrace, including business events powered by Grail™, make accessing the goldmine of business data flowing through your IT systems easier than ever. One of the more popular use cases is monitoring business processes, the structured steps that produce a product or service designed to fulfill organizational objectives. By treating processes as assets with measurable key performance indicators (KPIs), business process monitoring helps IT and business teams align toward shared business goals. This collaboration increases process efficiency and improves customer satisfaction by identifying opportunities for process improvement and detecting process anomalies in real time.

Business process monitoring leverages business events, a new data type designed to support the real-time accuracy and long-term granularity demands common to business use cases. Business events can come from many sources, including OneAgent®, external business systems, RUM sessions, or log files. Of particular note, OneAgent can now extract business data from in-flight application payload, prioritized to ensure the lossless precision that business use cases demand. OneAgent business events require no code changes, just a simple configuration to define capture rules. Business events are automatically enriched with Smartscape® topology context, connecting them to their source systems and applications for effective BizOps collaboration.

The Business Flow app

Business Flow, built with AppEngine, simplifies the configuration, monitoring, and analysis of business processes. Business Flow uses business events to define and track each process step or milestone. Correlation IDs—an order number, transaction ID, or any unique identifier—connect steps to create an end-to-end business flow that can span hours, days, weeks, or longer.

Business Flow reports four KPIs:

• Flows completed
• Business exceptions or process errors
• Average process completion time
• A configurable business performance indicator such as revenue, extracted from a business event.

Let’s examine a few practical business process monitoring examples.

Order fulfillment

In this example, an e-commerce company wants to track its order fulfillment business process. Important metrics include end-to-end measures of delay in tracking an order fulfillment service level goal and delay between each step to highlight anomalies and identify opportunities for process improvement. While the process includes many steps, the company started by tracking three milestone steps: order placed, order shipped, and order delivered.

Getting started is simple.

• Define a business event for each milestone step. The event source determines the configuration steps; see our business analytics documentation for details.
• In Business Flow, choose a configured business event to represent each step.
• Choose an attribute from a business event (for example, order_amount) to represent the business flow KPI.
• Choose an attribute common to each business event (for example, order_id) as the correlation ID.

  

Real-time business process monitoring helps IT and business teams track process performance, detect process anomalies, and optimize process inefficiencies to ensure customer satisfaction and business outcomes.

Business process conversion funnels

In our e-commerce example, there’s an implied expectation that every order placed will be delivered; only a process anomaly would result in a lost package, and process efficiency might focus on improving fulfillment time. Some business processes behave like conversion funnels, with expected attrition at each step. A loan application process is an example; not all customers who apply for a loan will commit, and delays in the approval process might result in increased abandons. It’s easy to switch the business process display from the tree view to a funnel view.

To get started with Business Flow, add the app from Dynatrace Hub and follow the setup guide.

Use DQL to monitor business processes

Business Flow is built with AppEngine, designed to simplify business process monitoring, in part by masking the underlying queries. For most use cases, it’s the best starting point for process observability. There are also cases where dashboards or alert integrations are important, often as complements to using the Business Flow app. The following example examines the DQL used to build a custom dashboard for a major French B2B food company to monitor five critical steps in a production line. We provide a Notebook for you to follow along in your Dynatrace tenant; you can download the Notebook from GitHub and upload it to your tenant. The Notebook includes static business event data to help you examine the DQL shown in the following examples.

The Dynatrace Query Language (DQL) we use to monitor the production line process generates a tabular report detailing the progress of individual orders in five distinct steps.

The DQL for the five-step production process is relatively simple, using order_ID within the summarize command to correlate all five steps.

Next, we can consider the case of a major online gaming company; their goal is to minimize the time between a win notification and the corresponding payout to keep the player engaged. Their use case is more complex due to different correlation IDs. Despite the differing IDs, we can use a simple DQL query to measure the delay between a win notification and the associated online payment. With this simple process monitoring solution, the company can manage its commercial commitment to keep the payment delay below a predefined threshold.

For this use case, the DQL request needs two successive summarize commands:

• The first summarize command uses runID to correlate bizevents 2 and 3 in a record.
• The second summarize command correlates the record with bizevent 1 based on the betID

Service level objectives (SLOs) and workflow automation for business processes

Monitoring the payment process provides real-time insights into performance and anomalies. The next step is to formalize the goals by defining a service level objective (SLO) and an error budget policy. We’ll add the SLO function to the mix, with the goal of 99% compliance to an eight-minute service level.

We can add the following SLO calculation to our previous query to measure compliance:

To configure your first Site Reliability Guardian, add the app from your tenant and follow the Get started guide.

All that remains is to set up a workflow that, based on the SLO, will trigger notifications to your service management solution and to your business teams.

See more Workflow automation use cases by visiting Workflows in Dynatrace Hub.

Automated business processes are the heart of modern organizations. IT operations and business teams benefit from a shared view of process health, using business KPIs as the primary metrics. Dynatrace makes monitoring, analyzing, and optimizing business processes easier than ever.

For a closer look at Business Flow, see this App Spotlight, or contact your Dynatrace representative.

The post Practical business process monitoring for real-time business observability appeared first on Dynatrace news.

Growing awareness and increasing regulatory scrutiny have propelled carbon emissions data into the public consciousness. How will your organization respond to this global challenge? How can you reduce the carbon footprint of your hybrid cloud?

A structured approach

Reducing carbon emissions involves a combination of technology, practice, and planning. Evaluating these on three levels—data center, host, and application architecture (plus code)—is helpful. Options at each level offer significant potential benefits, especially when complemented by practices that influence the design and purchase decisions made by IT leaders and individual contributors.

Level 1: Data centers

This is the starting point for most organizations. There are some big moves possible here that are easy to understand, though not necessarily easy to implement. If you’re running your own data center, you can start powering it with green energy purchased through your utility company. This is a rather simple move as it doesn’t directly impact your infrastructure, just your contract with your electricity provider.

The complication with this approach is that your energy bill will likely increase. The law of supply and demand dictates green energy prices, and even though renewable energy is often cheaper to produce, the demand exceeds supply.

Next, we consider possible energy savings in the data center. You might optimize your cooling system or move your data center to a colder region with reduced cooling demands. Most approaches focus on improving Power Usage Effectiveness (PUE), a data center energy-efficiency measure. A PUE of 1.0 is the unattainable perfect score, meaning that all power is used for computing, and none is used for other purposes such as cooling or lighting. The average PUE for data centers is about 1.8; energy-efficient data centers—cloud providers—achieve values closer to 1.2.

Is the solution to just move all workloads to the cloud? Unfortunately, it’s not that simple. There might not be enough cloud capacity where you need it. Application architectures might not be conducive to rehosting. Data sovereignty regulations might constrain your hosting options. So, it’s time to consider the next level of optimization.

Level 2: Hosts and containers

You might run thousands of hosts and containers, many of which have been sized by prioritizing performance over energy consumption. How many of these have been over-provisioned? How many sit idle most of the time? Right-sizing and consolidating (or retiring) over-provisioned and idling hosts and containers represent two big opportunities for reducing energy consumption and carbon emissions. Of course, you need to balance these opportunities with the business goals of the applications served by these hosts. To illustrate how tricky this balancing act can be, here is an anecdote from our own internal effort to reduce our carbon footprint at Dynatrace:

Klaus: “Hey Thomas, we’ve identified this host as idling for the last month; nothing is happening on it. Can we shut it down?”

Thomas: “Not so fast, Klaus; this host is part of our Synthetic Monitoring node cluster. For failover and load SLA reasons, we require at least three nodes at every synthetic location. So you’ll have to look elsewhere for energy savings!”

Another anecdote comes from one of our customers. After identifying about 100 idle host instances to be shut down, they learned that these hosts were provisioned in anticipation of upscaling to support an upcoming major sales event.

These experiences illustrate a key point: Identifying instances to be right-sized or shut down is an easy—and good—first step, but you need to understand the business context to make informed decisions. And while these examples were resolved by just asking a few questions, in many cases, the answers are more elusive, requiring real-time and historical drill-downs into the processes and dependencies specific to each host.

From here, it’s time to consider the next level of energy optimization, green coding.

Level 3: Green coding

The topic of carbon reduction in data centers was new to me when I began digging into it just two years ago. It quickly became a “back to the future” experience for me. Starting with data center capacity and host rightsizing, it quickly became apparent that optimizing applications and their underlying source code was the responsibility of architects and engineers.

The Application Performance Management (APM) best practices we recommend to optimize user experience and application performance—driven initially by on-premises workloads and finite compute capacity—contribute to improved computational efficiency by reducing CPU cycles, optimizing inter-process communications, and lowering memory footprints. This computational efficiency also reduces energy consumption, which in turn reduces carbon emissions. Many of the same principles can be applied to green coding. A few examples:

• Reduce roundtrips between services (for example, the N+1 query pattern).
• Reduce the volume of data volumes requested from databases (for example, request all, filter in memory).
• Reduce inter-process communications overhead.
• Implement appropriate caching layers (for example, read-only cache for static data).
• Implement intelligent retry and failover processes.

For a deeper look into these and many other recommendations, my colleagues and I wrote an eBook about performance and scalability on the topic. We encourage you to take a look.

The sustainability community now refers to these best practices as “green coding;” in conjunction with green operations and green requirements engineering, green coding results in green software.

Dynatrace Carbon Impact app

The missing piece of the puzzle, at least for Level 2: Hosts and containers and Level 3: Green coding, is granular and actionable visibility. Cloud providers offer coarse carbon footprint tools that are designed for compliance reporting, not optimization. And data-observability solutions focus on performance, not carbon emissions.

In January 2023, Dynatrace released the Carbon Impact app, adding carbon emissions and energy consumption metrics to observability data. Dynatrace Smartscape® automatically adds real-time dependency and architectural context, from hosts to processes to software services. Carbon Impact can support your level 2 and level 3 optimization initiatives with the granularity and insights needed to reduce your carbon footprint without sacrificing your availability, performance, customer experience, and cost containment goals.

Want to learn more? Watch this Carbon Impact Observability Clinic recording to see Carbon Impact in action.

The post Sustainable IT: Optimize your hybrid-cloud carbon footprint appeared first on Dynatrace news.

A business process is a collection of related, usually structured tasks or steps, performed in sequence, that achieve a defined business goal. Tasks may be manual or automatic, and many business processes will include a combination of both. Business processes are important because they improve the efficiency, consistency, and quality of the business outcome.

Monitoring business processes is one thing organizations can do to help improve the key business processes that enable them to provide great customer experiences. Business process monitoring refers to continuously tracking and analyzing key performance indicators (KPIs) from relevant process milestones. The goal of business process monitoring is to provide real-time visibility into the performance of a business process, including end-to-end delays so that stakeholders can quickly identify and respond to issues as they arise, and drive business outcomes that will deliver a better customer experience.

Benefits of business process monitoring

Business process monitoring enables organizations to understand how an end-to-end process is performing and pinpoint where in the process they can make improvements. Business process monitoring helps organizations:

• Increase efficiency by identifying and addressing bottlenecks or inefficiencies that may slow down a business process.
• Improve quality control by tracking KPIs and identifying areas where quality may be lacking to enable corrective action.
• Improve customer satisfaction by improving processes to provide customers with faster and higher quality service.
• Make better decisions by providing managers with real-time data about the business.
• Reduce costs. Organizations can reduce their operating costs and increase their bottom line with more efficient and better processes.

Digital transformation increases the importance – and challenges – of business process monitoring

Most organizations rely on many business processes across different departments to meet their goals and deliver outcomes to their stakeholders, whether customers, partners, or employees. As organizations continue to undergo digital transformations, more and more of their business processes are dependent on increasingly complex digital technologies. In fact, most business processes still rely on multiple systems, long ago outgrowing the capabilities of built-in platform-centric monitoring. This leaves business leaders with incomplete visibility into these critical business assets.

Approaches to achieve end-to-end visibility have met limited success, in part due to these common challenges:

• Business data is scattered across multiple systems, with inconsistent formats and different means of access. Code changes are often required to expose important business data, delaying implementation. Fragile data pipelines introduce ongoing maintenance overhead.
• Data at rest, the source for many pipelines, can be stale, often by days or longer, preventing real-time business agility.
• Business data lacks connection to the supporting IT context, limiting effective collaboration between business and IT teams. Consequences include delayed anomaly detection and missed optimization opportunities.

The results can include delayed, incomplete, and disconnected views of business process health, impacting a range of stakeholders:

• Business leaders lack real-time visibility into some of their most important assets.
• IT teams lack insight into how the systems they manage impact business outcomes.
• Customers experience product and communication delays.
• Opportunistic competitors encroach on market share.

Traditional approaches to business process monitoring must evolve to provide the end-to-end visibility, IT context, and timeliness that organizations need.

Business process monitoring examples: A retail use case

It can be helpful to put business processes in the context of a specific type of business or industry. For example, consider three common retail industry business processes that must work together efficiently to meet customer expectations:

• Inventory management to anticipate and meet dynamic customer demand
• Order processing workflow triggered by customer orders
• Order fulfillment to deliver orders to customers

Anomalies or delays anywhere in these business processes can impact customer satisfaction and revenue – yet we know these anomalies will occur.

• Without timely inventory data, business owners may be unable to respond in real time to supply chain issues or sudden shifts in customer behavior.
• Without IT context, business owners may not be able to identify the root cause of order processing failures.
• Without viewing the business process as an asset, IT teams will have little understanding of business process health, instead relying on isolated system health.

While the examples above illustrate one industry, it’s easy to see how similar processes or flows would extrapolate to other industries, such as a financial transaction workflow, applying for a loan, or booking and checking in for a trip.

How Dynatrace helps improve business process monitoring

Business Observability helps organizations across all industries gain visibility into their business processes, overcoming many of the challenges outlined above. With Dynatrace, organizations benefit from one AI-powered data platform for unified observability, security, and Business Observability, providing real-time observability for data-driven business decisions. The ability to capture business data from anywhere, whether that’s OneAgent, real user monitoring sessions, log files or external tools and data sources, simplifies access to the real-time, precise business data organizations need for better visibility into their business processes.

With access to topology metadata to automatically enrich business events, Dynatrace enables a straightforward approach to business process monitoring that goes beyond traditional methods, putting individual business process steps in context with business and IT data. Organizations can visualize an entire business process through a purpose-built Business Flow application or a custom dashboard, leveraging Davis AI for root cause analysis and Smartscape to drill down into host and process details.

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The post Improving customer experience with business process monitoring appeared first on Dynatrace news.

In part 1 of this blog series, we explored the concept of business observability, its significance, and how real-time visibility aids in making informed decisions. In part 2, we’ll show you how to retrieve business data from a database, analyze that data using dashboards and ad hoc queries, and then use a Davis analyzer to predict metric behavior and detect behavioral anomalies.

Dataflow overview

Dynatrace ActiveGate extensions allow you to extend Dynatrace monitoring to any remote technology that exposes an interface. Dynatrace users typically use extensions to pull technical monitoring data, such as device metrics, into Dynatrace.

However, as we highlighted previously, business data can be significantly more complex than simple metrics. To accommodate this complexity, we created a new Dynatrace extension.

Create an extension to query complex business data

Creating an ActiveGate extension with the Dynatrace extension framework is easy; there’s a tutorial on using the ActiveGate Extension SDK that guides you through making an extension to monitor a demo application bundled with the SDK.

Similar to the tutorial extension, we created an extension that performs queries against databases. Notably, the SQL query is not limited to specific columns or data with specific metric values (int or float). Instead, the data can be of any type, including string, Boolean, timestamp, or duration.

There are three high-level steps to set up the database business-event stream.

1. Create and upload the extension that connects to the database and extracts business data in any form.
2. Configure the extension with the appropriate database credentials, query names, Dynatrace endpoint, and tokens necessary to send the business data to Grail.
3. Once the data is received in Grail, you can explore, manipulate, and analyze the data, utilizing advanced techniques such as filtering, grouping and aggregation, calculations and transformations, time windowing, and much more. Further, you can set alerts based on predefined or auto-adaptive thresholds.

Step-by-step: Set up a custom MySQL database extension

Now we’ll show you step-by-step how to create a custom MySQL database extension for querying and pushing business data to the Dynatrace business events endpoint.

Create and upload the extension

1. Download the extension ZIP file. Don’t rename the file. This is a sample extension that connects to a MySQL database and pushes business events to Dynatrace.
2. Unzip the ZIP file to the plugin deployment directory of your ActiveGate host (found at /opt/dynatrace/remotepluginmodule/plugindeployment/).
3. In the Dynatrace menu, go to Settings > Monitored technologies > Custom extensions and select Upload Extension.
4. Upload the ZIP file.
5. Once uploaded, extract the ZIP file at the same location.
6. Configure the information needed to query business observability data from the target database.
   There are three configuration sections, as shown below in the Dynatrace web UI.

Configuration details

Database configuration

• Endpoint name: Any label to identify this connection. This is used for identification purposes.
• SQL IP/Hostname: The database IP or hostname.
• SQL Username: Username of the user who has permission to login on the SQL server remotely and access the database.
• SQL Password: Password for the username.
• SQL DB: The database name.

Bizevents API and token configuration

• Endpoint to Push Bizevents: Bizevents API that will receive the business data.
  • Replace tenantid with your tenant ID
• Client ID to generate token: Client ID used to generate OAuth token. To generate client-id, refer to our OAuth documentation.
• Client secret to generate token: Client secret for token generation.

Define your SQL Queries

• Queryname 01: Unique name to identify the query to ensure data identification and retention within Dynatrace.
• Query 01: SQL query to retrieve data.
• Interval 01: Frequency in minutes for executing the configured query.
• Add multiple queries (depending on the requirement) with the above config for each query.

Define the retention period with matcher DQL and bucket assignment

Data stored in Grail can be preserved for extended periods, up to 10 years. To achieve this, we’ll create a Grail bucket specifically designed to retain data for a duration of 10 years (3,657 days).

Here is a JSON response from an API that successfully created a bucket capable of storing data for a period of up to 10 years.

After obtaining a bucket with a suitable retention period, it’s time to create a DQL matching rule that effectively filters events and directs them to the appropriate Grail bucket. This ensures that the data is retained for the correct duration while restricting access to users who are authorized for that specific bucket.

Analyze the data in real-time using Dashboards or collaborate with colleagues using Notebooks

In the screen recording provided below, we begin by examining the business data ingested into Grail using a notebook. This initial overview provides a broad perspective of the ingested data. However, real insights emerge when we delve deeper and analyze specific events over time. As you follow along in the video, you’ll notice the ability to determine the day of the week for each transaction and visualize the data in a user-friendly bar chart.

The video below showcases a business dashboard that effectively visualizes important events, including pending withdrawals and deposits from the past hour, transaction amounts throughout the week, transaction queue status from the previous hour, and the overall transaction status.

Enhance data insights with real-time ad hoc queries

While predefined dashboards can offer comprehensive overviews, they don’t always anticipate and meet the needs of business analysts. Dynatrace Query Language (DQL) is a powerful tool for exploring your data and discovering patterns, identifying anomalies and outliers, creating statistical modeling, and more based on data stored in Dynatrace Grail. Now we’ll use a Dynatrace Notebook to execute our DQL queries.

In the below query, we’re specifically searching for pending deposit transactions greater than $8,000 that occurred between 10:00:04 AM and 12:00:00 AM on August 21, 2023. The query for pending deposit transactions within a specific time frame is useful for real-time analysis, issue investigation, performance assessment, impact assessment, and compliance/auditing purposes.

Proactive alerting for accumulating business transactions: Mitigating business impact

To ensure timely action and address potential bottlenecks, we can set up alerts that notify you when pending transactions accumulate within a short period. These alerts serve as early business warnings, allowing you to take necessary measures to prevent disruptions and minimize delays in transaction processing.

In the above recording, we demonstrate an alert specifically designed to notify when there is a significant increase in pending transactions. This alert serves as a valuable tool in maintaining operational efficiency, ensuring business continuity, and delivering optimal customer experiences.

Forecast business data Using a Davis analyzer

In the context of monitoring business-related data such as sales, orders, payments, withdrawals, deposits, and pending transactions, Dynatrace Davis analyzers offer valuable forecast analysis capabilities. Davis analyzers offer a broad range of general-purpose artificial intelligence and machine learning (AI/ML) functionality, such as learning and predicting time series, detecting anomalies, or identifying metric behavior changes within time series.

By utilizing a Davis analyzer, organizations can predict future trends and patterns in their payment and transaction data. This forecast analysis helps businesses anticipate customer behavior, plan for fluctuations in transaction volumes, and optimize their operations accordingly.

For example, by applying forecast analysis to payment data, businesses can identify potential cash flow issues or predict periods of high transaction activity. This type of insight enables you to proactively manage liquidity, ensure sufficient funds are available, and make informed decisions about resource allocation.

Conclusion

By combining proactive alerts and leveraging AI-powered insights, we can effectively manage pending transactions, optimize processes, and ensure smooth operations.

To address the business need for extracting business data from databases, we demonstrated using a custom database extension to bring the data into Dynatrace. This integration allows seamless connectivity to a variety of databases, enabling the real-time retrieval and storage of business data.

By leveraging the powerful combination of business, security, and observability, organizations gain immediate access to their critical business data without any delays or data staleness. The real-time nature of the data extraction ensures that decision-makers have up-to-date information at their fingertips, empowering them to make timely and informed decisions.

Furthermore, we showcased the flexibility and versatility of the Dynatrace platform in exploring and analyzing the extracted data. By seamlessly integrating the data into Notebooks and Dashboards, organizations can gain comprehensive insights into trends, patterns, and key performance indicators relevant to their business. This empowers data analysts and business users to delve deep into the data, uncover valuable insights, and derive actionable intelligence.

Additionally, we demonstrated the power of custom alerts in Dynatrace. By defining specific thresholds for key business KPIs, the platform can proactively monitor data and generate alerts whenever a breach or potential issue is detected. This proactive alerting capability ensures that stakeholders are promptly notified of any anomalies or deviations, enabling them to take immediate corrective actions and mitigate risks. More advanced use cases integrate with automation workflows to automate recovery actions.

Through seamless database connectivity, real-time data retrieval, exploratory capabilities, proactive alerting, and automation, organizations can enhance their overall operational efficiency, customer satisfaction, and business performance. The integration of the Dynatrace observability platform with the custom database extension provides organizations with a solution to extract, analyze, and act upon their at-rest business data, driving success in a rapidly evolving business landscape.

The post Extend business observability: Extract business events from online databases (Part 2) appeared first on Dynatrace news.

Business leaders benefit from in-the-moment business insights. They frequently articulate the need for real-time visibility into business data to support agile business decisions. But existing business intelligence (BI) tools often lack the broad context, ease of data access, and real-time insights needed to understand and improve customer experience and complex business processes.

The key challenges include:

• Business data is often difficult to access, resulting in fragile data pipelines.
• Data is not delivered in real time; it’s often delayed by weeks or longer.
• Business data often lacks IT context, which prevents effective BizOps collaboration.

Dynatrace business events address these systemic problems, delivering real-time business observability to business and IT teams with the precision and context required to support data-driven decisions and improve business outcomes.

Dynatrace business events provide precise, real-time business metrics that support fine-grained business decisions and auditable business reporting. They offer lossless access to hard-to-reach business data embedded in in-flight application payloads, ensuring that valuable information is not missed. Additionally, Dynatrace business events enable organizations to explore and analyze large, long-term data sets without pre-indexing, which allows for flexible and comprehensive data analysis.

Extend business observability to data at rest

In our past blog post about business agility, we looked at a retail sales use case example to investigate potential causes of underperforming store locations. We also looked at a pizza chain example, connecting each customer order to the fulfillment process milestones that followed, including the handoff to the delivery agent.

In both examples, we used Dynatrace OneAgent® deep payload inspection to capture business data in motion. There are also many cases where business data—transactional, inventory, or financial—is at rest or in use, stored in a database. For comprehensive business observability, you need access to this data in real time. This can be accomplished using Dynatrace extensions. Dynatrace extensions can easily query data from various databases and store the results in Grail™, the Dynatrace data lakehouse. Once the data is in Grail, it can be transformed, queried, reported to dashboards, and more.

Business data is more than metrics

Dynatrace Extensions enable the expansion of Dynatrace monitoring to encompass any technology that provides an interface. For instance, the SQL datasource facilitates universal database queries across commonly used databases, subsequently transmitting the results to Dynatrace in the form of metrics or logs.

However, in the real world, business-related data isn’t limited to metrics. Business data should be viewed through a different lens, storing it separately while preserving the unique characteristics that enable business observability:

• Certain business data, such as product names, customer details, sentiments, order dates, payment methods, and more, are not simple metrics. Instead, they can consist of various data types: strings, integers, float, timestamps, and combinations of values.
• Such business observability can’t reside in traditional databases or data warehouses and thus needs to be in a data lakehouse that can unify and contextually analyze observability, security, and business.
• Metrics lack the contextual information to automatically trigger actions such as targeted outreach to impacted customers or automations to remediate process anomalies. Business events, however, capture specific occurrences or actions, allowing organizations to understand triggers, respond promptly, and foster collaboration among teams for improved customer experiences and business outcomes.

To get past the basic metric limitations, we created a custom extension to extract business data from existing databases and store it in Grail. Here’s a peek at the approach:

Business observability

Business observability refers to gaining insights into a business’s operation, performance, and behavior in real time. It involves collecting and analyzing data from various sources within an organization, such as IT systems, applications, customer interactions, and business processes, to gain a comprehensive view of how the business is functioning. An effective business observability solution should make it easy to ingest business data from any source, including databases.

Similar to the concept of observability in IT systems and applications, business observability focuses on capturing data at different layers of the business and making it easily accessible and understandable for analysis and decision-making. It goes beyond traditional business intelligence by providing real-time, granular, and contextual data that enables organizations to identify patterns, trends, anomalies, and correlations across different business dimensions.

Illustrating the value of business observability

Business observability helps you understand and evaluate the performance and effectiveness of systems in achieving their intended business goals. While observing individual requests is essential for performance engineering purposes, taking a business lens perspective provides deeper insights into the actual value delivered by the underlying system.

For example, consider an e-commerce website aiming to maximize sales. By implementing business observability, you can analyze conversion rates, sales patterns, and order fulfillment times. This enables you to identify bottlenecks, optimize user experiences, and make data-driven decisions to improve sales performance.

Similarly, in the case of a ride-sharing app, business observability allows you to monitor metrics like ride acceptance rates, driver and rider satisfaction, and average wait times. By analyzing these business-oriented indicators, you can optimize an app’s algorithms, allocate resources effectively, and enhance the overall experience for both riders and drivers.

For an insurance provider, business observability provides insights into key metrics such as policy sign-ups, claim processing times, and customer satisfaction levels. By closely monitoring these business-focused metrics, you can identify areas for improvement, streamline processes, and deliver better service to your customers.

Business observability not only ensures that systems perform well technically, it also ensures that systems are aligned with their intended business objectives. By gaining visibility into the business value delivered by these systems, you can make informed decisions, optimize performance, and ultimately achieve your business goals more effectively.

The post Extend business observability: Extract business events from online databases (Part 1) appeared first on Dynatrace news.

Digital experience is often considered the most important customer-facing aspect of digital commerce. This is typically the first thing that comes to mind for IT professionals working in the retail industry when evaluating holiday readiness. While digital experience has many facets, transaction speed usually ranks among the most important. Almost two decades ago, a Google experiment showed that fast-loading transactions are more important to customers than content quality—even small increases in transaction delay result in substantially more abandoned sessions. That lesson remains important. (Though the three-second rule for page load time is often misinterpreted).

CEOs of hybrid retailers prioritize e-commerce growth over in-store shopping, investing heavily in their online storefronts. IT teams spend months preparing for the peak traffic they anticipate will arrive with holiday shopping. However, this is a dynamic target; shopping behaviors are increasingly unpredictable, customer expectations continue to rise, and fierce competition makes cultivating loyalty more challenging than ever. These challenges can be summarized by this quote, paraphrased here from Adobe’s 2021 Digital Trends report: “Your customers are digital, unpredictable, and easy to lose.”

From first to lasting impressions

But there’s more to digital experience than speed. Digital experience, measured by fast, frictionless user journeys, paints an incomplete picture, tracking just the beginning of the customer relationship. What happens after the conversion creates a lasting impression with a larger impact on loyalty and your business.

Let’s shift our focus to the backend systems and business processes, the behind-the-scenes heroes of end-to-end customer experience. These retail-business processes must work together efficiently to orchestrate customer satisfaction:

• Inventory management ensures you can anticipate and meet dynamic customer demand.
• Order processing workflow is triggered by customer orders.
• Order fulfillment is the packaging and delivery of orders to customers.

From a customer perspective, the nuances of these business processes are uninteresting as long as they work. Increasingly, however, order fulfillment is a differentiating customer-facing aspect of the end-to-end customer journey, often with digital touchpoints woven into the experience. The fulfillment clock starts ticking the moment a customer purchases your product. Yet fulfillment is often an area over which retailers have little visibility or control.

Customers value real-time visibility into order status and delivery tracking. However, these fulfillment processes are often strained under the pressure of increased online shopping, next-day delivery expectations, and environment-friendly choices. Flexible delivery options, including “buy online, pick up in store” (BOPIS), curbside pickup, self-service lockers, and gig economy delivery require even greater real-time coordination to commit to competitive and narrowing delivery windows. Decentralized last-mile delivery strategies such as micro-fulfillment centers complicate inventory management and order fulfillment oversight.

Technology to the rescue?

Solutions such as inventory management, order management, and delivery optimization can introduce new challenges:

System integration. To effectively leverage multiple systems to manage orders, inventory, and logistics, retailers must invest in often complex integrations. Unsynchronized and siloed data prevents real-time decision-making and business automation.

Multi-channel logistics. Most retailers work with multiple carriers to handle deliveries, resulting in disparate tracking systems. Aggregating tracking information and presenting it to customers in a uniform way can be a challenge.

Real-time updates: Customers expect real-time visibility into fulfillment milestones beyond order confirmation, including packing, shipping, and delivery notifications. Self-service tracking information, preferred by most customers, becomes especially difficult when there are delays or disruptions.

Embracing business observability

Successful retailers benefit from real-time insights into business processes across all milestones. While each system and service provider might adhere to SLOs, the end-to-end health of the process is greater than the sum of its parts. How can you discover optimization opportunities, patterns behind recurring disruptions, or the root cause of an anomaly? The answer lies in the context—connecting business process KPIs to system performance becomes the starting point for real-time business/IT collaboration and automated remediation. The resulting agility supports targeted responses to process disruptions, anomalies, and bottlenecks as they happen, not when daily or weekly reports are produced, not when your call center is inundated, not when your Net Promoter Score (NPS) plummets. To accomplish this transformation, IT teams need to expand their observability scope to include business KPIs.

How Dynatrace can help

Recent platform innovations have made monitoring end-to-end business processes such as order fulfillment easier. Consider these requirements for effective business observability.

• Business data must be accurate to instill the confidence to make business decisions.
• Business data can come from many sources, including OneAgent, RUM, external business systems, and log files.
• Business data must be easy to access without modifying code to reduce the burden on development and maintenance resources.
• Business data must remain granular over long retention periods to support long-running business processes and “needle in the haystack” queries.
• Business data must be unified, regardless of the source or data type.
• Business data must be easily queried to answer unanticipated questions without upfront indexing.

Business events deliver real-time business observability to business and IT teams with the precision and context to support data-driven decisions and improve business outcomes. Business events extract critical business data from your IT systems with lossless precision and can illuminate dark data quickly and easily, wherever that data exists.

Order fulfillment process example

Retail order fulfillment is a good example of business process monitoring, a use case enabled by these innovations. Fulfillment processes vary between retailers, with subprocesses that might introduce branches and loops. It’s a good practice to identify process milestones as a starting point; these should be relatively consistent. For example:

1. Purchase confirmation
2. Order picked from the warehouse
3. Shipping label created
4. Order accepted by the delivery agent
5. Delivery confirmation
6. Survey completed

Once you’ve defined the list of milestones, identify where to capture the data.

• Purchase confirmation: E-commerce platform (via OneAgent)
• Order picked: Warehouse management system (via OneAgent)
• Shipping label created: Warehouse management system (via OneAgent)
• Order scanned by delivery agent: Agent logistics system (via API)
• Delivery confirmation: Agent logistics system (via API)
• Survey: VoC solution (via API or database query)

The Business Flow app, developed using Dynatrace® AppEngine, makes it easy to configure business process milestones for an end-to-end view of process throughput, delays, and anomalies.

The post Customer expectations for retail: Beyond digital experience appeared first on Dynatrace news.

Managing a Dynatrace Platform Subscription (DPS) budget requires balancing your organization’s usage of Dynatrace capabilities against a pre-defined annual budget commitment. Without adequate flexibility in the subscription model, your organization might fail to benefit from capabilities that could transform your observability and security processes. Conversely, with too much flexibility, costs may exceed your budget.

Subscription administrators typically limit team access and capability usage to avoid billing surprises. This approach is time-consuming and requires ongoing management. Also, if limits are set too low, some critical components in your infrastructure might go unmonitored, potentially negatively impacting your business. While if limits are set too high, you might pay for more monitoring than you need and exceed your budget. While there is no penalty for exceeding your DPS budget (the same rate card is applied for on-demand usage without additional fees or premium pricing), administrators need the ability to manage their subscriptions within a planned budget.

Cost monitors notify you of changes to your forecast usage

Cost monitors offer a different approach to these challenges. By using predictive AI with smart forecast algorithms that predict usage, you’re notified whenever forecasted usage is projected to exceed your defined budget or when unexpected usage spikes occur.

Cost monitors run in the background daily, automatically monitoring usage forecasts and costs. They notify you when unusual forecasts and cost events occur so you can focus on monitoring your applications, not your subscription. Cost monitors automatically notify subscription administrators without manual setup or configuration. However, you retain full control over configured thresholds and notification paths if you desire to change them. They even provide APIs so you can integrate them into your monitoring ecosystem as needed.

Smart forecasting capabilities

Cost monitors leverage the smart forecasting capabilities of the Dynatrace Platform Subscription to predict your costs through the end of your subscription period. The budget summary includes a median forecast value with an upper and lower range for each day of your subscription into the future. By monitoring the forecast cost for the end of your subscription, you can proactively plan for growth and be notified of spikes in usage to stay on top of daily spending. Usage is forecast daily and you’re notified of significant changes or forecasts that exceed your defined threshold values. This allows you to plan and make changes accordingly.

Cost monitors also track daily costs at the capability and environment levels, alerting you when daily costs exceed predicted levels. There are no thresholds to manage for cost events; cost monitors are designed to ensure that all meaningful increases are caught and notified without generating false alerts. This predictive AI technique allows you to stay on top of daily costs and be notified of unexpected cost increases.

Cost monitors also generate email notifications for specified recipients when forecast and cost events occur. By default, you are notified of all forecast and cost events. License administrators can additionally define a specific email distribution list for these notifications. This approach ensures that a responsible team member is notified about all subscription-relevant events without any upfront configuration. Proactive alerting can be used to draw attention to unusual usage in non-production environments or an unexpected increase in use for a specific capability, allowing you to plan or manage accordingly.

What’s next

Dynatrace will continue to monitor and fine-tune the cost-event detection algorithms used by cost monitors to ensure that notifications are accurate.

If you’re already using the new Dynatrace Platform Subscription model (available as of April 26, 2023), cost monitors are already running on your account! For full details on how to get the most from them, please see our Cost monitor documentation.

Additional information about the Dynatrace Platform Subscription model is also available in our DPS documentation. If you’re interested in migrating to a Dynatrace Platform Subscription, please contact a Dynatrace account representative.

The post Avoid billing surprises with smart Dynatrace cost monitors appeared first on Dynatrace news.

Without native log support, overhead and complexity grow

OpenTelemetry, the Cloud Native Computing Foundation (CNCF) incubating project, introduced standards that enable companies to instrument, generate, and export telemetry data. When combined with out-of-the-box correlation, such telemetry data provides context-rich observability. Dynatrace has supported the OpenTelemetry project for years as a key contributor and contributed to its rise to a popular open source observability framework for cloud-native software. Many global enterprises have instrumented their code to emit traces, metrics, and logs in a standardized and vendor-neutral way using OpenTelemetry.

While ingestion of OpenTelemetry traces and metrics into Dynatrace is supported, companies often prefer to collect logs in the OpenTelemetry format. Earlier this year, OpenTelemetry announced that their logs API/SDK specification is stable, making it ripe for broader adoption. This enables unified observability because logs are indispensable for troubleshooting apps, monitoring infrastructure, auditing or investigating security incidents, tracking business events, and many other use cases.

Without such a holistic view of system behavior and performance, organizations typically need to invest in separate instrumentation and integration efforts for each telemetry type, which brings additional overhead, costs, and complexity.

Unify OpenTelemetry logs, traces, and metrics in Dynatrace

Dynatrace now includes full support for OpenTelemetry logs, which provides unified observability for organizations with vendor-neutral and open-source tech stacks. Our commitment to this open standard allows you to cover all three pillars of observability with minimal configuration effort because OpenTelemetry traces, metrics, and logs can be exported to Dynatrace using the same OTLP exporter.

By ingesting OTLP logs into Dynatrace, you can utilize the Grail™ data lakehouse and its massively-parallel processing analytics engine. This allows you to eliminate log forwarding and collection solutions, which not only add maintenance overhead and complexity but can also become bottlenecks in performance and log volume throughput.

With the added support of logs to OpenTelemetry traces and metrics, Dynatrace now gives you a unified and holistic overview of observability signals, with integrated linking of traces and logs. This enables you to connect the traces in your stack directly with root-cause information in logs. One customer recently shared why working with all telemetry signals together really makes sense for them.

Native support for OpenTelemetry (OTLP) logs also supports enterprises that have highly diverse technical architectures. While Dynatrace OneAgent^® is often the preferred way of discovering and ingesting logs from traditional hosts or Kubernetes environments, there are certain environments for which OneAgent is not a viable option. As an alternative, OpenTelemetry lets you extend Dynatrace technology coverage with log data.

Generic ingest of log data now works with OTLP

Ingesting OTLP logs is now supported via the OpenTelemetry Logs Ingest API. In SaaS deployments, you can use this approach to ingest log data into Grail and analyze it via Log Management & Analytics in your environment. The same API is also available for Log Monitoring Classic for Dynatrace Managed deployments (via an Environment ActiveGate).

All you need to do is configure your OpenTelemetry collector or any other OTLP log source to send logs to the OpenTelemetry Logs Ingest API endpoint.

Start using the full OpenTelemetry set today

• OpenTelemetry log ingest is supported in Dynatrace version 1.270 and Environment ActiveGate version 1.269
• Read more about OpenTelemetry logs in Dynatrace, ingest transformations and start ingesting logs over the API

The post OpenTelemetry logs in Grail unlock full observability appeared first on Dynatrace news.

More development teams across enterprises are adopting new mobile UI frameworks, namely SwiftUI, .NET MAUI, and Android’s latest toolkit, Jetpack Compose. While these frameworks use a declarative syntax to simplify the codebase and expedite development lifecycles, they also introduce new challenges in monitoring the user experience of mobile apps. As a front-runner for auto-instrumentation in the observability space, Dynatrace has rolled out support for these technologies to expand its coverage for mobile applications. With mobile RUM, developers and their teams who have migrated their codebase to these new UI frameworks can take advantage of the benefits of the Dynatrace platform, which enable them to quickly identify errors and facilitate their immediate resolution. This allows developers to focus more of their efforts on innovation and delivering the best user experience to their customers.

High consumer expectations for mobile

In today’s digital world, mobile apps have become an essential part of our daily lives. We use mobile apps to communicate, entertain us, conduct business, shop, and much more—on the go, anytime and anywhere. As people typically spend 4.8 hours a day on their mobile devices, it’s natural that they demand a flawless experience. Among all digital channels, mobile has the lowest tolerance for bad experiences. If an app is slow, slightly buggy, or doesn’t fulfill the user’s needs promptly, it gets deleted. In many cases, an alternative app is installed. Overall, 52% of users feel frustrated by their experiences with mobile apps.

New development frameworks from the key players

Apple, Google, and Microsoft, among others, are heavily invested in development tools and frameworks. Backed by strong communities, these tools are continuously enhanced and have redefined how apps are built. Incorporating the latest advancements in technology allows companies to develop and deliver more reliable applications, resulting in smoother user experiences. To ensure consistent progress in app development, it’s crucial to stay updated and integrate these innovations into your development process.

With the introduction of Jetpack Compose and SwiftUI, the development of mobile apps has become more accessible, efficient, and streamlined. These frameworks are based on declarative syntax, which allows developers to build native UI for Android and iOS, respectively, with ease and speed. As a result, modern mobile apps increasingly use Jetpack Compose and SwiftUI to deliver excellent user experiences. According to Google’s recent announcements, approximately 23% of the top 1,000 Android apps are built with Jetpack Compose, and these numbers doubled from last year.

The importance of observability for mobile app development

An essential aspect of improving the user experience on mobile is observability. In order to make informed decisions, business owners, UX designers, and developers require data—specifically, they rely on insights from their data on a large scale, ideally in a single location. Providers of mobile apps must be able to identify issues that users are experiencing quickly so that they can address them. They also need to assess and optimize the performance of both their apps and the back end services that support them. Finally, by gaining complete visibility into user behavior, app providers can improve user journeys and achieve better outcomes for their businesses.
A key aspect of observability is the monitoring agent that a mobile app is instrumented with. This is important because the data collected—including both its breadth, depth, and semantics—play a significant role in determining the value that can be derived from automated or ad hoc analysis.

Mobile observability challenges

Mobile agents are included and shipped with their respective mobile apps, making the selection of the right agent and its configuration crucial. Frequent changes necessitate the publication of new app versions, and successful updates are dependent on users manually updating their devices. Therefore, anything that is not monitored accurately or is monitored incorrectly leads to lengthy cycles of roll-out and adoption until accurate data and answers can be obtained.

Furthermore, companies often require time from mobile app development teams to add dependencies to their agents and configure them. However, the primary goal of these teams is to develop new features and improve existing ones, which means that monitoring user experience is often a secondary concern. Therefore, it’s crucial to minimize the time and complexity required to set up a mobile agent.

Modern development frameworks pose unique challenges when it comes to monitoring. Jetpack Compose and SwiftUI, in particular, allow developers to create UI components using declarative programming. With this approach, a developer describes the desired end result and lets the framework figure out how to update the user interface. This approach simplifies development but introduces new challenges in detecting and relating user interactions with corresponding functions and context. Recognizing on which screen a particular interaction happened and providing human-readable names automatically is challenging. However, such automation is key to ensuring that developers spend less time instrumenting their apps (and that Dynatrace users can more easily make informed decisions based on analyzed data).

Dynatrace extends auto-instrumentation capabilities

The Dynatrace platform offers the best observability coupled with the least required effort for monitoring your mobile channels. Dynatrace boasts industry-leading auto-instrumentation and auto-capture of Real User Monitoring data, regardless of whether it’s for troubleshooting, performance monitoring, or optimizing user experience and business outcomes. With auto-instrumentation, Dynatrace tackles the aforementioned challenges, offering a high level of visibility into the user experience of your mobile app with no manual effort.

Based on feedback from our development community and customers, we have expanded our auto-instrumentation support to include the following technologies:

• Jetpack Compose
• SwiftUI
• .NET MAUI

Dynatrace OneAgent^® now automatically captures user interactions, related web requests, crashes, and other vital app lifecycle information when these technologies are in use (detailed functional scope varies based on the technology in use). Hence, it has never been easier to monitor the user experience of modern mobile apps.

Jetpack Compose support

Starting with the Android Gradle plugin version 8.263+, Dynatrace OneAgent offers auto-instrumentation for Jetpack Compose UI components.

OneAgent creates user actions based on the UI components that trigger these actions and automatically combines the user action data with other monitoring data, such as web request information and crashes. Currently, to get this feature set activated, developers need to manually enable Jetpack Compose auto-instrumentation. For the Android Gradle plugin version 8.271+, the instrumentation will be enabled by default. At the moment we auto-capture various types of user interactions, including

• Clickables
• Toggleables
• Swipeables
• Sliders

For complete details about Jetpack Compose UI components and user action support, go to User action monitoring for Jetpack Compose documentation.

As mentioned, analyzing user experience and identifying the specific elements with which the user interacts can be more challenging with a declarative UI. This is especially true when it comes to providing proper names and context. With the addition of Jetpack Compose support, we have developed a new method for detecting user action names. Dynatrace now evaluates four properties to generate meaningful user action names. The property value that has the most intuitive meaning is used as the name. A special sensor captures semantic information and evaluates the information from the merged semantics tree. This evaluation occurs in the following order:

1. SemanticsPropertyReceiver.dtActionName
2. SemanticsPropertyReceiver.contentDescription
3. SemanticsPropertyReceiver.text
4. Class name

With this approach, companies that use semantic properties appropriately also benefit from a better understanding of user journeys in Dynatrace user sessions.

Another key addition that helps make sense of user interactions is the auto-capture of additional metadata based on the UI component used. Metadata is stored as key-value pairs and is accessible through the Dynatrace web UI. With this additional context—for example, location in code, initial and transition states, interaction types, and more—Dynatrace makes sense of the user journey and the technical components in use. So, not only UX but also developers can retrieve the information they need to optimize user experience.

Based on each UI component, Dynatrace also automatically captures relevant metadata, including:

• The function name to make it easier to find the code behind each interaction
• The user action type, to make it easier to understand the user journey in the User session view
• And a state (from/to) to make it easier to understand the evolving state of each UI component

For full details, see Captured component metadata documentation.

SwiftUI support

Initial support for SwiftUI controls was released with OneAgent for iOS version 8.249+. With OneAgent for iOS version 8.265+, the auto-instrumentation capabilities of SwiftUI were significantly expanded to include:

• New controls and views
• SwiftUI methods(for example, onTapGesture)
• Lifecycle monitoring

SwiftUI is a declarative UI framework. Therefore, its instrumentation and monitoring pose additional challenges.

The Dynatrace SwiftUI instrumentor adds additional code to the project source code (*.swift files) during the build process in order to enable the auto-capture of UI elements. After the build process is complete, all changes to the source code are reverted.

Auto-capture support has been expanded. For details regarding which SwiftUI controls and views are supported, see Instrument SwiftUI controls documentation.

At the moment Dynatrace auto-captures various types of user interactions, including

• Buttons
• Pickers
• Sliders
• NavigationLink
• Tabview

In addition, Dynatrace now supports the instrumentation of some SwiftUI methods. When a supported method is closed, Dynatrace collects the method name, the type of view the method was attached to, and the parent view name.

Finally, Dynatrace is now capable of tracking key lifecycle events for SwiftUI views, including:

• Application start
• onAppear (for all SwiftUI views presented from a NavigationLink or a TabView control)
• onAppear (for revisited SwiftUI views presented from a NavigationLink or a TabView control)

.NET MAUI support

Starting with Dynatrace version 1.265+, our dedicated NuGet package helps auto-instrument your .NET MAUI mobile application.

.NET MAUI is a cross-platform framework for creating native mobile apps and more. It’s a combination of open source code and the evolution of Xamarin.Forms that enables developers to use a single code base to cover as much of app logic and UI layout as possible. The repo has been starred by ~19,000 users and receives regular updates and pull requests.

The Dynatrace NuGet package is used to instrument your mobile applications with OneAgent for Android and iOS. The supported feature set is similar to Xamarin.

Auto-instrumentation support is available for:

• User actions
• Lifecycle events
• Web requests
• Crashes

…plus, a wide range of options for manually instrumenting your app.

Conclusion

Regardless of whether you have a Dynatrace SaaS or a Dynatrace Managed deployment, update OneAgent for Mobile today to leverage the latest observability innovations and benefit from immediate visibility and fast value-creation with the unique end-to-end visibility Dynatrace provides!

The post Dynatrace RUM expands coverage for modern mobile UI frameworks appeared first on Dynatrace news.

Optimize cost and availability while staying compliant

Observability data like logs and metrics provide automated answers, root cause detection, and security issues.

Customer decisions about data retention are often determined by important security, privacy, and legal issues. Customers must comply with internal and external policies and regulations that might demand them to keep specific data stored for a minimum period of time (for example, audit logs). However, the opposite is also true—in some cases data must be deleted after a certain period of time. This is the case when a company no longer has legal grounds to retain its customer data, as outlined in privacy protection regulations.

Often customers make business decisions about data retention based on the value they get from keeping historical data and the associated data retention costs. This means compromising between keeping data available as long as possible for analysis while juggling the costs and overhead of storage, archiving, and retrieval. For example, suppose data has to be retained for a longer period because of legal or business reasons. In such a case, the data is archived in cold storage where it can only be accessed for analysis following a delay, re-ingestion into a log analysis tool, and reindexing to prepare the data for analysis.

Ultimately this leads to a lose-lose situation for customers—they have to pay for and maintain data storage but they can’t get answers from their data quickly and effortlessly when needed.

Grail gives you control and the answers from data

With Grail, Dynatrace provides control over data retention and access policies for granular portions of data called “buckets.” This allows you to design data management and retention policies based on individual requirements, starting from days of retention up to a decade.

By introducing control over data retention, Dynatrace doesn’t impose any additional complexities. Even with the flexibility of buckets, there is no additional overhead of data storage management, no archiving, no retrieval from archives, and no performance degradations when using retained data for answers.

The price of data retention is always transparent and uniform, based on the number of days the data is retained, with no hidden fees for managing data. The same applies to querying data with transparent pricing based on read-data volume, with no extra costs for querying older data.

Use buckets for any use case in a secure way

When using Log Management and Analytics or Business Observability with Grail, you can create custom buckets with specified data-retention periods. For example, you can route incoming log data to a specific bucket so selected team members can access it.

App developers might need to read logs from their environment for debugging purposes, but only for a specific timeframe. With Grail, it’s easy to create a bucket with ten days of retention time and provide all developers access to the data.

Infrastructure teams may need to work with host logs from recent months or quarters. To do this, infrastructure logs can be routed to a bucket with a retention period of three months to a year.

Local regulation often requires that security or audit logs be retained for 7 to 10 years. Such logs can be collected in a bucket with the required retention period, with only the security operations team having access to the logs.

A bucket can be wiped if, at any point in time, there is a need to delete the data stored in it. The reasons for this can vary from a changing business justification to data-privacy regulations. It’s also possible to extend or shorten a bucket’s retention period, which impacts how long existing data in a bucket is stored.

To support configuration-as-code for enterprise environments, creating, updating, and deleting data buckets in Grail is available through an API endpoint. This allows you to create new buckets, change the retention period of existing buckets, or delete buckets via an API call.

Bucket management follows a strict permission policy approach, where only users with corresponding permissions can create, update, or delete buckets. Every API call is saved in audit logs to document the complete picture of activities in your environments.

Get value from your data with the Dynatrace Grail today

• Sign up for a free trial account to start using Dynatrace today.
• Custom bucket support is available beginning with Dynatrace version 1.265; it will be rolled out gradually over the coming months to new and existing customer environments with Grail, and to free trial user accounts.
• Learn how Grail data lakehouse combines logs, metrics, traces, and Smartscape to power the Dynatrace platform and unlock boundless analytics
• Perform any analysis any time with Dynatrace Log Management and Analytics 

The post Stay in control of your data retention with Dynatrace Grail—from 10 days to 10 years appeared first on Dynatrace news.

As online services become more complex, end-to-end observability that spans the full customer journey, from request to fulfillment, is becoming harder to achieve. People now depend on digital experiences for access to goods, services, and entertainment. Consequently, organizations need a way to capture and understand user behavior so they can make their services more reliable.

For the British Columbia Lottery Corporation (BCLC), end-to-end observability has become imperative for understanding and quickly responding to customer experiences. BCLC is a government ministry corporation that provides lottery, casino, and sports betting services to benefit the province’s healthcare, education, and community programs. As such, the corporation’s mission is to deliver exceptional—and healthy—gambling entertainment experiences.

At Dynatrace Perform 2023, Ben Rushlo, Business Insights leader at Dynatrace, and Navid Mehdiabadi, BCLC’s APM expert, discuss how the right business insights are crucial to making data-driven decisions and improving business outcomes.

Business Insights is a managed offering built on top of Dynatrace’s digital experience and business analytics tools. The Business Insights team helps customers manage or configure their digital experience environment, extend the Dynatrace platform through data analytics, and bring human expertise into optimization.

Journey to data-driven decisions using end-to-end observability

BCLC has been partnering with Dynatrace since 2021. Mehdiabadi explains that, though BCLC’s strategic vision isn’t fully realized yet, its number one goal is to proactively manage incidents and provide increased visibility with end-to-end observability to improve the player and employee experience.

“It’s a journey in Dynatrace,” Rushlo said. “There’s a maturity journey, and it’s not just a maturity journey within the overall company. It’s even within app teams and different parts of your organization as they are implementing Business Insights”

Before Dynatrace, BCLC was considered a reactive company. “We relied on customers (our players) to call us and let us know if something was broken and had scattered monitoring tools,” Mehdiabadi says. “Each team had their own tools, and every time we had a failure, we had to go into the war room with each team looking into their own monitoring tools trying to come up with something that might be connected to the issue and find what’s broken.”

Accessing business insights and data with precision and long-term context

After working with Dynatrace, BCLC now has a twenty-four-seven data center team with an easy-to-share, intuitive datacenter hyper wall dashboard showing the overall health of the entire system — infrastructure, applications, networks, and user experience. Mehdiabadi says the company can now easily forecast both frontend and backend data to see everything that’s going on.

Monitoring both the frontend and backend is critical to achieving its strategic goals. “Our players just see the frontend. They go on the website and play the games,” Mehdiabadi says. “And that’s all they care about. Before Dynatrace, we didn’t have a view of what’s going on with them. But now, we can see the user behavior and get the information we need, like browser version, OS version, [and] IP address.”

When players call for assistance, customer support has all of that information in front of them in real time. “We can see the player’s journey,” he says. The BCLC support team has information on the player’s entry point, which path they went through, each pass to reach a certain destination, how well the application functions, and so on. “A lot has changed and it’s a lot better now that we have a detailed view of how our players behave in the system.”

Accelerating maturity with Business Insights

Partnering with Dynatrace Business Insights has resulted in on-demand, automated real user monitoring (RUM) and end-to-end observability of their high-value, big-money players, including key metrics, session replay, and monthly reporting. To accomplish this, the Business Insights team helps customers with three major items:

1. Configuring and managing the digital experience environment, such as setting up dashboards and making sure the right data is collected.
2. Troubleshooting issues, such as when a customer turns on real user monitoring but doesn’t get the right data (e.g., the right names or the right alerts).
3. Optimizing with expert knowledge from cross-vertical teams who understand why things are running slow, issues with content delivery networks, JavaScript, and backend calls.

“We also have some big data analytics use cases that help extend the Dynatrace platform, see how performance is affecting behavior, and identify long-term trends,” Rushlo said. “I would estimate 80% of our largest Dynatrace customers use Business Insights.”

End-to-end observability is central to BCLC’s strategic vision

BCLC’s beginning goal was successful monitoring and end-to-end observability for its online gaming business, helping it gain data-driven insights to improve the customer and employee experience. The company is now extending that vision to its lottery business and brick-and-mortar casinos. “We want to bring all of them into Dynatrace,” Mehdiabadi says. “It gives us end-to-end visibility and that single pane of glass for troubleshooting.”

BCLC has started onboarding developers into Dynatrace and monitoring additional services. “Everyone is very interested in using Dynatrace during their development,” he adds. “We’ve moved on to a champion concept that every team must have a Dynatrace champion if they’re interested in using Dynatrace for monitoring.”

Harnessing user behavior analytics for data-driven decisions and insights

With visibility into front-end performance and user behavior tied to end-to-end observability, Dynatrace provides quantitative and qualitative insight for actionable answers to improve customer experience and business key performance indicators (KPIs).

The post End-to-end observability provides deep insights into user behavior for British Columbia Lottery Corporation appeared first on Dynatrace news.

There are many definitions of environmental sustainability, most of which converge on a common theme. Collectively and individually, we have a responsibility to act to protect global ecosystems and support health and wellbeing, now and in the future. Sustainability seeks a balance between human activity and the natural world.

As a result, environmental sustainability is one of the three pillars of environmental, social, and governance (ESG) initiatives. ESG embodies a set of criteria that guides an organization’s strategy, measures the organization’s impact, and informs potential investors across these three pillars. Specifically, the environmental criteria focus on an organization’s utilization of natural resources and the resulting impact on the environment. McKinsey summarizes the importance of this focus. “Every company uses energy and resources; every company affects and is affected by the environment,” the article states. ESG metrics are increasingly important to investors as they evaluate risk. In turn, these metrics are increasingly important to organizations because they measure and disclose their performance.

Balancing act or tipping point?

What motivates us to act? The answer, to varying degrees, is favorable investment potential, regulatory requirements, competitive advantage, cost savings, and moral imperative. And the time to act is now. In fact, in an article for the Wall Street Journal, Gartner analyst Stephen White anticipates that organizations will rapidly adopt performance metrics tied to energy consumption. “Sustainability is increasingly becoming a board-level issue with broad companywide mandates flowing down from chief executives to tech leaders,” White says.

Regulatory mandates are another driver that organizations can’t ignore. As a result, the Securities and Exchange Commission (SEC) in the US and the European Parliament in the EU are adopting stricter reporting rules that will apply to a larger number of companies. Globally, the International Sustainability Standards Board (ISSB), established at the UN Climate Change Conference in Glasgow, has developed requirements for climate-related disclosure. Sustainability reporting informs investors about sustainability risks and helps focus a company’s sustainability actions.

Your IT carbon footprint

Corporate carbon footprint calculations consist of multiple facets, including transportation, waste management, fuel, and electricity consumption. To that end, measuring, understanding, and reporting results are important precursors to intelligent reduction actions.

From an IT perspective, the sustainability focus is on carbon emissions from electricity consumption, specifically related to on-premises, hybrid, and multicloud computing. Some interesting facts:

• Moving a workload to the cloud can reduce its carbon footprint by up to 96%.
• Cloud computing has a greater carbon footprint than the airline industry.
• Average cloud server idle time exceeds 70%.

Yet most organizations don’t have the tools to measure, much less reduce, their IT carbon footprint.

Measuring your carbon footprint: from intention to action

Cloud providers offer tools to measure carbon emissions from the use of their cloud services. But these tools don’t support multicloud environments, and they don’t account for the footprint of on-premises services. More importantly, these tools are fundamentally backward-looking. They lack both the time and dimensional granularity required to derive carbon-emission analytics and optimization insights. Environmental sustainability emphasizes our collective responsibility to take action. For this, we need real-time intelligence and analytics, not just historical reports.

As executive mandates reach technology leadership teams, your organization’s carbon reduction goals will become more tangible. As a result, you’ll need to shift from intent to action, from passive reporting to active reduction initiatives.

Introducing Carbon Impact

The Carbon Impact app from Dynatrace measures and reports the carbon footprint of all Dynatrace-monitored hosts across your entire hybrid and multicloud environment in a single interface. The app translates utilization metrics, including CPU, memory, disk, and network I/O, into their CO2 equivalent (CO2e). Dynatrace engineers developed the app using guidance from the Sustainable Digital Infrastructure Alliance (SDIA), expanding on formulas from Cloud Carbon Footprint.

With Carbon Impact, you can explore the sources of your IT carbon footprint, regardless of where your workloads run. The app automatically identifies opportunities to reduce carbon emissions, capturing the details needed to analyze and make informed decisions. It also generates granular reports and host-level details to provide a focus for carbon reduction initiatives.

The Carbon Impact dashboard: Your IT carbon footprint overview

The Carbon footprint summary reports total CO2e emissions for the selected and preceding timeframes for quick interval-based comparisons.

Optimization summaries report idle instances and under-utilized instances. The app derives thresholds that influence these calculations from Google Compute Engine recommendations, although you have full configuration control to adjust these thresholds to your needs.

The table breaks down emissions by data center, listing your cloud and on-premises instances. All your carbon footprint data is in one place, and the measurement methodology is consistent across all your data centers.

Two charts complete the dashboard. The first shows accumulated carbon footprint and energy consumption over time. As cloud providers decarbonize their data centers, or as you move your workloads to more carbon-efficient locations, the slope of the carbon emission trajectory should change to illustrate the impact. The second chart allows you to compare carbon emissions over time with a business metric of your choice derived from any of your configured business events.

Instances: Where should you focus your attention?

The Instances view details energy and CO2e consumption per host instance. Filters help narrow your focus. For example, you could view underutilized instances in a specific AWS data center or top CO2e emitters within a specific host group. Because Carbon Impact automatically connects to Dynatrace Smartscape® topology modeling, it’s easy to drill into host and process details. Or you can open Notebooks for ad hoc analysis, arming your teams with the insights needed to evaluate opportunities to reduce carbon emissions.

Carbon Impact and Dynatrace AppEngine

Carbon Impact tracks, reports, and helps you reduce the carbon footprint of your cloud and on-premises infrastructure. It also provides data center, host, process, and application details to help you understand where to focus your efforts. Moreover, it provides recommendations to help you get started quickly. Significantly, Carbon Impact supports multiple audiences, including compliance teams as a source for reporting, business teams that need investment guidance, and operations teams that need to act to reduce their carbon footprint.

Carbon Impact is a purpose-built app we created using the new Dynatrace AppEngine. It addresses a well-defined business need, bringing custom analytics to Dynatrace data stored in Grail. Carbon Impact is available now on the Dynatrace Hub. To learn more, please contact your account team.

The post From observability to sustainability: Reduce your IT carbon footprint with Dynatrace Carbon Impact appeared first on Dynatrace news.