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Dynatrace Unveils Data Observability for Analytics and Automation Platform

Dynatrace announced new AI-powered data observability capabilities for its analytics and automation platform.

With Dynatrace® Data Observability, teams can confidently rely on all observability, security, and business events data in Dynatrace to fuel the platform’s Davis® AI engine to help eliminate false positives and deliver trustworthy business analytics and reliable automations.

Dynatrace Data Observability enables business analytics, data science, DevOps, SRE, security, and other teams to help ensure all data in the Dynatrace® platform is high quality. This complements the platform’s existing data cleansing and enrichment capabilities provided by Dynatrace OneAgent® to help ensure high quality for data collected via other external sources, including open source standards, such as OpenTelemetry, and custom instrumentation, such as logs and Dynatrace APIs. It enables teams to track the freshness, volume, distribution, schema, lineage, and availability of these externally sourced data, thereby reducing or eliminating the need for additional data cleansing tools.

Dynatrace Data Observability works with other core Dynatrace® platform technologies, including Davis hypermodal AI combining predictive, causal, and generative AI capabilities, to provide data-driven teams with the following benefits:

- Freshness: Helps ensure the data used for analytics and automation is up-to-date and timely and alerts to any issues—for example, out-of-stock inventory, changes in product pricing, and timestamp anomalies.

- Volume: Monitors for unexpected increases, decreases, or gaps in data—for example, the number of reported customers using a particular service—which can indicate undetected issues.

- Distribution: Monitors for patterns, deviations, or outliers from the expected way data values are spread in a dataset, which can signal issues in data collection or processing.

- Schema: Tracks data structure and alerts on unexpected changes, such as new or deleted fields, to prevent unexpected outcomes like broken reports and dashboards.

- Lineage: Delivers precise root-cause detail into the origins of data and what services it will impact downstream, helping teams proactively identify and resolve data issues before they impact users or customers.

- Availability: Leverages the Dynatrace platform’s infrastructure observability capabilities to observe digital services’ usage of servers, networking, and storage, alerting on abnormalities such as downtime and latency, to provide a steady flow of data from these sources for healthy analytics and automation.

“Data quality and reliability are vital for organizations to perform, innovate, and comply with industry regulations,” said Bernd Greifeneder, CTO at Dynatrace. “A valuable analytics solution must detect issues in the data that fuels analytics and automation as early as possible. Dynatrace OneAgent has always helped ensure that the data it collects is of the highest quality. By adding data observability capabilities to our unified and open platform, we’re enabling our customers to harness the power of data from more sources for more analytics and automation possibilities while maintaining the health of their data, without any extra tools.”

Dynatrace Data Observability is expected to be generally available for all Dynatrace SaaS customers within 90 days of this announcement.

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Dynatrace Unveils Data Observability for Analytics and Automation Platform

Dynatrace announced new AI-powered data observability capabilities for its analytics and automation platform.

With Dynatrace® Data Observability, teams can confidently rely on all observability, security, and business events data in Dynatrace to fuel the platform’s Davis® AI engine to help eliminate false positives and deliver trustworthy business analytics and reliable automations.

Dynatrace Data Observability enables business analytics, data science, DevOps, SRE, security, and other teams to help ensure all data in the Dynatrace® platform is high quality. This complements the platform’s existing data cleansing and enrichment capabilities provided by Dynatrace OneAgent® to help ensure high quality for data collected via other external sources, including open source standards, such as OpenTelemetry, and custom instrumentation, such as logs and Dynatrace APIs. It enables teams to track the freshness, volume, distribution, schema, lineage, and availability of these externally sourced data, thereby reducing or eliminating the need for additional data cleansing tools.

Dynatrace Data Observability works with other core Dynatrace® platform technologies, including Davis hypermodal AI combining predictive, causal, and generative AI capabilities, to provide data-driven teams with the following benefits:

- Freshness: Helps ensure the data used for analytics and automation is up-to-date and timely and alerts to any issues—for example, out-of-stock inventory, changes in product pricing, and timestamp anomalies.

- Volume: Monitors for unexpected increases, decreases, or gaps in data—for example, the number of reported customers using a particular service—which can indicate undetected issues.

- Distribution: Monitors for patterns, deviations, or outliers from the expected way data values are spread in a dataset, which can signal issues in data collection or processing.

- Schema: Tracks data structure and alerts on unexpected changes, such as new or deleted fields, to prevent unexpected outcomes like broken reports and dashboards.

- Lineage: Delivers precise root-cause detail into the origins of data and what services it will impact downstream, helping teams proactively identify and resolve data issues before they impact users or customers.

- Availability: Leverages the Dynatrace platform’s infrastructure observability capabilities to observe digital services’ usage of servers, networking, and storage, alerting on abnormalities such as downtime and latency, to provide a steady flow of data from these sources for healthy analytics and automation.

“Data quality and reliability are vital for organizations to perform, innovate, and comply with industry regulations,” said Bernd Greifeneder, CTO at Dynatrace. “A valuable analytics solution must detect issues in the data that fuels analytics and automation as early as possible. Dynatrace OneAgent has always helped ensure that the data it collects is of the highest quality. By adding data observability capabilities to our unified and open platform, we’re enabling our customers to harness the power of data from more sources for more analytics and automation possibilities while maintaining the health of their data, without any extra tools.”

Dynatrace Data Observability is expected to be generally available for all Dynatrace SaaS customers within 90 days of this announcement.

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AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...