Enabling AI at Scale with Unstructured Data Integration and Governance

When it comes to agentic AI, leveraging enterprise data is one of the most effective ways to boost output quality and gain a competitive edge. Increasingly, organizations are turning to unstructured data—text, images, video, IoT sensor data, and more—for its rich potential to power generative AI (gen AI).

Yet despite making up over 90% of enterprise data and growing three times faster than structured data, less than 1% of unstructured data is being used in gen AI today, according to the International Data Corporation (IDC).

This gap reveals a fundamental challenge: while unstructured data’s potential to drive the next wave of AI innovation is enormous, most of it remains inaccessible and ungoverned. A mountain of technical and operational barriers still stands in the way of even the most ingenious data teams.

Data teams are vital for improving data quality and supporting AI and analytics. Yet data science teams dedicate most of their time to processing data for downstream use—few tools can manage unstructured data effectively, highlighting the need for scalable solutions. Data teams face numerous challenges when trying to manage unstructured data for AI, including:

• Handling diverse file types and preprocessing unstructured data for downstream use
• Managing multiple different versions of unstructured documents, or tracking changes that occur within source documents
• Manually filtering irrelevant document content to ensure that only high-quality, valuable information is fed into the model
• Identifying and addressing sensitive information, such as personally identifiable information (PII), within unstructured documents
• Large volumes of unstructured data—combined with data silos, poor search functionality and a lack of indexing or tagging—make it difficult to locate, access and monetize organizational knowledge

These challenges underscore the growing need for an automated solution that can efficiently process unstructured data at scale, transforming messy, raw inputs into clean, usable assets for downstream applications. Historically, the status quo has required reliance on several piecemeal tools that need stitching, custom code or third-party integrations.

Enter unstructured data integration (UDI), an emerging concept that re imagines the traditional extract, transform, load (ETL) process for unstructured data. UDI is an end-to-end workflow that connects to raw, unstructured data sources and enhances data quality by structuring, enriching and cleansing the data to remove things such as PII. It then delivers the refined output to systems ready for use, whether that’s a vector database, large language model (LLM) or analytics engine.

Rather than relying on slow, error-prone manual processes, data teams can implement scalable, reusable pipelines. This process can automate the entire integration lifecycle with the revolutionary concept of running this entire process from a single, integrated experience. This unified approach not only accelerates time-to-value but also frees up engineers to focus on higher-impact work, while unlocking a rich source of data for a wide range of use cases.

Use cases
 

Given the critical role of unstructured enterprise data in powering AI, one of the most impactful use cases for UDI is retrieval-augmented generation (RAG).

To support RAG, UDI should go beyond traditional ETL heuristics for tabular data and include capabilities such as text chunking, embedding generation and vectorization. It must also integrate seamlessly with the RAG stack’s key components or offer these integrations natively. Examples include chunking frameworks such as LangChain, embedding models such as Slate or Word2Vec, and vector databases such as Milvus or Pinecone.

The value of unstructured data integration—as opposed to a stand-alone solution—is compounded when embedding this technology in data integration and lakehouse solutions. This approach allows organizations to unify unstructured data with structured data. Unifying these two data types unlocks deeper insights that neither type can offer alone.

When combined, unstructured and structured data enables more powerful analytics, such as identifying customer behavior patterns, predicting trends or detecting anomalies. This integration supports more accurate AI and machine learning models, improves operational efficiency and enhances decision-making by providing a richer, 360-degree view of business challenges and opportunities.

In addition to RAG and unifying unstructured with structured data, there are other significant use cases for this technology:

• Agentic workflows: Turns unstructured content into structured insights that AI agents can understand and use, enabling intelligent automation such as real-time customer support and risk detection
• Training AI models: Cleans and organizes raw content—such as documents, images and audio—into high-quality inputs that are ready for AI training. This training helps improve model accuracy

How tools can help
 

To address these challenges, organizations should look to streamline how unstructured data is prepared for AI and analytics. Leading practices include automating the ingestion of raw unstructured data, enabling intuitive transformation through visual or code-based interfaces, and integrating outputs directly into downstream systems such as vector databases. By building reusable and repeatable pipelines, data teams can significantly reduce manual effort and accelerate the preparation of unstructured data for enterprise-scale AI initiatives.

Unstructured data integration is only one piece of the puzzle—governance is equally critical to ensuring reliable AI outcomes. Enterprises manage millions of unstructured assets, making it essential to know where data resides, what it contains, how it’s classified and whether it includes sensitive information.

Gaining visibility helps organizations better understand their unstructured data landscape so they can determine whether the data is suitable for powering AI outputs or of high enough quality to train AI models.

Unstructured data governance (UDG) refers to the application of governance principles—such as data quality, lineage, access control, privacy and compliance—to data that does not reside in structured formats. It ensures that unstructured content is managed with the same rigor as structured data, enabling organizations to extract value while mitigating risk.

The urgency for this discipline is growing rapidly. As generative AI and large language models gain traction, unstructured data is becoming a primary input for model training and inference. However, when this data lacks proper oversight, organizations face significant challenges: regulatory exposure, biased or inaccurate AI outputs, and inefficiencies in data access and usage.

Robust unstructured data governance enables enterprises to:

• Safeguard sensitive content through automated classification, policy enforcement and redaction.
• Promote responsible AI by providing transparency and traceability into the data powering AI systems.
• Accelerate data discoverability and reuse by enriching content with metadata and organizing it for consumption.
• Maintain regulatory compliance with frameworks such as GDPR, HIPAA and other jurisdictional mandates.

Use cases
 

• In healthcare, unstructured data governance supports the protection of patient information in clinical notes and diagnostic imagery, while enabling AI-assisted analysis.
• In financial services, institutions are securing customer interactions—such as call transcripts and chat logs—for fraud monitoring and audit readiness.
• In manufacturing, companies are governing technical manuals, sensor logs and maintenance records to feed AI models for predictive maintenance and operational optimization.

How tools can help:
 

To turn unstructured data from a liability into a strategic asset, organizations should adopt a modern, automated approach to governance. Best practices include implementing an end-to-end workflow that enables teams to connect to unstructured sources. Also, this approach allows teams to extract key entities, enrich metadata for context and discovery, and track data lineage to ensure transparency and trust. Ideally, this process should be managed through a unified interface that simplifies oversight and integrates seamlessly with existing governance and security frameworks.

IBM’s unstructured data integration (UDI) and unstructured data governance (UDG) capabilities come unified in watsonx.data®. The tool represents the evolution of IBM’s hybrid, open data lakehouse—now enhanced with integrated data fabric capabilities to help manage the entire lifecycle of data for AI.

• Unstructured data integration: Powered by watsonx.data integration, this capability enables data teams to load, transform and preprocess unstructured data at scale.
• Unstructured data governance: Powered by watsonx.data intelligence, this feature unlocks curation, governance and cataloging of unstructured data.

Together, these innovations equip data teams to manage the entire lifecycle of both structured and unstructured data, from a single, unified experience.