> For the complete documentation index, see [llms.txt](https://whiteklay.gitbook.io/virtual-data-assistant/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whiteklay.gitbook.io/virtual-data-assistant/overview.md). # Overview ## Overview The Virtual Data Assistant (VDA) is an open-standards, cloud-native analytics platform designed to revolutionize the way data analysts perform their data analytics job. It leverages the power of Assistive Analytics and Generative AI to streamline workflows, enhance productivity, and empower analysts to make data-driven decisions with ease. ### What We Do Virtual Data Assistant is a game-changer for data analysts, empowering them to perform complex data analytics tasks with ease and efficiency. By harnessing the capabilities of Assistive Analytics and Generative AI, the VDA revolutionizes the data analysis workflow, fostering a data-driven culture, and propelling organizations towards data-driven success. 1. Simplified Data Modeling: Assistive templates accelerate data modeling, enabling analysts to focus on refining insights rather than getting lost in code development. Automatically generated metadata documentation ensures transparency and enhances collaboration among team members. 2. Efficient Data Cataloging: Data connectors and metadata extraction streamline data cataloging, empowering analysts to discover and leverage relevant data assets quickly. A centralized view of metadata fosters data governance and aids in maintaining data quality. 3. Intuitive Dashboard Creation: NLP-powered dashboard and chart building democratize data visualization, making it accessible to a broader audience. Visual representations drive better decision-making and communication of key insights. 4. Seamless Workbooks for Analysis: Document, Query, and Expectation Workbooks expedite data analysis tasks, reducing the risk of errors. VDA promotes iterative and data-driven decision-making and continuous improvement. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://whiteklay.gitbook.io/virtual-data-assistant/overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.