How Tracing Unstructured Data Lineage Can Make or Break GenAI Success

Businesses have leveraged structured data for decades to make business decisions. But they also have been generating a substantial amount of unstructured data in the form of reports, emails, texts, voicemails, photos, illustrations, and videos. And, of course, a whole lot of social media posts and messages in recent times. Despite the immense value this data holds, its lack of a defined structure has prevented businesses from using and managing it effectively. An IDC study notes that even though the volume and variety of unstructured data is vastly greater than that of structured data, less than half of all unstructured data is shared, analyzed, or reused.

GenAI (Generative AI) has changed the situation of unstructured data lying unused in the numerous silos of your enterprise data. For the first time in history, the real worth of unstructured data is in the limelight. Unstructured data is now at the epicenter of the GenAI revolution, powering wide-ranging use cases from aviation to retail and medicine to research.

No wonder 80% of the CDOs agree that GenAI would eventually transform their organization’s business environment. While this is a positive shift to leverage this vast treasure of information, the safe use of unstructured data is essential for GenAI's success. One of the central pillars of its safe and compliant use is data lineage, knowing where the data has originated and how it has been transformed across its GenAI lifecycle.

Why Unstructured Data Lineage is Critical for GenAI

Google Cloud Chief Evangelist Richard Seroter says, “If you don’t have your data house in order, AI is going to be less valuable.” Getting your data house in order for GenAI requires a multipronged approach, which we covered in our earlier blogs on data intelligence and data quality. In this blog, let us focus on data lineage.

Harnessing unstructured data safely is crucial, as it often contains sensitive and proprietary information. Data lineage plays a critical role in it by tracing data sources, destinations, transformations along the way, and usage. It can help you optimize operations, enhance security, and support better decision-making through improved data governance and traceability.

For GenAI, tracing the data mapping and flow from data systems to vector databases, LLMs (Large Language Models), and final endpoints is essential. It provides an end-to-end lineage view, helping monitor GenAI model inputs, identify contributing sources, verify response integrity, and protect sensitive data.

Effective management of data lineage can enhance transparency, compliance, and trust in GenAI outputs, making it a critical factor that can make or break GenAI success. The volume and variety of unstructured data, coupled with the loss of associated metadata—such as sensitivity, access, and regulatory intelligence—make tracing its lineage extremely challenging.

Challenges in Tracing the Lineage of Unstructured Data

Tracing the lineage of unstructured data poses several challenges due to its inherent nature of diverse formats and the complexity of its handling. Here are some key challenges:

1. Massive volumes and data silos: Enterprises generate large quantities of data every day, and 90% of it is unstructured. Moreover, this data streams in real-time and goes through several processes. Keeping track of this data across organizational silos is not easy.
2. Complex data systems: Modern data systems can be complex. When data moves across several systems, tracking the transformations at every stage can be difficult.
3. Tool limitations: Traditional tools rely on structured ETL processes for tracing lineage. Unstructured data requires a new approach to infer lineage by changes in content and metadata.
4. Metadata limitations: Unstructured data and embeddings may lack clear and complete metadata associated with them, such as file date, ownership, and sensitive information. A context needs to be built around unstructured data to fully understand it.
5. Sensitive Information: Unstructured data can contain sensitive or personally identifiable information (PII). Proper handling and strict access control are crucial when tracing its lineage.

The challenges in tracing the lineage of unstructured data arise from its large volumes, complex transformations, limited tool support, and privacy concerns. Addressing these challenges requires advanced technologies and robust methodologies.

Data Lineage: Structured vs. Unstructured Data

How Securiti Delivers Unstructured Data Lineage for GenAI Success

For unstructured data, Securiti infers data lineage by tracking changes in content and metadata. The changes are documented and analyzed to build data and flow mapping. Metadata and context are preserved before the data is chunked and loaded into the vector database. This approach provides a complete view of the data map. For example, a clear visual map can provide information about where the specific unstructured data originated, how it was processed, how it was used during RAG (Retrieval Augmented Generation), model fine-tuning, or model training models, and how it was finally consumed by the end-user or system.