This week on Catalog & Cocktails: The Honest No-BS Data Podcast, we chatted with Winfried Adalbert Etzel, Data Governance Enthusiast, on rethinking and automating data governance. 👉 Listen to the full episode here: Spotify: https://hubs.li/Q036Mq060 Apple Podcasts: https://hubs.li/Q036Mpxg0
关于我们
data.world is the data catalog platform built for your AI future. Its cloud-native SaaS platform combines a consumer-grade user experience with a powerful knowledge graph to deliver enhanced data discovery, agile data governance, and actionable insights. Get fast, trusted access to your company’s knowledge when you create AI-powered data experiences with the AI Context Engine™. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work eight years in a row.
- 网站
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https://data.world/home
data.world的外部链接
- 所属行业
- 软件开发
- 规模
- 51-200 人
- 总部
- Austin,Texas
- 类型
- 私人持股
- 创立
- 2016
- 领域
- data collaboration、data science、analytics、enterprise、teamwork、open data、collaboration、data catalog、data governance、data ops、data stewards、ai、saas和data cloud
地点
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主要
7000 N Mopac Expy
US,Texas,Austin
data.world员工
动态
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Data governance doesn't have to be complicated. Our latest guide breaks down 10 essential best practices that will transform how your organization manages data. 🔑 Did you know global data generation is expected to surpass 394 zettabytes in the next five years? Without proper governance, all that data can quickly go from being an asset to a liability. Discover how to: 🔹 Build a flexible governance framework that adapts to change 🔹 Foster a data-driven culture across your organization 🔹 Leverage automation and AI to streamline governance 🔹 Measure the success of your governance initiatives Ready to make data governance less of a chore and more of a competitive advantage? Check out our comprehensive guide by Liz Elfman here: https://hubs.li/Q036BGG70
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Rethinking Data Governance for a Distributed World with Winfried Adalbert Etzel, this week on Catalog & Cocktails: The Honest No-BS Data Podcast! What if data governance could be as natural as safety culture at an energy company? Data Governance Enthusiast Winfried Etzel challenges traditional notions of governance for the AI age, proposing a fresh framework through three crucial lenses: Data Negotiator, Director, and Accountant. Join us for a thought-provoking discussion about moving beyond compliance to build a culture where every data professional becomes a governance champion.
Rethinking Data Governance for a Distributed World with Winfried Etzel
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Calling all financial services leaders! Join data.world and Hakkōda for an exclusive summit in Miami this March 🌴 Network with industry pioneers, engage in thought-provoking panels, and yes - test drive luxury cars while you're here. When: March 5 & 6 📆 Where: Miami 🏖️ Learn more here: https://lnkd.in/gA5bAKs4
We are excited to announce that we are partnering with Hakkōda for AI in Financial Services Leadership Summit! 🥳 On March 5th & 6th at the beautiful Mandarin Oriental in Miami, this intimate event will host the top leaders in the financial services landscape for two days of impactful discussions and networking. We'd love for you to join us for this exclusive invitation-only experience. If you are interested in attending, please send us a DM. 🥂 Learn more here: https://lnkd.in/gA5bAKs4 Amee Ricketts Mungo Juan Sequeda Tim Gasper
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Confused about data catalogs vs. metadata management? 🤔 Think of it this way: A data catalog is like a GPS app guiding you straight to your data, while metadata management builds the detailed map that makes navigation possible. 🗺️📍 Both are seeing massive growth (data catalogs at 22.7% CAGR!), but which one does your organization need? Or do you need both? Read our recent blog by Liz Elfman to discover how these tools work together to make your data more discoverable, manageable, and valuable: https://hubs.li/Q0369xfb0
Data Catalogs vs. Metadata Management: What’s the Difference?
data.world
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This week on Catalog & Cocktails: The Honest No-BS Data Podcast, we talked with Alexander Torrenegra, CEO at Torre.ai, on setting real expectations, pilots in AI, and governance as differentiation and innovation. 👉 Listen to the full episode here: Spotify: https://hubs.li/Q035MZ1G0 Apple Podcasts: https://hubs.li/Q035MVRC0
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We are excited to announce that we are partnering with Hakkōda for AI in Financial Services Leadership Summit! 🥳 On March 5th & 6th at the beautiful Mandarin Oriental in Miami, this intimate event will host the top leaders in the financial services landscape for two days of impactful discussions and networking. We'd love for you to join us for this exclusive invitation-only experience. If you are interested in attending, please send us a DM. 🥂 Learn more here: https://lnkd.in/gA5bAKs4 Amee Ricketts Mungo Juan Sequeda Tim Gasper
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How Data Governance Becomes Your Secret Weapon with Alexander Torrenegra, this week on Catalog & Cocktails: The Honest No-BS Data Podcast! Alexander Torrenegra, CEO of Torre.ai, reveals how data governance can be transformed from a constraint into competitive advantage. From his early days revolutionizing voice actor casting to now building AI recruitment solutions, Torrenegra shares candid insights on why true AI value lies not in copilots but in full pilots, and why transparency might surpass privacy. Pour yourself a drink and join Juan and Tim for an honest, no-BS conversation about the intersection of AI, data governance, and the future of recruitment.
How Data Governance Becomes Your Secret Weapon with Alexander Torrenegra
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Data governance principles are foundational rules that help organizations manage and protect their data assets effectively. 📝 Explore the 7 essential data governance principles with us, showcasing how companies like P&G and GE Aviation transform data management. Read more in this blog post by Liz Elfman: https://hubs.li/Q035n_tT0
7 Essential Data Governance Principles
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In the era of generative AI, understanding data context is crucial. In a recent article by Tony Baer, Juan Sequeda, our Principal Scientist & Head of AI Lab, emphasizes how critical it is for organizations to be able to effectively reuse their data. 🧠 Check out the rest of his insights here 🔗 : https://lnkd.in/gmcW-HWZ
As promised, we've put together our deep dive takeaways from Data Day Texas 2025 #ddtx25. If we reduced this to a single word, it would be context. In the age of AI, traditional data quality rules are no longer adequate. You might have data that is valid, correct, but not quite right from answering the question. The stakes grow with the need to apply value engineering to #genAI models. As the headlines over DeepSeek last week revealed, there is a need to shrink models and data sets down to size. this is no longer the heyday of Big Data where you could overwhelm bad data with good data through sheer volume. When you have a smaller wall, throwing enough mud at it is no longer a viable strategy. So how do you ensure that you have the right data for the task? That's where the context comes in. But how do you get that context? Part of it can come from automated harvesting and some use of AI. But ultimately it requires human in the loop because of the need for a quality that AI does not provide: Judgment. And that's where the who comes in. There's a crying need for someone who can judge what is the appropriate information. Just like that professional we relied on in the old days when we visited libraries, scanned through card catalogs, microfiche and the like. You might have found the titles, but the reference librarian was the person you entrusted to tell you whether you were on the right path. It's very much the same qualities that today we look for in knowledge engineers. Thanks to colleagues Joe Reis Matthew Housley Juan Sequeda and conference speakers like Ole Olesen-Bagneux Andrew Nguyen Lisa N. Cao Patrick McFadin Susan Chang Su Vin Vashishta and many others for the stimulating discussions.
Data in the generative AI era: We need more knowledge engineers - SiliconANGLE
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