Joe Futty

Dutch startup orq.ai is on a mission to make AI accessible for everyone! 🛠️ Their no-code platform empowers engineers, product teams, and even non-technical folks to build and ship amazing AI-powered features. With support from Google Cloud and the Google for Startups program, Orq.ai is accelerating the adoption of generative AI, helping businesses innovate faster than ever. 📈 Why Orq.ai is making waves: 🌊 Less than an hour to start developing AI apps 🌊 Secure, reliable deployment of AI solutions 🌊 Increased collaboration between teams Learn more here ↓

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DAGHUB.... Daghub is an open-source platform for managing and visualizing directed acyclic graphs (DAGs). DAGs are used to represent complex workflows, dependencies, and relationships between tasks, assets, or data. *Key Features:* 1. DAG Visualization: Interactive graph visualization for easy understanding of complex workflows. 2. DAG Management: Create, edit, and manage DAGs using a user-friendly interface. 3. Collaboration: Multi-user support for collaborative workflow management. 4. Scalability: Designed to handle large-scale DAGs with thousands of nodes. 5. Extensibility: Supports plugins and integrations with other tools. *Use Cases:* 1. Workflow Management: Manage complex workflows in data science, machine learning, or DevOps. 2. Data Pipelines: Visualize and manage data pipelines for data integration and processing. 3. Task Automation: Automate tasks and workflows using DAGs. 4. Dependency Management: Manage dependencies between tasks, assets, or data. 5. Auditing and Compliance: Track changes and visualize workflow history. *Daghub Components:* 1. *Daghub Core*: The main application for managing and visualizing DAGs. 2. *Daghub API*: RESTful API for integrating Daghub with other tools. 3. *Daghub CLI*: Command-line interface for interacting with Daghub. 4. *Daghub Plugins*: Extensions for adding custom functionality. *Benefits:* 1. Improved productivity through visualization and automation. 2. Enhanced collaboration and communication among teams. 3. Scalable and flexible architecture. 4. Easy integration with existing tools and workflows. *Alternatives:* 1. Apache Airflow 2. Argo 3. Zapier 4. Zappy 5. Graphviz *Tutorials and Resources:* 1. Daghub Documentation: https://docs.daghub.com/ 2. Daghub Tutorial: https://lnkd.in/gsbdp3xX 3. Daghub GitHub: https://github.com/daghub/ 4. Daghub Community Forum: https://lnkd.in/g2cAbuVq ......... *****.......

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'How much value are your customers getting from your products/services?' It's a great question and one which Felix Eggers, Marco Vriens, Rogier Verhulst, Jason S. Talwar, and Avinash Collis [on HBR] suggest could be answered by asking your customer: 'How much money they’d need to be given to give up your product for a period of time.' Not sure it would work with lawyers charging north of $1,000 per hour, but food for thought... https://loom.ly/LF8QqUY #psf #psfbd #NPS #CX #customerfeedback #readfirstlegal

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Any data generated, whether from user actions, services, devices, or transactions such as buying or selling, constitutes an event occurring at a specific point in time. Our goal with the Zinzu service is to aggregate this data from disparate datasets with different schemas, organizing them as events on a timeline. Users will then be able to query these datasets and navigate through events seamlessly, as if scrolling back and forth in time. DM me if you would like to know more.

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Following suits of great people in my network and seeing a trend almost before we bit goodbye to 2024, so here ny my 2024 LinkedIn Rewind, by Coauthor.studio: 2024: The year I discovered that building world-class data platforms isn't about perfect technology—it's about creating an environment where human imperfection becomes our greatest innovation engine. This journey transformed how I see leadership in AI and data. No longer just about technical excellence, but about understanding how diverse perspectives and unique human strengths can solve complex challenges. My transition to Director of AI and Data Platform at Collinson wasn't just a title change; it was a profound shift in understanding that breakthrough innovations emerge from teams who embrace their differences. Three posts this year crystallized this transformation: ➡️ Product Development Insight: "An underappreciated engineer and an overwhelmed product manager—two sides of the same coin, yet often miles apart." Success comes from bridging technical excellence with human understanding. https://lnkd.in/efJE8Yub ➡️ Team Building Reflection: "Building a great team means bringing together great people. Not everyone needs to fit perfectly into your ideal checklist; what someone lacks in one area often highlights their strengths in another." https://lnkd.in/egEV6qf4 ➡️ Leadership Philosophy: "Make it a habit to ask at least one person every day, 'How can I help?'" https://lnkd.in/ekWHy7ap Beyond these insights, 2024 marked significant milestones: completing the INSEAD Leadership Communication with Impact certification, speaking at the CDO Travel and Hospitality Exchange, and continuing to grow "One Perspective at Once" on Substack. Looking ahead to 2025, my focus remains unwavering: building platforms that unifies the intelligence both of human and machines, that don't just process information, but unlock true potential. The most sophisticated AI won't replace human creativity—it will amplify our collective intelligence. For data leaders and AI practitioners: your most powerful technology is the team you build, not just the code you write. #AI #DataLeadership #LeadershipLessons #TeamBuilding #LinkedInRewind #Coauthor #2024wrapped

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This is a useful high-level summary of how the data engineering landscape has evolved this year.

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What if Generative AI could take you on a bike ride through 750 years of Amsterdam’s history—in just 2 minutes? To celebrate Amsterdam’s 750th anniversary, the city that has been my home and inspiration for over a decade, I’ve created "Bike to Future": an AI-powered video tribute to Amsterdam’s unique spirit. "Bike to Future" blends history and technology, featuring Amsterdam’s most iconic symbol—the bike. It takes you on a journey through 9 defining historical periods, from the Middle Ages (the birth of Amsterdam) to the present day, showcasing how technology can honor and reimagine the city’s timeless spirit. Take a ride and see how the past and future collide in this heartfelt tribute to Amsterdam. Which historical era speaks to you the most? Let me know in the comments! For more, visit: https://lnkd.in/ea54jumd #Amsterdam750 #AIArt #CreativeAI #CulturalInnovation #DigitalArt #GenerativeAI Hans Teuben Maaike van Groenestyn Eva Brobbel Sietse Bakker

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Friends, it's time... We're going to dive 🏊🏽‍♂️ into the Mathematical Model of Revenue Architecture...👨🏽‍🔬 Because none of this matters, if you can't count ➕ ➖ ✖ ➗ or know of these effects 📈 on which you build a SaaS or AIaaS business. Let alone that you will T3D2 🚀 . Unless you leverage and weave these principles and mathematics into everything you do in terms of GTM 👨🏽‍🚀 . T3D2 is a concept in our business where as soon as you hit 1 mio ARR, you triple (3x) every year and then you double (2x) your revenue for two years, bringing you to a theoretical 100 mio+ ARR (and IPO). Today on average it takes 14 years to IPO, but that's a different topic. We're here for the math today. My math teachers made 1 critical mistake when trying to educate me on these principles: they expressed it in theory - not in money. 🐶 Jacco van der Kooij taught me many things, like reading again, and much more often. But one of the most important gifts was falling in love with math. But a different kind of math than you're taught at school: GTM math. Like how hypergrowth is the result of non-linear behavior and things like COMPOUND and EXPONENTIAL impact... It's a bit like magic - but then science magic, if you will... And perhaps most importantly: how far that REALLY goes or is in every little thing you do in your GTM system, aside from the data models itself. Math matters. And you better get your math straight. I'll help you with that. This week, I'll cover: ⏺ The elements of Accelerated growth ⏺ Linear vs. Exponential Impact ⏺ Nonlinearity in Acquisition ⏺ Nonlinearity in Retention & Expansion Looking most forward to taking my famous lab coat on and diving into the theory with you folks. School tomorrow starts at 2.06 pm. Don't be late.

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I used to think complex models were everything in AI. Then I learned: real value starts with simple, goal-aligned data. Simplicity drives results. 🧩 Want to hear more lessons from my data journey? Hit follow! #AIstrategy #DataDriven

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Doing some fun weekend research on AI-driven data fabric. Still remember over 10 years ago when a speaker at the TDWI said the next person to innovate at the metadata layer will win big. Before AI, cataloging, mapping relationships, and maintaining lineage was a complete pain—it was all manual. An often-overlooked but super important innovation for enterprises is the AI-driven data fabric. This sits above your data sources, integrates with all systems, and essentially gives the metadata layer "superpowers" as I understand it. Now, I don’t see AI-driven fabrics replacing existing tools but rather complementing them. Your data warehouse will still be the central repository for structured analytics, and BI platforms will still handle visualization. But having AI at the metadata layer will analyze data relationships and lineage to make data available for those tools. For example, it can automatically identify that "customer ID" in one system corresponds to "client ID" in another, saving time and ensuring consistency. For less technical folks, think of the data fabric as a super-smart logistics manager. He/She/They 😉 know where every piece of raw material (data) is located, ensures materials are cleaned, organized, and transported (processed) to the right place at the right time, and communicates seamlessly with the warehouses (data warehouses), production lines (BI tools), and suppliers (point-of-record systems) to make the entire system efficient. Back to technical jargon, one of the most important AI-driven capabilities is Real-Time Query Optimization (determines how to retrieve data for a user query). For example, redirecting a query to a data lake for high-volume raw data or to a data warehouse for pre-aggregated metrics. Traditional data fabrics relied on predefined rules, but now, AI algorithms dynamically map relationships, clean data, and optimize query paths, making integration smarter, faster, and more adaptive. Overall, this might not be as flashy as building LLM models or exploring app-level use cases, but it’s a critical enabler in the enterprise space that ensures everything runs smoothly. If you're serious about scaling AI and data initiatives, I highly recommend taking a deep dive into this topic. #aidatafabric #enterpriseai #datamodel

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Call it web3, call it decentralization, call it the new web, call it Going Dutch. Fact is: we need to change how we deal with our data on the web. And marketing needs to reinvent how it deals with that. Great article by Arno Otto.

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𝐒𝐩𝐞𝐧𝐝𝐚𝐭𝐚: A True 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Solution (𝘐𝘵'𝘴 𝘯𝘰𝘵 𝘺𝘰𝘶𝘳 𝘨𝘳𝘢𝘯𝘥𝘱𝘢'𝘴 𝘴𝘱𝘦𝘯𝘥 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘺𝘮𝘰𝘳𝘦!) No "data warehouse", "data lake", or "data lakehouses" needed either! Nor do you need a star schema to avoid suffering in computational limbo either! (But if you still think PowerBI is a great presentation tool, it can build you a perfect star schema on millions of records in seconds ... ) Just real spend analysis, which includes the ability to: * extend the schema at any time * add new derived dimensions at any time * change those mappings at any time * build derivations, data views, and mappings that are dependent on other derivations, mappings, views, inputs, linked datasets, and so on, with real-time “recalculation” * create new views and reports WITHOUT dumping the data to Excel * preserve ALL of the above on cube data refreshes (that's right! you DON'T have to rebuild the cube from scratch on every refresh!) * do so in your OWN private copy of the cube (that inherits from the shared master cube) so you don't have to change the common cube for your own custom analysis and reporting needs * get the answer you need RIGHT NOW (not on the next refresh next month when you've forgotten why you were told to get the answer in the first place) So what else can it do besides every spend analysis report ever invented under the sun? * 𝐒𝐚𝐥𝐞𝐬 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Over Time By Salesperson … and Client Type ... that dynamically updates in real time on every parameter or "what-if" change * Real 𝐏𝐨𝐬𝐭-𝐌𝐞𝐫𝐠𝐞𝐫 𝐖𝐡𝐢𝐭𝐞-𝐒𝐩𝐚𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (if you think you can do this now, think again) * 𝐌𝐮𝐥𝐭𝐢-𝐯𝐚𝐫𝐢𝐚𝐭𝐞 𝐁𝐢𝐥𝐥 𝐑𝐚𝐭𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ... and more! https://lnkd.in/eM9Y4fez

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🧮 Amazon's turning to AI to supercharge its finance operations tl;dr: Amazon's ditching spreadsheets for something a little more...intelligent. They're weaving generative AI into their finance processes for everything from sniffing out fraud to forecasting financial performance. * * * 🧐 The Big Picture: Remember when Amazon was all about just selling books online? Fast forward to today, and they're at the forefront of AI innovation. This isn't their first rodeo with AI either. They've already been using rule-based systems and machine learning in their finance departments. Now, they're taking it up a notch with generative AI, for example for analyzing contracts, predicting future financial trends, and even navigating the labyrinthine world of tax regulations. 📦 What it means for You: This is a prime example (🥁) of a company putting its money where its mouth is. Amazon's not just talking about AI; they're actively integrating it into the core of their business. Now usually Amazon is an early adopter of technologies that become mainstream over time. And it's likely that Corporate Finance will be integrating quite a bit of AI in the coming years. 🚀 My take on It: Amazon's commitment to AI, particularly within its finance department, shows a larger trend we're seeing across industries. This strategic move is likely to influence other companies to rethink their approach to AI adoption, especially when it comes to functions like finance, which are traditionally reliant on manual processes, and rarely at the technological forefront. #CorporateInnovation #AIinFinance #GenerativeAI #TechLeadership #FinanceTransformation Image credit: Acabashi via Wikimedia Commons

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🎤 𝗜'𝗹𝗹 𝗯𝗲 𝘀𝗽𝗲𝗮𝗸𝗶𝗻𝗴 𝗮𝘁 𝗠𝗲𝘁𝗮𝗽𝗵𝗼𝗿𝘂𝗺 𝟮𝟬𝟮𝟰! I will share my ideas on rapid iteration and data thinking for AI products and how we can apply them to new challenges in software and society. 💡 The lack of a concrete, feasible vision for our products makes them complex and opaque, moving further away from their designers' original vision (look at legacy codebases!). We face a similar problem in society, where the lack of a compelling vision leads to re-hashes in culture and a lack of alternatives for the systems we design and manage ("hauntology," as Jacques Derrida defined it, and the "slow cancellation of the future," in the words of the late Mark Fisher). 🔧 Methods such as Futures Thinking and Amazon's Working Backward can help ensure the viability of our designs and builds. Mark Lambertz, Christina Albrecht, Dr. Sina Niedermaier #aiproducts #datathinking #progress

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PowerKPIs allows all your users to access your KPI grids and driver trees throughout the entire organization on every level. With this enhanced meta data embedding you lift your steering information to the next level. Want to know more? Contact me!

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If you think KPI cards in #tableau are great but require a lot of timw/effort to build, have a look at POWERKPIs It is basically a drag and drop KPI card and driver tree builder.

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Ready for an #Agentic future where AI agents collaborate, adapt, and act autonomously? OpenAI announced the next frontier in Enterprise AI: "Swarm." It introduces the concept of agent chaining: If an agent cannot complete a task independently, it can delegate the task to another agent through a handoff. Want to learn more about Agentic AI? Attend the IIT Bay Area Leadership Conference 2024 this Saturday (October 19, 2024). Jeremiah Owyang will give a talk on "Agentic AI: Shaping Autonomous Future." The article mentions: "Swarm’s release has also rekindled debates about the ethical implications of advanced AI systems." You can attend Brian Green's "Ethical Decision Making In the Age of Data and AI" talk to learn more. It mentions: "Security experts stress the need for robust safeguards to prevent misuse or malfunction in networks of autonomous agents." You can attend two sessions: (1) "Driving Business Innovation: AI, Zero-Trust, and Investment Insights" by Mario Espinoza (CPO of Illumio), Dr. Vipin Chaudhary (Prof and Chair at Case Western University), Hitesh Sheth (CEO of Vectra AI), and Pramod Gosavi (Venture Partner at Blumberg Capital) and (2) "Gen AI: Data Security and Privacy" by Dr. Anand Kashyap (CEO at Fortanix), Nilesh Thakker (President at Zinnov), and Dr. Anirban Ghosh (AI/ML Consultant at Infosys). The article mentions: "The specter of job displacement adds another layer of complexity," and you can attend the session "Future of Work in the Age of Data and AI: VCs' Perspectives" by Ashmeet Sidana (Engineering Capital), Raviraj Jain (Lightspeed Ventures), Apoorva Pandhi (Zetta Ventures) and Lochan Alagh (VC Nest). And, of course, there are many more sessions. Register here: ttps://lnkd.in/gRUVzbfA #Leadership #IITBayArea #TechIcon Speaker Affiliations: Databricks, Glean, Sapphire Ventures, Accenture, Illumio, SAP, Okta, Birlasoft, Infosys, Vectra AI, Fortanix, Postman, Workato, and many more Nidhi Kannoujia, Arnab Debnath, Ashis Khan, Anshu Agarwal, Swapnil Sinvhal, Lochan Alagh, Rao Charagondla (CNR), Dipty Desai, Ph.D., Praveen Vayalamkuzhi, PhD, FRSA, Surbhi Kaul, Ishita Sharan, Hrishikesh Sathawane, Bidyut Parruck https://lnkd.in/gJwa_ZXJ.

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