How to Transform Unstructured Data Into Actionable Insights

One of the most powerful uses of AI is transforming unstructured data into structured formats. Structured data is often used for analytics and machine learning—but here’s the critical question: Can we trust the output? 👉 Structured ≠ Clean. Take this example: We can use AI to transform retail product reviews into structured fields like Product Quality, Delivery Experience, and Customer Sentiment, etc. This structured data is then fed into a machine learning model that helps merchants decide whether to continue working with a vendor based on return rates, sentiment trends, and product accuracy. Sounds powerful—but only if we apply Data Quality (DQ) checks before using that data in the model. Here’s what DQ management should include at least the following: 📌 Missing Value Checks – Are all critical fields populated? 📌 Valid Value Range: Ratings should be within 1–5, or sentiment should be one of {Positive, Negative, Mixed}. 📌 Consistent Categories – Are labels like “On Time” vs “on_time” standardized? 📌 Cross-field Logic – Does a “Negative” sentiment align with a “Excellent product quality” value? 📌 Outlier Detection – Are there reviews that contradict the overall trend? For example, a review with all negative fields but field "Recommend Vendor” has “Yes". 📌 Duplicate Records – Same review text or ID appearing more than once. AI can accelerate many processes—but DQ management processes is what make that data trustworthy.

AI is only as smart as the data you feed it. Most HR teams already have the data. But it’s buried in the wrong formats. At Fig Learning, we help HR leaders unlock it. Here’s how to make your data AI-ready. Structured vs. Unstructured: What’s the difference? Structured = ready to use. Labeled, searchable, clean data in tools like LMSs. Unstructured = hidden value. Think emails, transcripts, PDFs, and feedback notes. Structured data is plug-and-play. Unstructured data needs work - but holds gold. Step 1: Audit your data sources Where does learning actually live right now? Start by mapping your tools, folders, and files: - LMS reports? - Post-training surveys? - Feedback forms? - Meeting notes? Inventory what you touch often but never analyze. Step 2: Prioritize what to work on Not all messy data is worth it. Start with content that’s high-volume and high-impact. Focus on: - Post-training feedback - Coaching and 1:1 notes - Workshop or debrief transcripts - Policy docs in unreadable formats This is where insights are hiding. Step 3: Structure the unstructured Use lightweight AI tools to make it usable. Try: - ChatGPT Enterprise to tag and summarize - Otter.ai / TLDV to transcribe and recap - Guidde to turn steps into searchable guides And tag docs with topic, team, and timestamp. Step 4: Train AI on what matters Once structured, your data becomes leverage. Use it to power SOPs, checklists, or internal bots. Let AI write based on your real examples. It will save time and multiply your reach. Good AI starts with good prep. Don’t feed it chaos. Feed it clarity. P.S. Want my free L&D strategy guide? 1. Scroll to the top 2. Click “Visit my website” 3. Download your free guide.

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗳𝗼𝗿 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Unstructured data is one of the biggest hurdles in scaling intelligent systems—be it customer support content, product manuals, or internal documentation. The sheer volume and inconsistency make it hard for AI to extract real value. Having worked extensively in the fintech and payments space, I’ve seen how this challenge plays out across merchant onboarding, compliance, and transaction monitoring. RAG pipelines offer a practical path to bridge that gap—by converting scattered knowledge into structured, retrievable insights. This visual breaks down a typical RAG pipeline that transforms unstructured sources into structured, queryable knowledge. 1. Data Sources: Start by pulling in content from community support forums, product docs, and internal knowledge bases the goldmine of domain-specific knowledge. 2. Metadata & Content Extraction: Documents are processed to extract both metadata (title, author, timestamps) and content, feeding into different parts of the pipeline. 3. Chunking Strategies: Raw text is split using smart strategies like semantic, paragraph-based, or recursive chunking each with its pros and cons depending on your use case. 4. Text Embeddings: These chunks are converted into embeddings using powerful language models. Metadata is also encoded for enhanced context. 5. Storage in Vector DBs: Finally, both embeddings and metadata are stored in a vector database for efficient retrieval  forming the foundation for powerful RAG-based applications. This structured approach ensures your LLM retrieves the most relevant chunks, leading to accurate and context-aware responses. A well-designed RAG pipeline = better answers, faster insights, and smarter AI. Follow Nikhil Kassetty for more updates ! #RAG #LLM #AIpipeline #UnstructuredData #VectorDB #KnowledgeEngineering

Converting unstructured text into usable data structures remains one of the most frustrating challenges for ML/AI engineers working with real-world data. Over the weekend, I put together a short blog post on LLM-powered data extraction - a challenge I face regularly as an ML/AI engineer working with messy, unstructured text. In the article, I cover: - The common frustrations of dealing with inconsistent formats, ambiguity, and noise in unstructured text - How Pydantic provides a foundation for defining clear data schemas with validation - Using Instructor to seamlessly integrate #LLMs with #python for structured extraction - Boundary (YC W23) (BAML) as a more robust approach for complex, production-grade extraction pipelines - A practical workflow that combines these tools for reliable data extraction without regex nightmares If you've struggled with extracting structured data from text, I'd love to hear your thoughts and experiences. https://lnkd.in/ejmft3Vf

How are you using agents & AI? Here's my first experiment (little weekend fun) At my recent hack-a-thon with the team at allGood I tackled this little question... We thought we had a decent handle on the answer to the question "How did you hear about us?" until we put an AI Agent to the test. Using the allGood - Meet Mary agent, we processed and categorized the open-text responses to the classic “How did you hear about us?” question and I saw a night and day difference compared to our original data built on traditional workflows with strict rules. Here are just a few % change highlights that show the agent’s value: 📈 Word of Mouth: +72% more attribution than originally recorded 🚀 Organic Search: +50% more accurate identifications 🔻 Community: -72% (a huge overestimation in original tagging) 🔍 Social Media: corrected down -6%, showing clearer signal These shifts didn’t just tweak the data—they fundamentally changed how we think about channel performance, resource allocation, and attribution accuracy. 👎 Traditional workflows? They're built on rigid branching logic and brittle keyword rules that fall apart when humans do what they do best: write freely. 🤖 AI Agents? They understand language. They handle ambiguity. They scale with nuance. This project is proof: → When you use AI Agents on unstructured data, you don’t just get cleaner data—you get better decisions. Huge thanks to the allGood team for helping us unlock a new level of insight. Can't wait to keep pushing the boundaries with agents like this!! How are you using AI to improve your insights? #marketingops

Ever wonder what role Salesforce Data Cloud can play in data prep and transformation? Let's dig in to some of the key capabilities. 🔧 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽 𝗥𝗲𝗰𝗶𝗽𝗲𝘀 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝘀: Transform raw data into the shape you need it in. With Data Cloud, you can aggregate, normalize, and enrich data to ensure it's ready for any use case. 📦 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴, 𝗧𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗯𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴: Have unstructured data? Data Cloud has you covered; chunk, transcribe, and embed data for more granular processing, making it easier to work with big data and turn unstructured data into structured insights. This is a key feature that powers 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚). ↕️ 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Data cloud handles structured, semi-structured, and unstructured data. Ideal for use cases that rely on personalized recommendations and other AI + ML-driven insights. 🌐 𝗗𝗮𝘁𝗮 𝗦𝗽𝗮𝗰𝗲𝘀: Enable secure, partitioned data sharing within a single Data Cloud environment. With Data Spaces, different teams or departments can work within a centralized unified data platform while maintaining data integrity and security. #salesforce #datacloud #howdatacloudworks