Doug Rose

If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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💥 Data Blizzard Alert Moving data from one tech stack to another isn’t a strategy—it’s just shoveling snow during a storm. ❄️ Data management today is still a greenfield project—messy, manual, and missing vision and governance. 🚨 DAI Maturity Check: What Are Organizations Getting Right—and What’s Missing? I analyzed results from a recent Data & AI Maturity self-assessment across Strategy, Culture, Governance, and Tech. 🧩 Findings: ✅ Tech & Ops are strong ⚠️ Strategy & Governance are falling behind 📉 Execution is outpacing alignment 🔥 Without strategic clarity, you’re just moving piles of digital snow—fast, but nowhere. Where does your organization stand in the maturity journey? Douglas Laney #DAIMaturity #DataGovernance #DigitalTransformation #DataLeadership #CMU #TechWithImpact #ForteConsulting Photo Source: Feb 2021 Blizzard.

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No A/B test? No problem. Learn how Regression Discontinuity Design (RDD) can uncover causal effects in observational data by exploiting sharp cutoffs. Dive into the theory and a real-world e-commerce example in Alejandro Alvarez Pérez's newest article.

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Much has been written about the MIT report findings that 95% of AI pilot projects are failing on ROI. Informatica's Siddharth Rajagopal tells Verdict he know why... #AI #datamanagement #LLMs https://lnkd.in/eQGq5igt

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96% of Applied Data Science Lab alumni call it a worthwhile investment. The difference? Learners don't just dive into theory—they build end-to-end projects that mirror real work. They wrangle messy data, create predictive models for housing prices and customer retention, develop interactive dashboards, and build APIs that deploy their insights into live applications! Curious? Learn more at https://hubs.ly/Q03Bq-G_0 #GrowWithWQU #AppliedLearning #LifelongLearning #GlobalEducation #DataScience

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AI and the Structural Logic of Book Indexing The discussion around AI in publishing often centers on speed and automation. In the context of book indexing, it is helpful to look at the practical pros and cons of these tools compared to manual professional indexing. Pros of AI in indexing: AI excels at high-speed data processing. It can instantly extract keywords, identify every instance of a specific term, and generate a basic concordance. For simple texts where a literal word-search is sufficient, automated tools offer a significant advantage in turnaround time and initial cost. The cons and the human advantage: the primary limitation of AI remains its struggle with "aboutness." While AI can find every mention of a word, it cannot always distinguish between a passing mention and a significant discussion. For complex works—such as clinical medical texts or technical manuals—the index must map out an intellectual hierarchy. A human indexer brings three critical elements that AI currently lacks: -contextual sifting: determining which mentions are actually useful to a reader. -complex cross-referencing: building a web of "See" and "See also" references that reflect the author’s intent rather than just linguistic patterns. -structural consistency: ensuring that multi-volume or 1,000+ page manuscripts maintain a logical, navigable flow. While AI is a useful tool for preliminary data sorting, the final organization of a professional index requires an understanding of how a reader searches for information. For high-stakes publications, the analytical oversight of an experienced indexer remains the standard for accuracy and usability. Further reading on AI and indexing: The Society of Indexers: Current Perspectives on AI in the Industry American Society for Indexing: Resources on Automated Indexing Technology NISO (National Information Standards Organization): Standards for Automated and Manual Indexing

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Unlock powerful insights with DSS Professional's Intelligent Analysis. Metadata tools for demographic and directional data, including gender and age. 'In Area Number' analysis with custom duration settings for strategic planning. Download & learn more: http://tiny.cc/dahuadss

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