Data Governance for RAG

Data Discovery
  • Routing
  • Ownership
  • Explainability
Data Security
  • Data Protection
  • Role-Based Access Control (RBAC) and Policy-Based Access Control (PBAC)
  • Policy enforcement with GenAI
Data Glossary
  • Context enrichment with Business Terms
  • Knowledge Graphs
Master Data
  • Reference Data Management
  • Bias-Mitigated Single Source of Truth

Data Governance for Retrieval-Augmented Generation (RAG)

Data Cost
  • Efficiency analysis
Data Quality
  • Enrichment with data quality metrics
  • Detecting biased data
  • User Feedback Loop
Data Lineage
  • Enrichment with data origins
  • Root cause analysis and explainability
Data Modeling
  • Zero-shot Prompting
  • Few-shot Prompting
Data Discovery


  • Application: Leveraging Data Governance tools, specifically data catalogs with enterprise-confirmed definitions, to enhance RAG systems. These catalogs provide RAG with validated and universally recognized information about data assets, enabling more curated and informed routing of queries.
  • Key Benefit: Promotes higher precision in RAG system query processing by using data catalogs that reflect enterprise-wide agreed-upon definitions, ensuring consistency and reliability in data routing.
  • Tools/Solutions: For Curated Query Routing: AWS Glue Data Catalog, Open Data Discovery, DataHub, Apache Atlas


  • Application: Incorporating data ownership information from Data Governance tools into RAG systems. This integration enables RAG to utilize knowledge about which individuals or groups are responsible for specific data assets, aiding in user navigation for assistance, queries, or escalations.
  • Key Benefit: Enhances the RAG system’s operational efficiency and user experience by providing clear guidance on data asset ownership, facilitating more informed interactions and decisions.
  • Integration Solution: For Data Ownership Information: Amazon DataZone, Open Data Discovery, DataHub, Amundsen


  • Application: Utilizing Data Catalogs, a core component of Data Governance tools, to augment explainability in RAG systems. Data Catalogs offer rich, detailed descriptions of data assets, including information on data structures, statistics, links to business terms, tagging, discussions on assets, and ownership. Integrating this detailed metadata into RAG systems allows them to enrich conversations and outputs with trustworthy, detailed information about the data they process.
  • Key Benefit: Significantly improves the transparency and reliability of information provided by RAG systems. Users gain a deeper understanding of the data, its context, and its relevance, leading to more informed decision-making.
  • Tools/Solutions: For Rich Data Asset Descriptions: AWS Glue Data Catalog, Amazon DataZone, Open Data Discovery, DataHub, Amundsen, OpenMetadata, Apache Atlas.
Data Security

Role-Based Access Control (RBAC) and Policy-Based Access Control (PBAC)

  • Application: Employing Role-Based Access Control (RBAC) and Policy-Based Access Control (PBAC) within RAG systems to manage data access securely and efficiently. RBAC provides a structured way to assign data access based on user roles, ensuring that individuals only access data pertinent to their role in the organization. PBAC adds another layer by enabling access control based on specific policies, which can include contextual factors and conditions. This dual approach allows RAG systems to dynamically adapt access rights, ensuring secure and appropriate data access in various scenarios.
  • Key Benefit: Enhances data security in RAG systems by allowing precise control over who can access what data and under which circumstances. This combination of RBAC and PBAC ensures that data access is both compliant with organizational policies and adaptable to specific context, thereby improving both security and operational efficiency.
  • Tools/Solutions:

Policy enforcement with GenAI

  • Application: Enhancing RAG systems with an internal GenAI engine for dynamic user permission assessment. The engine analyzes user roles, cross-references internal role descriptions, and evaluates the data requested to determine if additional approval is necessary. This approach adds a layer of intelligent, context-aware safeguarding to traditional authorization methods.
  • Key Benefit: Provides an extra layer of security by contextually assessing data access requests, ensuring compliance with data policies.
  • Tools/Solutions:
Data Glossary

Context enrichment with Business Terms

  • Application: Implementing Data Glossaries as part of Data Governance to create an enterprise-wide set of business terms for RAG systems. This ensures consistent use of terminology across the organization. By integrating these glossaries into RAG systems, the context of data can be enriched with reliable, verified business terms, thereby reducing ambiguity and enhancing clarity.
  • Key Benefit: Facilitates more coherent and accurate communication within RAG systems by using an established, organization-wide language. This standardization of terms ensures that the information provided by the RAG system is reliable and universally understood within the enterprise.
  • Tools/Solutions: For Creating and Managing Business Terms: Open Data Discovery, DataHub, Amundsen, OpenMetadata, Apache Atlas.

Knowledge Graphs

  • Application: Utilized in RAG systems for enhancing the interpretation and contextualization of queries in enterprise SQL databases. By integrating Knowledge Graphs, RAG systems can more accurately understand and process complex business data, improving decision-making and query responses.
  • Key Benefit: Increases accuracy of LLM-based responses from 16% to 54% in enterprise settings (research).
  • Tools/Solutions: For Knowledge Graph Creation: AWS Neptune, Open Data Discovery (ODD) for building and managing the contextual layer.
Master Data

Reference Data Management

  • Application: Utilizing Reference Data Management systems within the context of RAG systems. This involves enriching RAG systems with essential lookup data that aids in creating categories, hierarchies, and business translations. Reference Data Management systems provide a transparent and manageable approach for handling these crucial manually-supported data elements.
  • Key Benefit: Enhances the RAG system’s functionality by introducing structured, reliable reference data, aiding in more accurate categorization and business interpretation, which is vital for data-driven decision-making.
  • Tools/Solutions: For Reference Data Management: Amazon DataZone, Open Data Discovery

Bias-Mitigated Single Source of Truth

  • Application: Establishing a robust Master Data Management (MDM) system as a single source of truth for RAG systems. This approach focuses on identifying and prioritizing the most reliable and curated data sources, thereby reducing the risk of biased data influencing RAG’s operations.
  • Key Benefit: Ensures RAG systems rely on the most accurate, up-to-date, and unbiased data, enhancing the quality and reliability of retrieval and augmentation processes.
  • Tools/Solutions:
    • For Master Data Management: AWS Data Exchange for managing and sharing curated datasets, ensuring RAG accesses only high-quality data sources.
    • For Data Verification and Curation: Tools like AWS Glue for data cataloging and integration, enabling effective tracking and verification of master data quality.
Data Сost

Efficiency analysis

  • Application: Incorporating the Data Cost aspect of Data Governance into RAG systems to analyze and understand the implications of data storage and usage. This integration enables RAG systems to consider cost factors when retrieving and utilizing data, ensuring that information is obtained in the most cost-effective manner without compromising reliability.
  • Key Benefit: Enhances the cost-effectiveness of RAG systems by optimizing data storage and usage strategies. This approach not only reduces unnecessary expenses but also maintains the integrity and reliability of the information provided.
  • Tools/Solutions: For Data Storage and Usage Analysis: AWS Cost Explorer, AWS Cost and Usage Reports
Data Quality

Enrichment with data quality metrics

  • Application: Supplementing RAG systems with detailed data quality metrics. This involves integrating information about the accuracy and reliability of data sources and providing confidence scores to assess the trustworthiness of data used in the retrieval and augmentation processes.
  • Key Benefit: Enhances RAG systems by enabling more accurate and reliable data retrieval and augmentation, informed by robust data quality metrics and confidence assessments.
  • Tools/Solutions: For Data Quality Assessment: AWS Glue Data Quality, Great Expectations, dbt-expectations, Deequ, Data Quality Gate, Open Data Discovery

Detecting biased data

  • Application: Establishing mechanisms to identify and document biased data, enabling RAG systems to recognize and consider these biases in their operations. This involves analyzing missing data segments, detecting skewness due to source errors or user behavior, and cataloging identified biases for RAG system reference.
  • Key Benefit: Enhances the RAG system’s ability to account for and mitigate the impact of data biases, leading to more informed and balanced decision-making.
  • Tools/Solutions:

User Feedback Loop

  • Application:  Integrating a feedback mechanism in RAG systems that allows users to report issues with data, raise questions, and provide objections or concerns. This feature facilitates direct user interaction with the system to highlight data quality issues or ambiguities.
  • Key Benefit: Continuously improves data quality and system accuracy by incorporating real-time user feedback, leading to a more reliable and trustworthy RAG system.
  • Tools/Solutions: For Feedback Processing: Amazon Bedrock, Amazon Comprehend
Data Lineage

Enrichment with data origins

  • Application: Integrating Data Lineage capabilities from Data Governance tools into RAG systems. This involves providing RAG with detailed information about the origins of data and its subsequent transformations. Such integration allows RAG systems to trace the data’s journey, enhancing trust and explainability in the data used.
  • Key Benefit:  Increases the credibility and transparency of data within RAG systems. By understanding data origins and transformations, users gain deeper insights and trust in the data, enhancing the overall reliability of RAG-generated information.
  • Tools/Solutions: For Data Lineage Tracking: OpenLineage, Open Data Discovery, DataHub, Amundsen, OpenMetadata, Apache Atlas

Root cause analysis and explainability

  • Application: Employing Data Lineage capabilities from Data Governance tools for root cause analysis in RAG systems. This usage enables RAG to identify and understand the origins of various data issues, such as biases, outdated information, or missing data. Data lineage provides a clear path to trace back through the data’s history, helping to pinpoint the exact source of such issues.
  • Key Benefit: Enhances the ability of RAG systems to quickly address and remediate data issues, offering greater transparency and a better understanding of the data used. This leads to improved trustworthiness and reliability of the information provided by the RAG system.
  • Tools/Solutions: For Data Lineage Tracking: OpenLineage, Open Data Discovery, DataHub, Amundsen, OpenMetadata, Apache Atlas
Data Modeling

Zero-shot Prompting

  • Application: Leveraging data catalogs, especially those enriched with detailed data models, to provide contextual groundwork for effective zero-shot prompting in RAG systems. This involves using comprehensive metadata and data structures to frame and guide the RAG’s understanding and responses.
  • Key Benefit: Facilitates more accurate and context-aware responses from RAG systems, especially in complex querying scenarios, by providing a well-defined data model context.
  • Tools/Solutions: For Rich Data Catalogs: AWS Glue Data Catalog, Open Data Discovery, DataHub, Amundsen

Few-shot Prompting

  • Application: Employing Data Governance tools to store, manage, and curate examples for few-shot prompting. This process involves confirming, reviewing, and discussing examples to ensure their accuracy and effectiveness.
  • Key Benefit: Enhances the RAG system’s few-shot prompting ability by providing it with a repository of validated examples, leading to more precise and contextually relevant responses.
  • Tools/Solutions: For Managing Few-shot Examples: Open Data Discovery.