DataChat:一个直观和协作的数据分析平台

Rogers Jeffrey Leo John, Dylan Bacon, Junda Chen, Ushmal Ramesh, Jiatong Li, Deepan Das, R. Claus, Amos Kendall, Jignesh M. Patel
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引用次数: 3

摘要

企业投资于数据平台,目的是通过分析提取有意义的信息。通常,专家会创建分析管道,将其输入仪表板,并为预先确定的问题提供答案。这种方法使分析对大多数人来说是一项观赏性的运动,并为利用这些投资带来了操作瓶颈。为了提高数据的价值,许多组织选择开放其数据资产,并允许更广泛的用户访问。然而,对于大多数企业用户来说,使用SQL和Python等编程语言进行分析可能很困难。DataChat提供了一种简化的数据科学方法,它直观、强大,并且对所有数据用户都可访问。该平台建立在一个数据函数库的基础上,这些函数库被清晰地抽象,以最大限度地提高效率和易用性,同时维护数据科学所需的丰富工具套件。有了这些功能,用户可以通过在电子表格视图中使用简单的点击界面或使用自然英语界面来创建数据分析管道。现代共享和协作功能是平台各个方面的核心,允许团队轻松弥合专业知识差距。通过提供自动生成的关于如何推导结果的英文解释,有助于更深入地理解结果。通过增强数据科学和人与人之间交流的这些方面,该平台解决了许多组织在分析需求成熟时遇到的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DataChat: An Intuitive and Collaborative Data Analytics Platform
Enterprises invest in data platforms with the aim of extracting meaningful information through analytics. Typically, experts create analytics pipelines that feed into dashboards and provide answers to predetermined questions. This approach makes analytics a spectator sport for most people and introduces operational bottlenecks to leveraging those investments. To improve the value derived from data, many organizations are opting to open up their data assets and allow access to a wider range of users. However, using programming languages such as SQL and Python for analytics can be difficult for most enterprise users. DataChat provides a simplified data science approach that is intuitive, powerful, and accessible to all data users. The platform is built on a library of data functions that are cleanly abstracted to maximize efficiency and ease of use while maintaining a rich suite of tools necessary for data science. With these functions, users can create data analysis pipelines by using a simple point-and-click interface in a spreadsheet view or by using natural English interfaces. Modern sharing and collaboration features are central to all aspects of the platform, allowing teams to easily bridge expertise gaps. A deeper understanding of results is facilitated by providing automatically-generated English explanations of how they were derived. By enhancing these aspects of data science and human-to-human communication, the platform addresses the needs that many organizations are encountering as their analytics needs mature.
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