Acceldata Revolutionizes Data Observability with Industry-First AI Technology

Acceldata, the market leader in enterprise data observability, today 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 Gartne, “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.

As enterprises across various industries continue to experience a rapid influx of data, Acceldata has proven to be a critical solution for driving success with the most complete, all-in-one enterprise data observability solution. With the introduction of industry-first AI capabilities, the company has solidified its position as a leader in data management by equipping teams with the most innovative technology for building and operating best-in-class data products.

Source: https://www.acceldata.io/

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