Davis AI helps ensure all data in the Dynatrace
platform is reliable and accurate for business analytics, smart
cloud orchestration, and reliable automation
Dynatrace (NYSE: DT), the leader in unified observability and
security, today 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.
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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, with its OneAgent technology, provides us with a
high level of confidence that the data powering our analytics and
automation is healthy. The platform is also very flexible, which
enables us to tap into custom data sources and open standards, like
OpenTelemetry,” said Kulvir Gahunia, Director, Site Reliability
Office at TELUS. “New Dynatrace data observability capabilities
will help ensure the data from these custom sources is also
high-quality fuel for our analytics and automation. This will save
us from having to cleanse the data manually and reduce the need for
additional data cleansing tools.”
High-quality data is critical for organizations that rely on it
to inform business and product strategies, optimize and automate
processes, and drive continuous improvements. However, the scale
and complexity of data from modern cloud ecosystems, combined with
the increased use of open source solutions, open APIs, and other
customized instrumentation, make it hard to achieve this goal.
By adopting data observability techniques, organizations can
improve data availability, reliability, and quality throughout the
data lifecycle, from ingestion to analytics and automation.
According to Gartner®, “by 2026, 30% of enterprises implementing
distributed data architectures will have adopted data observability
techniques to improve visibility over the state of their data
landscape, up from less than 5% in 2023.i”
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. Visit the Dynatrace Data Observability blog for more
information.
About Dynatrace
Dynatrace (NYSE: DT) exists to make the world’s software work
perfectly. Our unified platform combines broad and deep
observability and continuous runtime application security with
Davis® hypermodal AI to provide answers and intelligent automation
from data at an enormous scale. This enables innovators to
modernize and automate cloud operations, deliver software faster
and more securely, and ensure flawless digital experiences. That’s
why the world’s largest organizations trust the Dynatrace® platform
to accelerate digital transformation.
Curious to see how you can simplify your cloud and maximize the
impact of your digital teams? Let us show you. Sign up for a 15-day
Dynatrace trial.
Cautionary Language Concerning Forward-Looking
Statements
This press release includes certain “forward-looking statements”
within the meaning of the Private Securities Litigation Reform Act
of 1995, including statements regarding the capabilities of Data
Observability, the expected benefits to organizations from using
Data Observability, and the timing for when Data Observability is
expected to be generally available. These forward-looking
statements include all statements that are not historical facts and
statements identified by words such as “will,” “expects,”
“anticipates,” “intends,” “plans,” “believes,” “seeks,”
“estimates,” and words of similar meaning. These forward-looking
statements reflect our current views about our plans, intentions,
expectations, strategies, and prospects, which are based on the
information currently available to us and on assumptions we have
made. Although we believe that our plans, intentions, expectations,
strategies, and prospects as reflected in or suggested by those
forward-looking statements are reasonable, we can give no assurance
that the plans, intentions, expectations, or strategies will be
attained or achieved. Actual results may differ materially from
those described in the forward-looking statements and will be
affected by a variety of risks and factors that are beyond our
control, including the risks set forth under the caption “Risk
Factors” in our Quarterly Report on Form 10-Q filed on November 2,
2023, and our other SEC filings. We assume no obligation to update
any forward-looking statements contained in this document as a
result of new information, future events, or otherwise.
i Gartner Innovation Insight: Data Observability Enables
Proactive Data Quality, 20 July 2023. GARTNER is a registered
trademark and service mark of Gartner, Inc. and/or its affiliates
in the U.S. and internationally and is used herein with permission.
All rights reserved.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20240131959076/en/
Investor Contact: Noelle Faris VP, Investor Relations
Noelle.Faris@dynatrace.com
Media Relations: Jerome Stewart VP, Communications
Jerome.Stewart@dynatrace.com
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