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Dynatrace Introduces Digital Business Analytics

Dynatrace announced the addition of Digital Business Analytics to its Software Intelligence Platform.

Digital Business Analytics provides real-time, AI-powered answers to business questions using data already flowing through Dynatrace’s application and digital experience monitoring modules. By tying together user experience, customer behavior, and application performance data with business metrics, Digital Business Analytics provides real-time answers about conversions, orders, churn, release validation, customer segmentation and more. Traditionally, application owners and business users have been forced to go to disparate, siloed tools and manually analyze data, thereby hampering their ability to run and optimize their digital business offerings in real-time.

Available today, Digital Business Analytics joins APM, Cloud Infrastructure Monitoring, Digital Experience Management, and AIOps as part of the Dynatrace all-in-one Software Intelligence Platform. As with these other modules, Dynatrace’s AI engine Davis is at the core of Digital Business Analytics. Davis continually learns what expected “normal” business performance looks like and provides proactive answers to issues, enabling faster decision making, greater optimization of resources and better business outcomes.

As data volume and velocity accelerates, organizations are struggling to make sense of disparate dashboards from traditional IT monitoring tools, web analytics and ad hoc reporting. Dynatrace’s Digital Business Analytics automatically captures business data and analyzes it in context with user experience and application performance data. Furthermore, Digital Business Analytics complements existing web analytics tools to deliver real-time and complete results, by combining existing customer-facing channels with application and user experience data. Dynatrace’s full-stack Software Intelligence Platform shares a common data model across the platform, with AI and automation at its core, applying anomaly detection to business metrics to provide precise, real-time answers to business-critical questions.

Key pillars of Digital Business Analytics include:

- Transactions: Automatic tracing, segmentation and data extraction from business transactions.

- Analytics: AI-powered analysis, exploration/querying and extraction of business-relevant insights from Dynatrace® application and user experience data.

- Conversions: Visualization of and collaboration on business-relevant metrics such as conversions and revenue performance by product, customer segment, geography and more.

- Automation: AI-powered anomaly detection, alerting and root cause determination for business processes, with programmable APIs to trigger business workflows and change events.

“Digital transformation projects are spurring companies to create multidisciplinary lines of business teams that run the business with a product mindset and are demanding answers to questions that were previously difficult, slow or impossible to obtain,” said Steve Tack, SVP of Product Management at Dynatrace. “Digital Business Analytics leverages the automation and intelligence at the core of the Dynatrace Software Intelligence Platform to provide alerts, best-in-class anomaly detection and real-time answers about high-impact business issues such as degradation in revenue or conversions across products, geographies or segments.”

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Dynatrace Introduces Digital Business Analytics

Dynatrace announced the addition of Digital Business Analytics to its Software Intelligence Platform.

Digital Business Analytics provides real-time, AI-powered answers to business questions using data already flowing through Dynatrace’s application and digital experience monitoring modules. By tying together user experience, customer behavior, and application performance data with business metrics, Digital Business Analytics provides real-time answers about conversions, orders, churn, release validation, customer segmentation and more. Traditionally, application owners and business users have been forced to go to disparate, siloed tools and manually analyze data, thereby hampering their ability to run and optimize their digital business offerings in real-time.

Available today, Digital Business Analytics joins APM, Cloud Infrastructure Monitoring, Digital Experience Management, and AIOps as part of the Dynatrace all-in-one Software Intelligence Platform. As with these other modules, Dynatrace’s AI engine Davis is at the core of Digital Business Analytics. Davis continually learns what expected “normal” business performance looks like and provides proactive answers to issues, enabling faster decision making, greater optimization of resources and better business outcomes.

As data volume and velocity accelerates, organizations are struggling to make sense of disparate dashboards from traditional IT monitoring tools, web analytics and ad hoc reporting. Dynatrace’s Digital Business Analytics automatically captures business data and analyzes it in context with user experience and application performance data. Furthermore, Digital Business Analytics complements existing web analytics tools to deliver real-time and complete results, by combining existing customer-facing channels with application and user experience data. Dynatrace’s full-stack Software Intelligence Platform shares a common data model across the platform, with AI and automation at its core, applying anomaly detection to business metrics to provide precise, real-time answers to business-critical questions.

Key pillars of Digital Business Analytics include:

- Transactions: Automatic tracing, segmentation and data extraction from business transactions.

- Analytics: AI-powered analysis, exploration/querying and extraction of business-relevant insights from Dynatrace® application and user experience data.

- Conversions: Visualization of and collaboration on business-relevant metrics such as conversions and revenue performance by product, customer segment, geography and more.

- Automation: AI-powered anomaly detection, alerting and root cause determination for business processes, with programmable APIs to trigger business workflows and change events.

“Digital transformation projects are spurring companies to create multidisciplinary lines of business teams that run the business with a product mindset and are demanding answers to questions that were previously difficult, slow or impossible to obtain,” said Steve Tack, SVP of Product Management at Dynatrace. “Digital Business Analytics leverages the automation and intelligence at the core of the Dynatrace Software Intelligence Platform to provide alerts, best-in-class anomaly detection and real-time answers about high-impact business issues such as degradation in revenue or conversions across products, geographies or segments.”

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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