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AI for Data Observability With Enterprise Guardrails

Latest AI technology helps improve data observability outcomes

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AI for Data Observability with Acceldata
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Ankit B.

Senior Technical Lead - Enterprise
We are using power of Acceldata for our data quality issues. Its integration is simple and we are able to identify various data quality issues quite easily.

Timothy C.

Senior Expert of Data Governance - Enterprise
Acceldata has opened quite a few doors for us at Nestle. We have started with the Data Reliability features of the product and moved into the Cloud/Cost Optimization aspects.

Jayanth Reddy G.

Senior DevOps Engineer - Enterprise
This combination of usability, seamless integration, and robust features makes Acceldata a powerful tool for enhancing data reliability and performance.

Mark. H

Manager Data Warehouse Operations - Enterprise
Onboarding our data into Acceldata was very quick and simple. Within 24 hours all production data was loaded and trends were easily established.

Chidambararajan M.

IT Consultant - Enterprise
Acceldata is giving the better quality checks for the data sources and the maintenance of the environment is good.

Prashanth. S

Director, Platform Engineering and Data Operations - Enterprise
Acceldata has been instrumental in enhancing our data platform management capabilities. Their solutions not only ensure platform stability but also drive significant cost savings.
Acceldata AI

Leverage the Power of AI to Improve Data Observability

Acceldata’s technology provides enterprises with the ability to tailor the use of AI for Data Observability to their unique technical environment and business scenarios. This includes providing the means to control how AI capabilities are applied at an individual asset level, with desired guardrails that ensure that business context, regulatory requirements, and the proper balance of human oversight with AI autonomy are taken into consideration.

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.
Compute & Query Observability with Acceldata

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.

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.
Essential Data Observability for LLM Success by Acceldata Ebook

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