The #InferencingCost Problem No One Is Talking About: #UnstructuredData Quality | TDWI tdwi.org/articles/2026/06/11…

The Inferencing Cost Problem No One Is Talking About: Unstructured Data Quality tdwi.org/articles/2026/06/11… #datamanagement #AI #unstructureddata @cloudKrishna @TDWI

Komprise | Storage Metrics to Optimize Capacity youtu.be/L0WnhXWiBYg?si=s9b0… via @YouTube #datamanagement #unstructureddata @Randy2112

Your data is already telling you the answer—you just can’t access it yet. Most enterprises don’t have a data problem. They have an unstructured data problem—where critical information is buried inside documents, PDFs, emails, and reports that systems can’t easily read or use. That’s where unstructured data extraction changes the game: Turns scattered documents into structured, usable data Eliminates manual data processing bottlenecks Improves accuracy across enterprise workflows Enables faster, smarter decision-making at scale See how enterprises are unlocking hidden intelligence from their data: zurl.co/TncET Stop storing data you can’t use. Start extracting value from it. #UnstructuredData #DataAutomation #IntelligentAutomation #IDP #EnterpriseAI #DataEngineering #DigitalTransformation #AIforBusiness #BusinessIntelligence #Automation

Here is a visual analogy of what is happening #Gemini #AI #LLM #PromptEngineering #MachineLearning #GoogleAI #AIReasoning #UnstructuredData #TechTwitter #AITools #NeuralNetworks #Transformers #UnstructuredData #JSON #DataParsing

🌏What Is Data? 🌟Data refers to any information, facts or figures that can be recorded, stored and analysed. In marketing analytics, data acts as the raw material for generating insights and supporting informed decision-making. ☑️Data can be classified by structure into structured, unstructured and semi-structured data, and by origin into declared, observed and inferred data. 🔥Machine learning is increasingly important because it can identify patterns in large datasets and infer insights, predictions and recommendations at scale. #Data #marketingdigital #structureddata #unstructureddata #marketing

90% of corporate data is unstructured, trapped in PDFs, emails, and audio logs. 🚨📁 If your data strategy only queries relational SQL tables, you are blind to 90% of your business reality. Real AI engineering means building deterministic pipelines to unlock the chaos. Stop ignoring your biggest data asset: mcal.in #DataEngineering #UnstructuredData #AIArchitecture

Are we heading towards a future where traditional PIM and Enterprise Search technologies are obsoleted by AI? Sam Bobb thinks we might be! Check it out. #UnstructuredData #BusinessIntelligence #KAAVIO #SAM_BOBB #THEECOMMERCEEDGEPodcast #E652 THE ECOMMERCE EDGE Podcast

80-90% of all new enterprise data is unstructured. And most of it is going to waste. Customer reviews. Social media posts. Support tickets. Meeting notes. This is your unstructured data and it contains the 'why' behind every trend your dashboards are showing you. The thing is, most enterprises have no way to connect it to their structured data. Their BI teams and customer success teams operate in silos, and the rarely come together to form a coherent picture. That's the problem ThinkingAI's Agentic Engine is built to solve. Our AI agents can simultaneously analyze both structured and unstructured data, and combine behavioral data, user feedback, community reviews, and internal knowledge bases into a complete 360-degree view of your business. Agentic Engine can reveal 'why' something happened, not just 'what.' Read our full breakdown of structured vs. unstructured data and how Agentic Engine bridges the gap here 👇 thinkingai.io/blog/a1b2en01 #DataAnalytics #UnstructuredData #AgenticAI #EnterpriseAI #ThinkingAI

The Unstructured #Data Crisis >> Why Organizations Can't See Their Biggest #Security Risk buff.ly/B4RwAwO @SecurityBlvd @AShimmy #tech #digital #innovation #datamanagement #business #leadership #management #governance #datasecurity #cybersecurity #CISO #CIO #CTO #CDO #CEO #unstructureddata #genAI #AI #LLM

Parsing moves fast. New models, new techniques, something worth paying attention to seems to drop every few weeks. But Chunking is different. The questions you're working through are always the same: how big should a piece of context be before the embedding gets too coarse? Where should boundaries fall so you're not splitting ideas in half? How do you make sure what gets retrieved is actually what matters? Those questions don't change, no matter what's happening on the parsing side. And getting them right has a huge impact on how your downstream systems perform. We wrote a blog going back to basics on all of this. Check it out 👉 unstructured.io/blog/chunkin… #RAG #AI #GenAI #DataEngineering #UnstructuredData #Unstructured #LLMs #AgenticAI #VectorDB

How rising storage costs are forcing a re-think of enterprise AI data strategies diginomica.com/how-rising-st… @diginomica #datamanagement #unstructureddata #AI

Legacy data reduction was built for a different era. ⏳ At multi-petabyte scale, unstructured data is pushing those approaches to their limits. In part two of our engineering series, we explore what a modern approach unlocks—greater efficiency, lower storage costs, and faster access to the data that fuels AI. ⚙️ See how Purity DeepReduce makes it possible: purefla.sh/4u0UEpA #Everpure #AI #UnstructuredData #DataReduction #PurityDeepReduce #EnterpriseDataCloud #StoragePlatform #StorageAndDataManagement

Most teams call themselves data-driven. In reality, they’re working off the 10-20% that fits into tables. The rest sits in emails, PDFs, logs, chats. Our new guide by Vladyslav Savchenko for #StarWind explains structured vs unstructured vs semi-structured data and where each matters. Read more here: starwind.com/s/18x #StarWind_handy #DataEngineering #Analytics #StructuredData #DataStrategy #UnstructuredData

Most RAG pipelines don't fail because of the model. They fail because the data isn't usable. When your pipeline hits messy PDFs, complex tables, and documents that standard parsers can't handle, retrieval breaks. And once retrieval breaks, everything downstream does too. We're teaming up with @IBM to show what it actually takes to fix this. 📆 Wednesday, April 1 @ 11AM PT 🎙️ Austin Eovito, Senior AI Engineer Daniel Schofield, Principal Solutions Architect In this session, we'll walk through: * How to extract and structure data from complex documents * How to land that data into @IBMwatsonx for governance and scale * How to power reliable RAG and agentic workflows on top This is about getting from raw documents to production-ready AI. If you're trying to move beyond demos, this one's worth your time. 🔗 Register here: ibm.webcasts.com/starthere.j… #AI #GenAI #RAG #UnstructuredData #IBM #watsonx #ETL #EnterpriseAI

Your agents are only as smart as the data they can *actually* reach. Most enterprise data is "dark" — locked inside scanned PDFs, complex tables, messy documents that standard parsers fumble. If your retrieval pipeline doesn't see it, your agents can't use it. So while everyone's upgrading models and tuning prompts, the real bottleneck is sitting untouched in your data layer. That's where production AI breaks down. And that's exactly what we're fixing! Join us for a hands-on technical session with @IBMwatsonx and @UnstructuredIO, where we'll build a working enterprise-grade RAG pipeline from scratch. 📅 When: Wed, Apr 1, 2026 @ 11AM PDT 🎙️ Speakers: - Austin Eovito, Senior AI Engineer, IBM Client Engineering - Daniel Schofield, Principal Solutions Architect, Unstructured We'll walk through: * Using Unstructured's advanced partitioning to handle the complex layouts — tables, headers, footers — that break standard parsers * Landing ingested data directly into @IBMwatsonx data for governance and scale * Integrating with IBM's agentic frameworks to build powerful, responsive AI agents Plus, every attendee gets access to a GitHub repo and Google Colab notebook with a pre-configured workflow you can take home and build on. 😎 If you're serious about moving from demo-ready to production-grade AI, this one's for you. Register here: ibm.webcasts.com/starthere.j… #AI #GenAI #RAG #AgenticAI #EnterpriseAI #UnstructuredData #IBMwatsonx #DataPipeline #LLM #RAG #DarkData #Unstructured #IBM

Drowning in unstructured data? Komprise and IDS cut storage costs, tier data for AI, and break silos across ANZ. Learn more: ids-g.com/products-services/… #Komprise #DataManagement #AI #UnstructuredData #DataGovernance #IDS #ANZ #ANZ

The Universe of Data Types & Structures | Quantitative, Qualitative & Data Organization Explained Understanding data starts with knowing how it is measured and how it is stored. In this video, we explore The Universe of Data Types and Structures, breaking data into clear, practical categories that every data analyst, engineer, and scientist should know. You’ll learn: ✅ The difference between Quantitative and Qualitative data ✅ Discrete vs Continuous numerical data ✅ Nominal vs Ordinal categorical data ✅ How data is stored as Structured, Semi‑Structured, and Unstructured ✅ Real‑world examples using spreadsheets, JSON, logs, and social media data This visual‑first explanation helps you quickly identify how different variables behave inside analytics systems, databases, and modern data platforms. Whether you're: Learning data analytics Working in data engineering Preparing for interviews Designing data models 👉 This video gives you a solid foundation. #DataTypes #DataStructures #DataAnalytics #DataEngineering #BigData #StructuredData #UnstructuredData #SemiStructuredData #AnalyticsBasics #DataScience youtu.be/fX7H7tTL1mg?si=juEI… via @YouTube

🎙️Don't miss my latest audio podcast: From AI Momentum To AI Maturity: The AI Trust Paradox And What Must Change In 2026 🚀 AI adoption is surging in 2026, but are organizations actually ready to scale it safely and responsibly? In this interview, I sit down with Nathan Turajski, Senior Director for Product Marketing at Informatica, to explore the key findings from Informatica’s CDO Insights 2026 report and what they reveal about the next phase of enterprise AI. We discuss why trust in AI is rising faster than readiness, what the AI trust paradox means for leaders, and why data foundations, governance, and literacy will decide who wins with GenAI and agentic AI. 🧠📊🔐 You will learn how leading data teams are thinking about: ✅ The AI trust paradox, confidence vs readiness ✅ Scaling GenAI from pilots to production, and why data reliability still blocks progress ✅ Agentic AI adoption, new risks when systems can act, not just advise ✅ Unstructured data governance, emails, PDFs, transcripts, and the new frontier of trusted context ✅ AI literacy and data literacy: why people are becoming the real scaling constraint ✅ Vendor sprawl and complexity: how tool fragmentation can slow ROI and increase risk 📄 Download Informatica’s CDO Insights 2026 report: youtube.com/redirect?event=v… podcasts.bernardmarr.com/104… #Sponsored #InformaticaPartner #Informatica #CDO #CDOInsights #AI #GenerativeAI #AgenticAI #Data #DataManagement #DataGovernance #AIGovernance #DataQuality #UnstructuredData #DataLiteracy #AILiteracy #Analytics #DigitalTransformation #FutureOfWork