In this article, we will explore:

• Why strong data governance sets successful AI adopters apart from those at risk of joining the half that fail.
• Common data governance challenges you'll need to overcome.
• How Pryon can help you establish a solid foundation for AI-ready data.

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Data Quality Can Make or Break Your AI Project

Accurate, consistent, and timely data is crucial for training effective AI models. Without high-quality data, AI systems are more likely to produce unreliable results or hallucinations, misinterpret contexts, and compromise decision-making processes.

Even strategies designed to improve accuracy and mitigate hallucinations, such as retrieval-augmented generation (RAG), can fall short if the underlying data is not appropriately prepared. Deploying a seemingly reliable RAG pipeline without proper data governance for AI can create a false sense of security, which may lead to harmful outcomes.

As AI models grow more complex, the demand for pristine data increases, intensifying the need for robust data quality protocols. However, organizations continue to struggle with data challenges—stemming from siloed, duplicated, or unreliable sources—that undermine their AI initiatives. In fact, Harvard Business Review reveals that nearly half of organizations identify data issues as the main barrier to AI adoption.

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Almost 50% of organizations list data challenges as the primary obstacle to adopting AI. - Harvard Business Review

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These challenges often intensify due to the skills gap in managing unstructured data. While data scientists and analytics professionals excel in handling structured data, generative AI's reliance on natural language data makes it important for organizations to build their expertise in unstructured data management to become AI-ready.

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What is AI-Ready Data?

AI-ready data is key to harnessing the full potential of AI systems, ensuring they operate efficiently and effectively.

For data to be considered AI-ready, it must be:

1. Machine-readable

Machine-readable data is structured in a way that allows AI systems to process and analyze it without human intervention. For instance, converting unstructured text into a structured format like CSV or JSON allows AI algorithms to easily parse and use the data for various applications.

If you're using an enterprise-grade RAG system like Pryon RAG Suite, the resource-intensive work of making your unstructured data machine-readable will be done for you automatically as part of the ingestion and retrieval processes.

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Recommended reading:
What Is Retrieval-Augmented Generation (RAG)?

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2. Retrievable by an AI system – quickly, accurately, and to the point

AI systems need to access specific data points quickly to make real-time decisions or generate insights. For example, in a customer support AI application, the system must be able to rapidly access detailed help desk data to quickly and accurately respond to customer questions.

To facilitate quick data retrieval without compromising accuracy, your AI system must have at least one of the following capabilities:

• Organized indexing for retrieval at scale.
• Metadata creation or extraction to filter down candidates and improve retrieval.
• Retrieval algorithms that can handle different query types and make sense of imprecise or poorly worded questions.
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Prepare Your Data for AI: The 7 Pillars of Data Governance

Let’s explore the seven data governance pillars that will prepare your data environment for AI. These pillars serve as strategic guideposts, ensuring data governance best practices are in place, enabling your organization to harness the full potential of AI technologies.

We will discuss why each pillar matters, identify key challenges you may encounter, and demonstrate how Pryon’s enterprise-ready ingestion and retrieval systems can help you build a solid foundation for achieving AI readiness.
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1. Data Quality Management

Data governance and data quality go hand in hand. Data quality management ensures the accuracy, completeness, and reliability of your data. It is important for training effective AI models that empower your teams to make informed decisions.
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Key challenges to overcome:

• Enterprise data can be messy, often containing duplicate and outdated versions of content.
• Content landscape is constantly evolving, with regular updates and changes.
• Content exists in many formats and file types.
• Content can be noisy with embedded graphics, drawings, and handwritten notes.
• Context and meaning of content are difficult to ingest, retrieve, and retain.
• Users receive partial answers because semantically consistent content, like lists, aren't chunked and stored together.
• Generative LLMs are prone to hallucinations.
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How Pryon can help:

• Easy no-code collection management that data owners and stewards can curate effortlessly.
• Automatic content updates at set intervals ensure content stays fresh and up to date.
• Multi-modal support for text, images, audio, and video.
• Proprietary optical character recognition (OCR) extracts text in reading order from images, graphics, schematics, drawings, and even handwritten notes.
• Vision segmentation identifies and labels important content elements like the table of contents, headers, footers, citations, and body text.
• Content normalization and filtering remove unnecessary objects, like non-ASCII characters, to keep things clear and avoid any context confusion.
• Visual semantic segmentation assembles smarter chunks from atomic document elements.
• Source attribution, particularly fine-grained attribution, delivers more trustworthy responses.
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2. Data Security and Privacy

Data security and privacy involve safeguarding sensitive information from unauthorized access and ensuring personal data is managed according to legal and ethical standards. It is essential for maintaining customer trust, complying with regulations, and protecting proprietary data.
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Key challenges to overcome:

• Maintaining data governance and security of sensitive information.
• Complying with data privacy laws such as GDPR, CCPA, and HIPAA.

How Pryon can help:

• Document-level access controls ensure that only authorized users can view content.
• Document-level privacy settings carry over through the entire query and response exchange, so only authorized users will receive responses based on restricted content.
• Pryon models are never trained on customer data.
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3. Data Architecture and Integration

Data architecture and integration involves creating a unified framework that organizes and connects your data across various databases and applications. This ensures data flows smoothly, allowing AI systems to analyze comprehensive datasets and provide accurate insights.
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Key challenges to overcome:

• Integration with existing data sources.
• Ensuring data interoperability and reducing silos.
• Building a scalable infrastructure to accommodate growing data needs without compromising performance.
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How Pryon can help:

• Pre-built connectors for common enterprise source systems including Microsoft SharePoint, Confluence, AWS S3, Google Drive, Zendesk, ServiceNow, Salesforce, and Documentum.  
• Proprietary Universal Connector Framework makes it easy to build custom connectors for additional source systems.
• Point-and-click interface allows users to create a composite retrieval knowledge base from different content repositories without needing to consolidate the content.
• Enterprise-grade Ingestion, Retrieval, and Generative Engines have been designed, tested, and deployed to serve as building blocks of a common enterprise RAG architecture and support various use cases.
• SOC 2-compliant and available with on-premises and air-gapped deployment options.

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4. Metadata Management

Metadata captures detailed information about your data's lineage, usage, and transformations to enhance an AI model's understanding. It provides the context and structure to your data that an AI system needs to deliver high-value, accurate, and fast responses.

Key challenges to overcome:

• Systematically creating and maintaining comprehensive metadata for data sources used in AI applications.

How Pryon can help:

• Captures detailed metadata during ingestion, such as date, author, version, and topic.
• Creates additional metadata during ingestion, such as layout analysis and named entity recognition to further improve retrieval accuracy.
• Retrieves all metadata along with the extracted answer to add valuable context for generative LLMs when responding to user queries.
• Granular source attribution allows users to view the document and trace each part of their response back to specific snippets in the source.

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5. Data Lifecycle Management

Data lifecycle management is all about keeping track of data as it moves from creation and storage to archiving and deletion. It helps ensure that data stays up to date, which is key for building reliable and efficient AI models.
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Key challenges to overcome:

• Ensuring knowledge base always stays current.
• Actively managing the knowledge base.

How Pryon can help:

• Intuitive no-code interface lets admins add, remove, and update content effortlessly. Admins can also include verified answers for questions that need set responses.
• Easy point-and-click experience for admins to modify content and roll back changes if content is accidentally deleted.

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6. Regulatory Compliance

Regulatory compliance ensures that your use of AI meets industry standards and legal requirements, shielding your organization from potential legal troubles and keeping consumer trust intact.

Key challenges to overcome:

• Data governance and compliance with laws, industry standards, and regulations, including data protection laws, specific guidelines, and internal policies.
• Protecting sensitive data.
• Ensuring third-party vendors involved in the AI implementation also meet data governance compliance requirements.

How Pryon can help:

• Comprehensive admin and configuration settings to implement guardrails for generated responses.
• Document-level access controls ensure only authorized users can view restricted content or receive related responses.
• Can be deployed in on-premises and air-gapped environments to meet regulatory compliance.

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7. Data Stewardship

Data stewards manage an organization's data assets to ensure accuracy, consistency, and availability. Their role is crucial in ensuring that all data, including structured and unstructured data, used for AI training and responses is reliable.

Key challenges to overcome:

• Designated data stewards need to constantly maintain data quality and governance within the AI implementation.
• Skill gaps for management of unstructured content.

How Pryon can help: ‍

• No-code interface for adding, removing, and updating content makes it easy for data stewards to manage data quality for unstructured and structured data.
• Admins can easily control how all data is used in each collection.

Data Governance: The Non-Negotiable Foundation for AI Success

Robust data governance is the cornerstone of any successful AI venture. Without it, efforts to harness AI's transformative potential are fundamentally undermined. Only through meticulous attention to data quality, security, and lifecycle management can you truly unlock AI's potential.

Get AI-Ready with Pryon

Pryon’s enterprise-grade Ingestion and Retrieval Engines can help you build a solid foundation for achieving AI readiness. Request a demo and a member of our team will connect with you to show you how.

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