

The Next Phase of Big Data: Integrating Large Language Models
By Andrew Linn, Chief Innovation Officer, Knowli Data Science
Across state and federal agencies, large volumes of data are collected and analyzed daily to inform policy, enhance services, and improve operational efficiency. As the scale and complexity of this data grows, so do the technical challenges of unlocking its full potential.
Data lakes have been critical in addressing the fundamental needs of big data management by enabling organizations to store structured, semi-structured, and unstructured data in its raw format. Data catalogs, in turn, provide a way to organize this data, adding metadata that tracks its origin, quality, and lineage. Together, these tools address the challenges of managing big data, enabling better searchability, collaboration, and governance.
Yet, these tools fall short when it comes to making unstructured data usable. Documents, reports, and other text-heavy files are often difficult to analyze due to their lack of predefined structure. Traditional methods struggle to integrate this information into reporting frameworks, leaving a vast number of potential insights untapped. This is where large language models (LLMs) are poised to drive the next phase of big data innovation.
The Power of Large Language Models
Large language models, such as BERT (Bidirectional Encoder Representations from Transformers), are trained on massive datasets to process and understand natural language. When paired with generative AI (GenAI), these models can also create content, such as summarizing reports or drafting recommendations based on data trends. These models make it possible to extract valuable insights from unstructured data, enabling entirely new capabilities for government agencies:
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Data Extraction and Structuring: LLMs can parse unstructured documents such as policy manuals or legal texts and convert them into structured formats for reporting and analysis.
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Generative Insights: Integrating LLMs with GenAI opens possibilities for summarizing long documents, drafting reports, or even creating narratives based on data trends.
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Conversational Interfaces: By combining LLMs with chat-style tools, users can interact with their data conversationally, asking questions, exploring relationships, and gaining real-time insights.
These capabilities are not limited to textual data. When paired with structured datasets already housed in data lakes or warehouses, LLMs can offer a unified view of all organizational data, enhancing analysis and decision-making.
Unlocking the Potential of Unstructured Data
Unstructured data has historically been difficult to integrate into decision-making processes due to its variability and lack of predefined format. However, with LLMs, this data becomes a valuable resource.
For example, LLMs can be used to automatically extract key themes and generate actionable insights from qualitative research data, such as free-form survey responses, by performing tasks like coding and pattern recognition. These capabilities extend to policy analysis and program evaluation, where LLMs can be used to review public comments, stakeholder feedback, and academic publications to assess program effectiveness or identify emerging issues. Additionally, LLMs can enhance compliance processes by detecting discrepancies or risks in contract language and audit reports.
While these are just a few examples, such applications highlight the potential of LLMs to uncover insights from data sources that were often previously ignored or underutilized. As their adoption grows, LLMs will continue to open new opportunities for leveraging unstructured data in a variety of contexts.
Challenges and Considerations
As with any technological advancement, the integration of LLMs requires careful planning. While these technologies offer enormous potential, agencies must remain vigilant about data quality. The old adage "garbage in, garbage out" applies: insights derived from poorly maintained or incomplete data will be unreliable, regardless of the sophistication of the tool. Proper governance, documentation, and validation of source data are essential to maximize the value of LLMs and generative AI.
Equally important is ensuring a balance between innovation and security when deploying these models. Pre-trained and customizable LLMs can be implemented directly within an agency’s environment, eliminating the need for external calls to outside servers and reducing the risk of data exposure. These private models allow agencies to innovate and unlock the full potential of LLMs while maintaining trust and security with full control over their proprietary and sensitive data.
A New Era in Big Data Management
Large language models represent a significant step forward for government agencies seeking to leverage all available data. By combining advancements in data storage, cataloging, and AI-driven analytics, agencies are better positioned than ever to turn raw information into actionable insights, shaping smarter policies and improving public services.
This next phase of big data management isn’t about replacing the foundational tools like data lakes or traditional data analytics – it’s about building on them. With LLMs, agencies can move from managing their unstructured data to actively harnessing it, ensuring that their vast repositories of information drive smarter policies and more impactful outcomes. As the pace of innovation accelerates, agencies that adopt large language models today will be better positioned to lead in the data-driven era.