Innovative AI-powered data observability features from a well-known provider ensure high quality of all externally collected data. This helps eliminate false positives and deliver trustworthy business analytics and reliable automation.
Dynatrace announces new AI-powered data observability capabilities for its analytics and automation platform. Business analytics, data science, DevOps, SRE, security, and other teams can use Dynatrace Data Observability to ensure that all data on the Dynatrace platform is of high quality.
Teams can track the origin of external data
Dynatrace Data Observability complements the platform's existing data cleansing and enrichment capabilities provided by Dynatrace OneAgent. High quality of data collected through other external sources is ensured, including open source standards such as OpenTelemetry and custom instruments such as Logs and Dynatrace APIs. The new capability enables teams to track the freshness, volume, distribution, schema, provenance and availability of externally sourced data, thereby reducing or eliminating the need for additional data cleansing tools.
“Dynatrace with its OneAgent technology gives us a high level of assurance that the data that powers our analytics and automation is reliable. The platform is also very flexible and allows for the use of custom data sources and open standards such as OpenTelemetry,” says Kulvir Gahunia, Director, Site Reliability Office at TELUS. “The new Dynatrace data observability features ensure that the data from these sources also provides high-quality input for our analysis and automation. This saves us from having to clean the data manually and reduces the need for additional tools.”
Improved transparency of the data landscape
Companies rely on high-quality data to develop business and product strategies, optimize and automate processes, and drive continuous improvements. However, the volume and complexity of data from modern cloud ecosystems, combined with the increasing use of open source solutions, open APIs and other custom systems, make the path to this goal difficult.
By adopting data observability techniques, companies can improve data availability, reliability and quality throughout the data lifecycle - from ingestion to analysis and automation. According to Gartner, by 2026, 30 percent of companies implementing distributed data architectures will use data observability techniques to improve the visibility of their data landscape; in 2023 it was less than five percent.1
Dynatrace Data Observability works with other core technologies of the Dynatrace platform, including Davis hypermodal AI. The Davis AI Engine combines predictive, causal and generative AI capabilities to provide data-driven teams with the following benefits:
- Topicality: It ensures that the data used for analytics and automation is up-to-date and warns of issues such as out-of-stock inventory, product price changes, and timestamp anomalies.
- Volume: It monitors unexpected increases, decreases, or gaps in the data, such as the number of reported customers using a particular service. This may indicate undetected problems.
- Distribution: It checks for patterns, deviations or outliers from the expected distribution of data values in a data set. The distribution may indicate problems with data collection or processing.
- Schema: It tracks the data structure and warns of unexpected changes, such as new or deleted fields, to prevent unwanted results such as incorrect reports or dashboards.
- Ancestry: It provides accurate information about the origin of data and the impact on downstream services. This allows teams to proactively identify and resolve data issues before they impact users or customers.
- Availability: It leverages the Dynatrace platform's infrastructure monitoring capabilities to monitor digital services' usage of servers, networks and storage and alert on anomalies such as downtime and latency. This ensures a constant flow of data from these sources for reliable analysis and automation.
About Dynatrace Dynatrace ensures that software works perfectly worldwide. Our unified software intelligence platform combines broad and deep observability and continuous run-time application security with the most advanced AIOps to deliver answers and intelligent automation from data at remarkable scale. This enables organizations to modernize and automate cloud operations, deliver software faster and more securely, and ensure flawless digital experiences.
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