Compare Microsoft Purview vs. Vedana

Microsoft Purview serves as a comprehensive data governance platform that facilitates the management and oversight of your data across on-premises, multicloud, and software-as-a-service (SaaS) environments. With its capabilities in automated data discovery, sensitive data classification, and complete data lineage tracking, you can effortlessly develop a thorough and current representation of your data ecosystem. This empowers data users to access reliable and valuable data easily. The service provides automated identification of data lineage and classification across various sources, ensuring a cohesive view of your data assets and their interconnections for enhanced governance. Through semantic search, users can discover data using both business and technical terminology, providing insights into the location and flow of sensitive information within a hybrid data environment. By leveraging the Purview Data Map, you can lay the groundwork for effective data utilization and governance, while also automating and managing metadata from diverse sources. Additionally, it supports the classification of data using both predefined and custom classifiers, along with Microsoft Information Protection sensitivity labels, ensuring that your data governance framework is robust and adaptable. This combination of features positions Microsoft Purview as an essential tool for organizations seeking to optimize their data management strategies.

Your organization's knowledge is scattered across various formats such as PDFs, spreadsheets, wikis, and ERP exports. Traditional retrieval augmented generation (RAG) techniques can only extract the top-K similar snippets, which is adequate for brief summaries but inadequate when precise and comprehensive answers are required. Vedana adopts a structure-first methodology, allowing you to define your domain through anchors, attributes, and connections. It systematically ingests your data into a categorized knowledge graph, enabling AI agents to investigate it incrementally: performing graph queries, vector searches, and compiling answers from actual data. In this setup, the language model interprets the information while the data remains the authoritative source. The advantages you gain include: - Precise figures: obtaining specific prices, dates, and statuses directly from the graph - Comprehensive outcomes: guaranteeing that all relevant records are included without omissions - Multi-step reasoning: connecting product information to categories, regulations, and documents seamlessly - Traceability: ensuring that every answer is linked back to specific nodes, edges, and data segments - Consistency: delivering the same results with identical queries and processes Additionally, the system comes with built-in evaluation using gold-standard datasets and is compatible with any language model. It can be deployed as open-core, managed cloud, or on-premises solutions, with pilot implementations available in just four weeks. This approach not only enhances accuracy but also fosters a deep understanding of the interconnected data landscape.