George Earl

The ol' adage "a picture is worth a thousand words" is very true. Take this picture ... this is explaining CLIP - CoreLogic's Property identifier, what a SSN is to you and I. CLIP is the glue between 210M+ addresses, across 176M+ structures sitting on 157M+. Indeed we track 250,000 to 500,000 changes per quarter ... new addresses, parcels split, new development. Why is it useful? Allows our clients to focus on the insight ... traversing market position, new leads, operational efficiency all the way through to risk mitigation use cases on their book of business. So easy to CLIP your book of business. Whether handling lat/long, shape files, APNs, addresses, CoreLogic takes the pain away of mastering property & location geospatially - to allow you to focus on the insight and the business decision. Took us over 2.5 years to build and has been in the market getting used across the housing industry for over 4 years. To the product, geospatial scientists and technology geniuses who created it Anand Singh Shaunak G., Matt Kjernes Frankie Famighetti Hari Krishna Kandula Chris Tackett Manny Cuevas Patrick Dodd Devi Mateti Jay Kingsley Garret Gray Lance Lambert Kristie Vainikos Stegen Robin Wachner Amy Polvado Robin Sun Massimo Mascaro Pete Lumbert Mark Weaver Bill McGinn Heidi Russell Bill Bernabei John Sikaitis This is CoreLogic 2.0

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Prosper Insights & Analytics Uncovers a Shift in National Sentiment on the Vitality of the American Dream Is the American Dream Fading? In a groundbreaking survey conducted in May 2024, Prosper Insights & Analytics has tapped into the pulse of 8,580 U.S. consumers, unveiling a significant shift in attitudes towards the American Dream. The results paint a picture of a nation grappling with doubt and uncertainty, as less than half of both younger and older Americans believe that hard work and sacrifice can still yield the rewards traditionally associated with the American Dream. https://lnkd.in/gb3c-43d #AmericanDream #sentiment #consumers #Attitudes #uncertainty

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Myth: AI systems are self-explanatory and don't require further clarification once they produce results. Reality: Many AI systems, especially those involving complex algorithms like deep learning, can be "black boxes." Some vendors may claim they can't disclose details because they're proprietary, which means we cannot know how these systems make decisions. This lack of transparency in AI models can lead to distrust. For example, if the AI system creates scores on something critical, it should be transparent about its parameters and conditions. Otherwise, it will feel like guesswork that cannot be verified. Hence, the development of explainable AI (XAI) is not just important but paramount. AI decisions should be made more understandable before they can see more widespread applications.

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A good article in Data Science Central from Bill Schmarzo "Data-driven economics focuses on understanding and maximizing the economic value of data. Unlike traditional assets that depreciate, data appreciates as it is used across different scenarios, making it a powerful driver for business transformation. This pillar emphasizes the importance of monetization, sustainable ROI, and strategic use of data to create value. By treating data as a reusable asset, organizations can prioritize initiatives that offer the most significant economic impact, ensuring that innovation efforts are financially viable and scalable." #ai #dataanalytics #dataanalysis #datamanagement https://lnkd.in/giaCpxxR

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Prosper Insights & Analytics Uncovers a Shift in National Sentiment on the Vitality of the American Dream The survey reveals a lukewarm belief in the American Dream across all age groups that hard work and sacrifice can lead to home ownership and upward mobility. https://lnkd.in/gb3c-43d #Americandream #economicexpectations #upwardmobility #Prosperinsights&analytics #nationalsentiment #homeownership

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Here's a sneak peek at our October community calls -- Enterprise Data Strategy Board Members are discussing everything from semantic layer tools to data management frameworks and maturity models to data catalog strategies. Plus, we'll get a behind-the-scenes look at Fifth Third Bank's data mesh journey. To learn about about joining these leaders-only conversations, visit https://board.org/data/. #datamesh #datamanagement #datacatalogs #semanticlayers

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Give infoFluency's new data strategy scorecard a try! It's designed to highlight areas of business where analytics could most move the needle for your organization.

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Automatic Data Quality is the competitive advantage for success in the new IA era.

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Another great post from Melinda Roy on discovering that data governance is already happening at your institution, just in less formalized ways. This article describes how you can match data governance roles to existing higher ed roles. #highereducation #datagovernance #irwatercooler educause

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My holiday read. A fascinating perspective from data leaders as to why they left their previous environments. It seems like the challenges are similar across various industries.

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Simplifying a model doesn't necessarily trivialize it. A former Capital One executive once challenged my data & analytics team to make predictive models relevant by rendering statistical models relatable. The concept was to reduce the overall quantity of independent variables and describe in plain-English language (and with simplistic charts) how each impacted the dependent variable. His thesis was that models are abstractions of reality and unless explainable managers would be reluctant to use them to make business decisions (and furthermore: the smaller quantity of variables reflected the fact managers control a limited extent of factors). Statisticians rightly complained that limiting the quantity of dependent variables impaired accuracy and sensitivity. But when carefully parsed we learned the models remained directionally correct (very strongly so) with tolerable loss of fidelity. The overall result was managers confidently understood the relationships and trade-offs between models’ “levers” and the effect on forecasted performance.

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Sharing ZDNET article by Jason Perlow "Make no mistake, none of these models -- ChatGPT, Apple Intelligence, Google's Gemini, Meta.ai, or Perplexity -- are anywhere close to the holy grail of General AI. They remain fundamentally task-specific information retrieval tools, and their incremental memory or summarization improvements are far from game-changers. Many of the intuitive, empathetic capabilities we yearn for remain out of reach." https://lnkd.in/guUZ-fUt #ai #aiethics #automation #tech4good #teckedin #chatgpt #chatbot

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🎃 New podcast alert: Data Science Rabbit goes full moon. (Warning: May cause uncontrollable urges to howl at datasets.) Brace yourselves, data nerds. The Data Science Rabbit Podcast just dropped a Halloween special that's more terrifying than your last failed model deployment. 🐺 Episode 1: "When AI Met Werewolf" https://lnkd.in/ex4Hbq4p More scary episodes to come in the next few weeks. Don't say I didn't warn you. Available now on Spotify and all major podcast platforms. Listen at your own risk – side effects may include increased hair growth and an inexplicable desire to chase squirrels while coding. PS: If you turn into a werewolf after listening, please rate us 5 stars. It's the least you could do before devouring the interns. #DataScience #AIWerewolves #PodcastsFromHell

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🔍 Just read a fascinating paper on the future of data ecosystem business models in the emergent "Artificial Intelligence of Thinks" (AIoT) space! Very useful systematic research that yields some key insights on how companies are transforming data into value in this developing ecosystem. https://lnkd.in/ert4nU7X 🔄 FOUR DISTINCT BUSINESS MODEL ARCHETYPES IN THE AGE OF AI ARISING 🤝 Adaptive Partnership - High control + High customization - Focuses on developing close, collaborative relationships with customers - Offers deep insights and personalized solutions - Best suited for strategic, long-term business relationships 👥 User-Centric - Low control + High customization - Prioritizes customer experience and flexibility - Enables broad market reach and scalability - Emphasizes user autonomy in configuring solutions ⚡ Standardized Efficiency - High control + Low customization - Focus on operational excellence and cost reduction - Streamlined processes and standardized offerings - Ideal for high-volume, routine operations [3] 🔄 Flexible Transaction - Low control + Low customization - Transaction-focused with minimal ongoing engagement - Emphasizes automation and self-service - Suitable for commoditized offerings 💎 AIOT BUSINESS MODEL VALUE CAPTURE STRATEGIES: 📊 Responsive Monitoring: Goes beyond traditional monitoring by enabling instant actions based on real-time data, including automatic corrective functions and predictive maintenance to prevent failures 🤖 Object Self-Service: Drives objects toward independent functionality with less human intervention, incorporating self-adjustment, replenishment, and self-operation capabilities 💡 Insights as a Service: A data-driven value proposition that leverages AIoT to collect, analyze and deliver real-time and collective insights that give meaning to massive amounts of data, enabling customers to make informed decisions   ✨ Enhanced User Experience: Focuses on improving customer interaction through digital add-ons and customizable interfaces and content that adapt to individual users' preferences and behaviors over time 💡💡 The research shows successful AIoT implementations balance control and customization to create sustainable competitive advantages. 💡💡 🤔 Where do you see your organization fitting in this framework? #AIoT #DigitalTransformation #Innovation #BusinessStrategy

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After 20 years of data work, I have seen MANY bad decisions... There are three big things that impact all of them. Read all about them in my latest substack post, free to all subscribers. Data leaders, what lessons have you learned over your years of analyzing things, making changes, and then seeing what happens? https://lnkd.in/gqFWdJFX

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AI and analytics systems need data – lots of it. CRN’s annual Big Data 100 takes a look at the developers of data analysis tools, database systems, data management software and other technologies in the big data arena that solution providers should be aware of. #ai #analytics #bigdata #dashboards #data #datacollection #datalake #datawarehouse #digital #digitaltransformation #generativeai #integration #ml #technology

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🚀 While diving into Harvey Mackay's "Swimming with the Sharks," I found a golden nugget that nails the importance of measuring the right metrics! 🦈 Reasonably similiar copy of the story. https://lnkd.in/gvNBK5G4 This humorous take on an evaluation of Schubert and his unfinished symphony. If we evaluate an orchestra’s efficiency we are reminded: - Productivity isn't just about being busy. (Silly oboists sitting idle)🚀 - Coordination isn’t necessarily wasteful. (So many instruments to repeat the same notes at the same time)🎻 - Impact isn’t always from rare, monumental events. (String echoing the horns who echo the winds)💥 - Efficiency doesn’t define meaningful change. (Seems the complex rhythms might be redundant and ovelrly difficult. Simplifying could save costs and time overall, no?) 🔄 In busienss, replicating the measures others use without evaluating appropriateness can create unintended consequences. Let’s focus on what truly matters. What matters most on your team or for your project? #BusinessInsights #Efficiency #Productivity #Impact #Leadership be

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Why Analytics Feels Like a Pain (and How to Fix It) I was working through Medium and found this article by Tim Wilson of Facts and Feelings. It was a breakdown of analytics pains as identified by survey. Here are some high level takeaways. Let’s be honest—analytics can be a real grind. Whether you’re a data analyst or a business user, the challenges are real, recurring, and frustrating. But here’s the good news: the pain points are solvable. 🎯 Here’s what analytics teams are struggling with: 1️⃣ Unclear Goals, Undefined KPIs 69% of analytics teams feel the pain of being asked to deliver results without clear targets. Solution: Start with clear, measurable business objectives (not just data dumps). 2️⃣ Messy Data 68% struggle with data quality issues, and 56% find themselves constantly explaining inconsistencies. Solution: Streamline your data collection. More isn’t better—it’s often just more mess. 3️⃣ The Myth of Self-Service 72% of analysts say self-service tools go unused, while 91% of business users complain about data access. Solution: Self-service isn’t just about tech—it’s about training and alignment. 4️⃣ Lack of Actionable Insights 73% of business users say they don’t get actionable insights, while analysts feel stuck in reactive mode. Solution: Analysts need to shift from “answering questions” to driving business outcomes. 💡 The bigger picture: Analytics isn’t just about collecting and visualizing data—it’s about reducing friction between teams, clarifying goals, and focusing on actionable insights. Stop being “data-driven” and start being decision-driven. Build workflows, not just dashboards. Fix the systems, not just the symptoms. Check out the full article here: https://lnkd.in/gps8RNWW #Analytics #DataDriven #BusinessStrategy #ActionableInsights #DataQuality

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