How to Extract Insights From Unstructured Data

Stop pasting interview transcripts into ChatGPT and asking for a summary. You’re not getting insights—you’re getting blabla. Here’s how to actually extract signal from qualitative data with AI. A lot of product teams are experimenting with AI for user research. But most are doing it wrong. They dump all their interviews into ChatGPT and ask: “Summarize these for me.” And what do they get back? Walls of text. Generic fluff. A lot of words that say… nothing. This is the classic trap of horizontal analysis: → “Read all 60 survey responses and give me 3 takeaways.” → Sounds smart. Looks clean. → But it washes out the nuance. Here’s a better way: Go vertical. Use AI for vertical analysis, not horizontal. What does that mean? Instead of compressing across all your data… Zoom into each individual response—deeper than you usually could afford to. One by one. Yes, really. Here’s a tactical playbook: Take each interview transcript or survey response, and feed it into AI with a structured template. Example: “Analyze this response using the following dimensions: • Sentiment (1–5) • Pain level (1–5) • Excitement about solution (1–5) • Provide 3 direct quotes that justify each score.” Now repeat for each data point. You’ll end up with a stack of structured insights you can actually compare. And best of all—those quotes let you go straight back to the raw user voice when needed. AI becomes your assistant, not your editor. The real value of AI in discovery isn’t in writing summaries. It’s in enabling depth at scale. With this vertical approach, you get: ✅ Faster analysis ✅ Clearer signals ✅ Richer context ✅ Traceable quotes back to the user You’re not guessing. You’re pattern matching across structured, consistent reads. ⸻ Are you still using AI for summaries? Try this vertical method on your next batch of interviews—and tell me how it goes. 👇 Drop your favorite prompt so we can learn from each othr.

For decades, organisations have managed their data in two separate worlds. On one side is structured data - numbers, categories, and neatly organised information - stored safely in databases and easily processed by machines. On the other side is unstructured data - the rich, nuanced content buried in emails, chat logs, documents, images, and social media comments - largely out of reach for computers. 🔵 LLMs Changed The Game: LLMs can now sift through mountains of text to uncover insights and connections, understanding sentiment, context, and relationships in ways that were previously impossible. Suddenly, unstructured data can be treated as if it were structured. But traditional tabular databases are too rigid to handle the complex, nuanced relationships revealed in this data. 🔵 Knowledge Graphs Structure Complex Data: This is where knowledge graphs come in. They offer a more flexible and expressive way to structure data, capable of modelling complex networks of information. With knowledge graphs, you can transform unstructured text into triples - subject > predicate > object - and these triples together form a graph that connects your data in a meaningful, machine-readable way. 🔵 Bridging Structured and Unstructured Worlds:  But extracting insights isn’t enough. The real power lies in weaving those insights back into your core business systems. You don’t want to discard the well-structured data you’ve carefully curated in databases over the years. The opportunity is in linking the two together - integrating structured data points with insights mined from unstructured content. You can treat your tabular data as a graph as well, mapping the rows and columns into triples. This is what we knowledge graph folk have been doing for years. 🔵 The Power of URLs: Imagine every client, product, or asset in your organisation having a unique URL identifier - like a web address, but for an entity in your data. Whether they appear in a database, an email, or a customer support chat, every reference points back to the same URL, giving you a single source of truth across all systems. Even better, if you want to link two entities together, you can simply use their URLs - subject URL > predicate > object URL - it’s as straightforward as adding a hyperlink to a webpage! 🔵 This Is a Strategic Shift in Thinking: This isn’t just about tidying up your data infrastructure. It’s about making a strategic shift to unlock new capabilities. Patterns emerge. Redundancies disappear. Decision-making becomes faster, more precise, and better informed. you are ready for the Age of AI. ⭕ What is a Triple: https://lnkd.in/e-hr5eQK ⭕ What is a Knowledge Graph: https://lnkd.in/eG8DhxVn

Introducing Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data ... Did you know that 80% of enterprise data resides in unstructured formats? This makes it incredibly challenging to extract meaningful information and gain insights ... 🤔 Addressing the Challenge of Unstructured Data A recent research paper introduces Docs2KG, a novel framework for constructing unified knowledge graphs from heterogeneous and unstructured data sources like emails, web pages, PDFs, and Excel files. The key innovations include: 1. Flexible and dynamic knowledge graph construction that adapts to various document structures and content types, unlike existing approaches limited to specific domains or schemas. 2. A dual-path data processing strategy combining deep learning document layout analysis and markdown parsing to maximize coverage of different document formats. 3. Integration of multimodal data (text, tables, images) into a unified knowledge graph representation with structural and semantic relationships. 4. Facilitation of real-world applications like reducing outdated knowledge in language models and enabling retrieval-augmented generation. 5. Open-source availability encouraging further research and development. 💪 Strengths: - Addresses the crucial challenge of extracting insights from the vast amounts of unstructured enterprise data residing in data lakes. - Offers flexibility and extensibility to handle diverse document types across industries. - Leverages advanced AI/ML techniques for document understanding and information extraction. - Unified knowledge graph representation enhances data integration, querying, and exploration capabilities. - Open-source nature promotes collaboration and accelerates innovation. 👉 Potential Limitations: - Performance may vary based on the complexity and quality of input documents. - Integrating information across highly heterogeneous sources could be challenging. - Maintenance and updating of the knowledge graph as new data arrives needs to be addressed. 👉 Opportunities: - Enhance enterprise knowledge management and decision-making processes. - Enable new AI applications by providing structured, integrated data to train language models. - Extend the framework to support additional document types or modalities. - Explore domain-specific customizations or industry-focused solutions. 👉 Risks: - Adoption may be hindered if the system cannot handle proprietary or highly specialized document formats. - Data privacy and security concerns need to be carefully addressed, especially for sensitive information. - Reliance on external open-source libraries and models could introduce vulnerabilities or dependencies.

Unlocking Hidden Insights: How #Snowflake's AI_EXTRACT is Revolutionizing Data from Scans and Engineering Drawings For industries like Energy, Manufacturing, Infrastructure, and Finance, vital data is often sitting in scanned PDFs, images, and engineering drawings. Traditional methods are slow, manual, and rely on complex custom code or basic OCR that misses context. Enter AI_EXTRACT.. Snowflake's AI_EXTRACT (powered by Cortex AI) is an innovative LLM function that instantly extracts this data. It provides sophisticated content understanding—not just raw text. The Fun part? No complex model training or custom machine learning. You simply use a SQL query to tell the AI, in plain English, what information to extract from your staged document. Use Case: Engineering Drawings Imagine an energy company needing to inventory grid assets. Their data (e.g., base voltage, transformer counts) is trapped in complex schematics (datablock-1.pdf). Using AI_EXTRACT, you ask specific questions: "What is the Base Voltage of Bus 77?" and "How Many Transformers are in the diagram?" and the function returns the values directly as structured, queryable columns. Top Benefits: - Speed & Efficiency: Automate data extraction that once took hours of manual effort. - Accuracy: Reduce human error and gain structured data, not just raw text. - No Code/Low Code: Integrate powerful AI directly into your existing SQL workflows. - Scalability: Effortlessly process thousands of documents stored in the Snowflake Data Cloud. - Accessibility: Unlock data previously stuck behind specialized, expensive tooling. Key Industry Applications: - Utilities & Energy: Digitizing grid assets, infrastructure maps, and maintenance records. - Manufacturing: Extracting specifications from product designs and assembly instructions. - Construction & Engineering: Pulling crucial details from blueprints, schematics, and project documentation. - Finance & Legal: Automating data capture from legacy contracts, applications, and legal documents. #AI #GenAI #unstructureddata

Learn problem framing before AI. Learn data curation before RAG. Learn ground truth before “LLM-as-a-judge.” Learn context engineering before multi-agent AI. Learn observability before deployment. Learn evaluation before scaling anything. RAG isn’t just retrieval + generation. It’s how you turn unstructured knowledge into a governed reasoning loop. Here’s the blueprint that actually ships. 1. Problem → Retrieval Objective Every strong RAG starts with defining what you’re retrieving and why. ↳ Clarify the intent: lookup, reasoning, or synthesis. ↳ Identify which data sources truly hold the answer. ↳ Define the expected output form: citation, snippet, summary, or decision aid. ↳ Then design your retrieval to serve that goal Without this alignment, every downstream step is noise. 2. Data Curation > Vectorising Internal Docs My first RAG, I dumped every internal wiki and doc into the pipeline, and it failed miserably. The information was there, but it wasn’t usable. ↳ Stitch related docs and close knowledge gaps before ingestion. ↳ Rewrite ambiguous text into task-relevant form. ↳ The best retrieval quality starts with curated structure, not volume. You don’t feed raw knowledge, you model it. 3. Chunking is Context Engineering Chunking isn’t about tokens, it’s about meaning boundaries. ↳ Segment by semantic units: definitions, procedures, FAQs, decisions. ↳ Preserve hierarchy: titles, headers, and relationships. ↳ Add connective tissue: short summaries that give each chunk standalone meaning. ↳ Test retrieval overlap: too small loses context, too large dilutes it. 4. Retrieval that actually retrieves ↳ Hybrid search (BM25 + vectors) → rerank. ↳ Domain-tuned embeddings when language is specialised. ↳ Routing/sub-queries for multi-facet questions. ↳ Tune your retriever to return diverse evidence; each chunk should add context the model didn’t already see. 5. Prompts as a lifecycle, not text ↳ Version in Git. ↳ Unit + regression tests tied to eval sets. ↳ A registry for safe rollout. You don’t YOLO prompts into prod. 6. Evals: the chicken-and-egg you must solve Most RAG metrics don’t help on day one, “LLM-as-a-judge” can grade a rubric, but without ground truth the score is noise. ↳ Start small: manually curate a seed Q/A set for your real tasks. ↳ Avoid synthetic Q/A from your own chunks as the only source (train-test contamination risk). ↳ Grow ground truth from user feedback (thumbs, edits, selected citations). ↳ Track per-query traces: input → sub-queries → retrieved chunks → final answer → citation correctness. Observability, Guardrails, Cost/Latency ↳ Log retrieval coverage, overlap, and dead-ends. ↳ Validate citations point to supporting text. ↳ Cache/rerank to cut tokens without cutting truth. ↳ Fail safe: when unsure, ask for clarification, don’t hallucinate. Stop wiring demos. Engineer retrieval, Then earn your evals. ♻️ Repost to help your team stop guessing and start measuring.

Structured reliability for unstructured intelligence. This week, we had the privilege of hosting Liana Patel, a Stanford PhD researcher and creator of LOTUS, an open-source system for LLM-powered data processing with accuracy guarantees. Key Learnings: - LOTUS introduces semantic operators such as filter, join, top-k, and aggregate that extend pandas with relational-style operators for unstructured data. - Each operator is parameterized by natural language expressions like “the paper title is the funniest,” turning LLM reasoning into declarative queries. - Under the hood, LOTUS handles batching, context-length management, and cost-based planning to keep LLM pipelines efficient and accurate. - Its optimizer uses model cascades and sampling-based thresholds to guarantee precision and recall targets while reducing cost by orders of magnitude. It bridges two worlds: the rigor of relational systems and the flexibility of language models, allowing users to bring database-style declarative programming to unstructured data. From analyzing research papers and sales call transcripts to building agent-trace dashboards, Lotus shows how structured reasoning can finally meet unstructured intelligence. Follow Liana: https://lnkd.in/gd7zMEYD Lotus Repo: https://lnkd.in/gG4WTxbg Paper: https://lnkd.in/gEusnP64 #AI #LLM #DataSystems #Research #Databases #OpenSource

How Embeddings and Clustering Reduced File Understanding Time by 96% 📉 A case study from Dropbox. 🔍📄 Reading through entire documents to extract key information is time-consuming and inefficient. 🐌 Dropbox aimed to automate and accelerate this process. Dropbox's engineering team introduced AI-powered summaries and Q&A for file previews. This solution works in two phases 🏗️: 🔎 Phase 1: Text Extraction and Embedding  • Riviera converts any file type into text  • Text is split into paragraph-sized chunks  • Each chunk is converted into vector embeddings • Embeddings are cached to improve efficiency for subsequent operations  • This system processes nearly an exabyte of data daily through 300 supported file types 🧠 Phase 2: Content Understanding  • For summaries: K-means clustering identifies diverse, representative chunks • For Q&A: Embeddings match question to relevant text chunks  • Dynamic context selection determines how much context to provide • Direct questions receive fewer, more relevant chunks while broad questions get more context • The system provides source references so users can verify information 📉 The Results • Processing time reduced by 96% (115s → 4s) • Cost-per-summary cut by 93% This combination of intelligent chunking, strategic embedding, and dynamic context selection proves to be a powerful approach for extracting meaning from unstructured data at enterprise scale. 💪

I’ve advised 100s of organizations in my career. The secret formula to harness unstructured data: Over the last decade, I’ve helped companies navigate the complexities of digital transformation. I’ve also managed data strategies for major enterprises. During that time, I've identified 5 critical components for effective unstructured data management: → Analysis: to derive insights from diverse data sources → Storage: to handle vast amounts of data efficiently → Retrieval: to access information quickly and accurately → Governance: to ensure compliance and security → Integration: to combine structured and unstructured data for a holistic view ... As well as what happens when each is missing. • Lack of analysis = "Missed Insights" • Poor storage = "Data Overload" • Inefficient retrieval = "Lost Opportunities" • Weak governance = "Compliance Risks" • No integration = "Fragmented View" And remember, mastering unstructured data is a continuous journey. You can improve in each of these areas. Here's how to do it: 𝟭/ 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Invest in advanced analytics and machine learning technologies. Use natural language processing and sentiment analysis to understand customer feedback. 𝟮/ 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Implement scalable storage solutions that can grow with your data needs. Consider cloud-based options for flexibility and cost-effectiveness. 𝟯/ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Develop robust search capabilities to find and use data quickly. Use metadata and tagging systems for better organization. 𝟰/ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Create policies for data categorization, security, and compliance. Regularly audit your data management practices. 𝟱/ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Ensure your unstructured data systems work seamlessly with your structured data. Use data integration tools to get a comprehensive view of your operations. The best organizations constantly adapt and innovate. Start using this formula today. And unlock the full potential of your unstructured data. Your business will thank you!

Google AI Releases LangExtract: An Open Source Python Library that Extracts Structured Data from Unstructured Text Documents Google’s LangExtract is an open-source Python library designed to extract structured, traceable information from unstructured text—such as clinical notes, customer emails, or legal documents—using large language models like Gemini. The tool leverages user-defined prompts and few-shot examples to reliably enforce output schemas and precisely map every extracted detail back to its source, enabling full auditability and rapid validation. LangExtract is optimized for handling large documents via chunking and parallelization, and it generates interactive HTML visualizations for easy review. In contrast to many generic LLM wrappers, LangExtract introduces robust controls for schema adherence, traceability, and explainability, making it suitable for sensitive domains like healthcare or compliance. Recent releases allow direct extraction from URLs and incorporate multi-pass extraction for improved recall on lengthy texts. Data from Google’s own demonstrations and user projects show extraction of hundreds of data points from single novels or bulk document sets, all with transparent provenance. LangExtract’s rapid adoption reflects a growing need for reliable, explainable AI-powered information extraction pipelines in research, business intelligence, and regulated industries..... Full Analysis: https://lnkd.in/eHTYShme GitHub Page: https://lnkd.in/epRRpMpg Google Google for Developers Akshay Goel, M.D. Atilla K.

Data is only as valuable as your ability to understand it. Let’s say you conduct an Employee Engagement Survey. You ask a simple question: "How can we make this company a better place to work?" Responses come in: ➡️ “Increase my salary.” ➡️ “Better pay would help.” ➡️ “We’re underpaid.” Different wording. Same message. But here’s where most companies struggle: Traditional data tools can’t recognize patterns in unstructured responses. You can’t run an SQL query on free-text feedback. And that’s a problem. Because without structure, insights remain hidden. 💡 Enter Natural Language Processing or NLP. With NLP tools, we can read, categorize, and transform messy, unstructured data into clear, actionable insights. Now, instead of drowning in a sea of random responses, you get: 🔍 52% of employees want higher pay. 🔍 24% need career growth opportunities. 🔍 13% seek more flexibility. Suddenly, you’re not guessing. You’re making data-driven decisions with confidence. This is how AI is reshaping business strategy today. It’s eliminating blind spots. It’s making organizations smarter.  It’s bridging the gap between intuition and intelligence. Companies that fail to leverage AI in data analysis aren’t just missing insights. They’re missing opportunities. Are you making decisions based on assumptions or real data?