Ben Zweig

A quick intro on three popular data mgmt tools

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Our pre-publication paper is out! The Next Phase of the Data Economy: Economic & Technological Perspectives co-authored with some amazing people: Scott Kominers, Leora Kornfeld, John Deighton, Apurva Chitnis, Kinjal Shah, Reid Williams, Doc Searls, Scott Belsky, Kasia Chmielinski, David Eliot, Carolina A. Rossini, JD, MA, LLM, MBA Full paper here: https://lnkd.in/eJcHRF8a We’re on the precipice of an internet with more personalized, user-centric applications, one that’ll stimulate new forms of economic interaction and innovation within the data ecosystem. With that, the demand for all forms of data is continuing to accelerate: from public data for training foundational models, to private data for personalization purposes and AI agents. All of this against strong tailwinds toward individual data portability and agency, enabling individuals to leverage their data across surfaces, scaling the utility and value of their data as the number of possible data destinations increases. To enable this, we will see the inversion of the internet’s personal data model – from data going to apps, toward apps, agents and services coming to us and our data. This’ll necessitate the re-aggregation of our data in personal data stores, and some system to intuitively provision access to our data to the various destinations we choose. The first challenge, however, is to incentivize users to re-aggregate as much of their data somewhere they have stewardship over -- a big part of our focus at koodos labs (the creators of Shelf). Once that’s accomplished, there’ll be opportunities to create upstream and downstream value from a more user-centered liquid data ecosystem. Personal data stores have the potential to increase our agency and independence with regard to data by allowing us to manage, share, and protect our data as we see fit. In our paper we discuss why personal data stores have faced significant challenges in gaining widespread adoption to date, and we cover some of the positive tailwinds in that direction, including new regulation, infrastructure developments, accelerating consumer adoption and fundamental market dynamics. We also cover how value will be created in a more individual-centric data economy.

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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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Open, Modular and Composable Analytics Will Scale a Decision Advantage For Less: https://lnkd.in/eBZSfjjN Organizations under increasing pressure to deliver flexible, cost-effective analytics must continually adapt to evolving stakeholder needs as Generative AI amplifies user expectations and governance remains an evergreen challenge. Decision leaders distributed across departments need to build their organization’s decision advantage without exceeding budgets, so, are adopting open, modular, and composable analytics architectures. #Analytics #BusinessIntelligence #ComposableAnalytics #DataAnalytics

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For those of you who have worked with or are interested in econometric models, what challenges have you faced when dealing with censored data? How do you approach these challenges in your analyses? I am always open to discussions about econometric methodologies and their real-world applications. If you are interested in econometric models, data analysis, or simply wish to exchange ideas about econometrics, feel free to reach out! Together, let's explore the frontiers of data analysis and contribute to a more analytical and informed future. #Econometrics #TobitModel #DataAnalysis #Statistics #Economics #DataScience #Research #Innovation

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I'm thrilled to announce that my new book, "Bridging the Gap: Empowering Small Vendors against Predatory Lending," is now available on Amazon and Eliva Books. This book represents a labor of love and dedication, drawing from my 15+ years of experience as an economist and finance professor, enriched by personal interviews and discussions with small business owners. It sheds light on the critical issues faced by small vendors and street businesses—key players in our economies who often struggle under the weight of predatory lending practices. Inside, you'll find a blend of theoretical insights and practical realities, with narratives capturing the resilience and challenges of vendors from Mumbai to Kolkata. It’s a vivid picture of their economic struggles and the systemic issues that perpetuate financial instability. A huge thank you to Eliva Press for their support and for believing in the impact this book can have. I am proud to be associated with their mission to bring valuable academic contributions to the forefront. Join me in celebrating this achievement and let's make a difference together! Your valuable feedback on this book will greatly motivate me to continue my research and leverage my experience to persuade policymakers in crafting optimal policies for nation-building. I encourage you to read the book and share your thoughts and comments. #NewBook hashtag #Economics hashtag #SmallBusiness hashtag #Entrepreneurship hashtag #PredatoryLending hashtag #FinancialStability hashtag #BookLaunch hashtag #AuthorLife hashtag #AcademicCommunity Reserve Bank of India (RBI), Ministry of Finance, World Economic Forum, The World Bank, OECD - OCDE, International Monetary Fund, Small Business Investing Scholars Program, Invest India, Ministry of Micro, Small and Medium Enterprises, Government of India, MakeinIndia.2023, ICRIER, Indian Economic Journal IEA, The Economic Times, Economic and Political Weekly, The Economist, Financial Times, Economic Research, INDIAN INSTITUTE OF ECONOMIC PLANNING AND MANAGEMENT PRIVATE LIMITED, Centre for Monitoring Indian Economy

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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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The greatest opportunity for most organizations to expand and upskill their talent pool for data science and AI is through upskilling CURRENT staff. My colleague T. Scott Clendaniel has just lead his first piece of Gartner research which helps data and analytics leaders execute UPSKILLING initiatives for Data Scientists and AI experts. 🔵 What I hear is, many Gartner clients still report difficulty finding and attracting talent. 🔵 Evidence from our Gartner’s CDAO Agenda Survey for 2024 indicating that skills and staff shortages are a top-three challenge for 42% of respondents. 🔵 Data science and AI leaders struggle to meet the needs of employees simply by hiring more data scientists. 🔵 Therefore, upskilling paths must broaden AI skills across experience levels, from experts to novices. This research provides recommendations for creating and implementing those initiatives. "How to Lead Upskilling Initiatives for Data Scientists and AI Experts" https://lnkd.in/eFXPMgm9 (Gartner client login required) [Published 19 November 2024] by Scott and Aura Popa, CStat, Peter Krensky, Afraz Jaffri, Kurt Schlegel. Let me know in the comments if you are or have led a #DataScience or #AI upskilling initiative, or if you are a: - data scientist - PhD level or equivalent experience - looking to join a great company - keen to do research in #AI - in Europe ...you can drop me a direct message for a quick chat. Gartner is hiring! https://lnkd.in/eyNmvBU5

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“As data becomes more accessible, the need for a trusted data foundation becomes more important.”  TealBook CEO and founder, Stephany Lapierre, has been tackling the #data problem in #procurement since 2018. In this CPOstrategy feature, she discusses this issue and shares how Tealbook works to address it, helping procurement teams diagnose gaps and opportunities to improve their data. #WITFund

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Today on Mic Drop: Sravan Kasarla, Ex- Chief Data Officer and Head of Data Analytics at Thrivent Join us as Sravan shares: 1. One key insight he wishes he had known earlier in his data career 2. Advice for data professionals on effective storytelling Listen in and share your thoughts in the comments below!

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Navigating Data Blind Spots: Workiva podcast interview on: 1. How our biases cause us to misinterpret data and have data blind spots 2. The importance of considering the context when using data, rather than just looking at whether a number is higher or lower than a competitor, or has gone up and down over time 3. How to communicate data fairly and accurately while remaining persuasive with a particular application to ESG metrics https://lnkd.in/e9kfDYPD

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published 5 years ago but still relevant today? 🤔 "deep learning needs to start asking why things happen" being able to answer why is essential in decision making use cases in order to transition from prediction to prescription/intervention structural causal models can answer why our scm framework, causalNet: https://lnkd.in/etcEyKj8 You can then do root cause analysis: https://lnkd.in/gPBWpEzH "Yoshua Bengio also expressed some frustration with how companies exaggerate the capabilities of AI and deep learning. “I think it would be a good thing if there's a correction in the business world, because that's where the hype is,” he says.” broadly speaking, AI does fall in the “overhyped in the short term, underhyped in the long term” theme Article: https://lnkd.in/gs2HtH9v WIRED Will Knight

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A few weeks ago, we showed evidence on the Great Stay and the factors that explain it. One thing that really stood out was how the decline in attrition coincides with rapidly declining employee sentiment in the US. I tried to understand the decrease in employee sentiment a little more. You can read the full article here: https://lnkd.in/eKcBr43y Here is a summary of what I found: 🔸Employee sentiment in the US is highly correlated with other measures of macroeconomic confidence, such as the Consumer Sentiment index and inflation expectations. When people feel negatively about the overall economic conditions, it reflects on how they perceive their work as well. 🔸A large share of the decline in consumer sentiment since 2021 is explained by changes in the labor market (most importantly unemployment rate, layoffs, and availability of jobs - measured by the number of new job postings). 🔸The decline in consumer sentiment was most pronounced in small companies (those with less than 250 employees). It is important to note that in general, the level of sentiment is higher in small companies than mid-and large-sized companies, but the decline in sentiment was much faster in small companies.

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Very nice blog by Xan H. on: Uncover hidden connections in unstructured financial data with Amazon Bedrock and Amazon Neptune. "In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events at suppliers, customers, partners, or other entities in a company’s ecosystem is challenging. For example, a supply chain disruption at a key vendor would likely negatively impact downstream manufacturers. Or the loss of a top customer for a major client poses a demand risk for the supplier. Very often, such events fail to make headlines featuring the impacted company directly, but are still important to pay attention to. In this post, we demonstrate an automated solution combining knowledge graphs and generative artificial intelligence (AI) to surface such risks by cross-referencing relationship maps with real-time news" link on the comments

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I recently spoke to Gartner about what is next in #AI. Here are my thoughts: We have seen impressive progress in #llm by scaling data and compute. Will this continue to hold? Yes, I believe so, but most of those gains will be in reasoning tasks where we have precise metrics to measure uplift, as well as the ability to have synthetic data to train further, and also the freedom to trade off computation for accuracy at test time. This is seen in the recent o1 model. For reasoning tasks, we will also be able to remove hallucination when we can construct accurate verifiers that can certify every statement that #llm makes. We have been doing this in our Leandojo project for mathematical theorem proving. However, there is one area of reasoning where #llm will never be good enough: understanding the physical world. This is because language is only high-level knowledge, and cannot simulate the complex physical phenomena needed in many applications. For instance, LLMs can talk about playing tennis or look up a weather app, but they cannot internally simulate any of these processes. While images and videos can help improve their knowledge of the physical world, models like Sora learn physics by accident, and hence, still produce physically wrong outputs. How can we overcome this? By teaching AI physics from the ground up. We are building AI models that are trained in a physics-informed manner at multiple scales. They are several orders of magnitude faster than traditional simulations, and can also generate novel designs that are physically valid. You can watch some of those examples in my recent TED talk.

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Imagine 100+ bare min. variables when scaled to 5 times features ... what they can do to asset valuation and allocation strategies? Combine it with flexibility to run on demand across time / asset grains in an auto mode? Plus an evangelist supervision to remove bias for the next optimal run ... 💥

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I always thought Snowflake was just another data warehousing solution (what could be so different?), but it surprised me with some of its unique approaches to making ETL processes easier while maintaining efficiency and scale. It comes in handy for a current analytics project I have with one of the regional companies. I highly recommend this course!

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