Acceldata announced an industry-first AI technology that enables
DataOps teams to deliver advanced, AI-assisted data observability that adapts
to their unique business context. Whereas previous approaches to AI for Data
Observability are black-box, Acceldata's technology provides enterprises with
the ability to tailor AI for Data Observability to their unique technical
environment and business scenarios. This includes providing desired guardrails
that ensure that business context, regulatory requirements, and the proper
balance of human oversight with AI autonomy are taken into consideration.
"Artificial
Intelligence has the potential to fundamentally change the way enterprise data
is managed," said Rohit Choudhary, CEO and co-founder of Acceldata. "Our
innovative approach empowers enterprises to tailor AI-assisted data
observability to adapt and conform with their specific operational and business
needs, setting us apart in the industry. Built on this AI technology, today we
are delivering an AI co-pilot that eliminates manual configuration hassles,
reduces setup time, enables automatic monitoring of data anomalies, and fosters
collaboration and contributions from non-technical users."
Amidst the
explosive adoption of AI, modern enterprises are demanding more control over
their AI models to avoid unwanted repercussions including poor and unreliable
model performance. Following the acquisition of Bewgle, a cutting-edge artificial
intelligence platform, Acceldata is addressing the needs of the enterprise with
the introduction of a new AI co-pilot to its All-in-One Enterprise Data
Observability platform.
Key benefits
of Acceldata's AI co-pilot include:
- Anomaly detection - improve data reliability by studying and alerting on
anomalies in data freshness, data profiling, and data quality changes,
ensuring the trustworthiness of data.
- Cost control & forecasting - auto-learn cost consumption patterns, including
seasonality, and alert users to prevent runaway consumption. Forecast
consumption based on learned behavior.
- Rule and policy application automation - leverage generative AI and large language models
(LLMs) to streamline bulk policy creation, easing effort and preventing
errors and omissions due to human oversight.
- Data asset description generation - automatically generate human readable descriptions
for data assets, policies, and rules to facilitate seamless communication
between the technical and business owners of data assets.
According to
Gartner, "Data observability driven by active metadata and AI/ML improves the
reliability of data and data ecosystems by increasing our ability to observe
changes and discover unknowns. Data and analytics leaders should understand and
leverage its features and benefits to ensure data trust and reliability." Melody
Chien and Ankush Jain, Gartner Analysts, in the 2023 Data and Analytics
Essentials: Data Observability Report.