Telmai's latest product feature updates enhance data quality
workflows, enabling enterprises to accelerate AI adoption while
ensuring data reliability and compliance across heterogeneous cloud
environments.
SAN
FRANCISCO, Dec. 11, 2024 /PRNewswire/ -- Telmai,
the AI-powered data quality company, today announced significant
enhancements to its enterprise data quality platform, introducing
automated workflows designed to accelerate AI adoption. The new
capabilities enable organizations to automatically monitor,
validate, and optimize data quality across their AI implementations
while ensuring regulatory compliance and data reliability at
scale.
As enterprises increasingly rely on AI to power decision-making
and innovation, the complexities of managing diverse and vast data
ecosystems often hinder success. Inconsistent data quality, manual
validation processes, and the risk of sensitive data exposure
create roadblocks that slow AI adoption and impact outcomes.
Telmai addresses these challenges head-on with updates that
streamline operations, enhance accuracy, and scale seamlessly
across systems.
Native integration support for Open table formats to power AI
workloads
Telmai's integration with Apache Trino expands support for open
table formats like Apache Iceberg and Delta
Lake by providing native data quality monitoring and
observability capabilities. These formats are essential for
building scalable, modern data architectures that support advanced
AI applications. They enable seamless querying, schema evolution,
and efficient scalability, which is critical for handling large and
dynamic datasets in AI pipelines. Telmai natively supports
column-level data quality monitoring without pre-processing or
sampling your data by embedding automated data quality checks
directly within these open formats. This minimizes operational
overhead for users and accelerates AI adoption while ensuring high
fidelity in data-driven decision-making.
Automated detection and prevention of exposure of sensitive data
in AI workloads
Telmai introduces automation for detecting and preventing
sensitive data exposure in AI workloads, addressing a critical need
for enterprises operating in regulated environments. Through
AI-powered pattern recognition, organizations can automatically
identify and protect PII data, such as credit card numbers and
social security details, across their AI training datasets,
ensuring compliance while accelerating development cycles.
"With these innovations, Telmai is simplifying the complexities
of data quality, enabling enterprises to focus on leveraging AI for
transformative growth," said Maxim Lukichev, co-founder and chief
technology officer at Telmai. "Our automated workflows, open format
integrations, and advanced automated workflows provide the
foundation for scalable, accurate, and trustworthy AI systems."
Efficient metadata scanning capabilities that reduce resource
overhead
Telmai's metadata-only scanning capabilities dramatically reduce
the computational overhead typically associated with data quality
monitoring. Users have the flexibility to perform complete
column-level data checks for critical data elements or ensure
metadata validation across all data assets. This approach enables
comprehensive quality controls across massive datasets without
impacting performance or adding significant costs.
Don't let data quality challenges slow your AI adoption. Take
the first step towards building reliable AI pipelines. Click here
to see how Telmai ensures data accuracy and reliability at scale
and achieves AI success.
About Telmai
Telmai is a data observability platform company that enables
enterprise data owners to monitor and detect real-time data issues.
The platform leverages AI to monitor all data passing through the
data pipeline before entering the data warehouse, protecting
downstream systems and analytics used for decision-making. Telmai's
real-time architecture supports anomaly detection closest to data
sources and works over complex data types with native support for
nested and multi-valued attributes. For more information, please
visit Telmai.
Contact: Steve Carman |
steve.carman@telm.ai
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SOURCE Telmai