New Acceldata co-pilot targets efficiency, broader use

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Acceldata Launches Generative AI Co-Pilot

Acceldata Launches Generative AI Co-Pilot

Acceldata launched the public preview of a generative AI co-pilot on Tuesday. The co-pilot aims to make the vendor’s tools easier to use and enable more people within organizations to work with data. It is a direct result of Acceldata’s September 2023 acquisition of Bewgle and its AI-powered unstructured data observability capabilities. The co-pilot is scheduled for general availability during the second quarter of 2024.

Acceldata is a data observability vendor based in Campbell, Calif., whose platform enables customers to monitor their data for quality as it moves from ingestion through the data pipeline. By monitoring data as they prepare it for analysis, users can immediately address any problems so that data can be trusted when put into action training models and informing decisions. Previously, users had to wait to discover and fix problematic data until the modeling and analytics processes.

Other vendors specializing in data observability include Monte Carlo and Unravel. Increased efficiency is one of the primary reasons for using generative AI co-pilots, according to Matt Aslett, an analyst at ISG’s Ventana Research. “The most popular motivation for adopting AI is efficiency, followed by innovation and business growth,” he said, citing ISG’s 2024 Buyer Behavior Study on Enterprise Adoption of AI. “AI-powered assistants and co-pilots are a significant area of investment for software providers and enterprises as they seek to improve efficiency.”

Co-pilots are essentially generative AI-powered assistants that allow users to converse with their software using natural language and automate tasks that take up significant time when performed manually. Generative AI large language models (LLMs) such as ChatGPT and Google Bard can be integrated with data management and analytics platforms to enable true natural language processing (NLP). In the case of data management and analytics vendors, they enable customers to monitor, prepare, query and analyze data using natural language rather than code. In addition, LLMs can generate code on their own, making them useful tools for data engineers and other data experts building data pipelines as well as analysts working with the data in those pipelines.

Acceldata’s new co-pilot enables various capabilities, including anomaly detection, cost control, application automation, and automatic summary generation. The goal is to bridge the business/technical divide and make data management and analytics tools available to more people within a given enterprise, according to Rohit Choudhary, the vendor’s founder and CEO.

With its co-pilot now in public preview and general availability expected by the end of spring, Acceldata has plans to continue expanding its AI capabilities. Data observability’s aim is to improve the quality and reliability of data. Choudhary noted that the same data pipelines that currently feed data products such as traditional AI models, dashboards, and reports will be used to train generative AI models in the future. To reduce the occurrences of AI hallucinations and make sure those generative AI models are as accurate as possible, data quality is imperative.

Aslett added that generative AI co-pilots are becoming common among data management vendors, and AI-powered assistants are likely to be included in all enterprise software within the next few years. He noted that while the various co-pilot offerings have similar names and overall value, they are tailored to the specific roles and responsibilities of individual users and the functional capabilities of the associated software.

Overall, the launch of Acceldata’s generative AI co-pilot signals a move toward greater accessibility to data management and analytics tools, with a focus on improving efficiency and enabling more users within organizations to participate in the process of making data products.


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