Knowledge Graphs In The Enterprise: What You Need To Know | Panel Discussion The Connected Data podcast

36:23

Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning. However, most of these approaches share little common ground, nor do many of them integrate effectively with popular data science tools (pandas, scikit-learn, spacy, pytorch), nor efficiently with popular data engineering infrastructure such as Spark, RAPIDS, Ray, Parquet, fsspect, etc. In this podcast episode, Paco Nathan reviews kglab – an open source project that integrates most all of the above, and moreover provides ways to leverage disparate techniques in ways that complement each other, to produce Hybrid AI solutions for industry use cases. Slides available: https://derwen.ai/s/kcgh --- If you liked this podcast, check #CDL24 for more Presentations, Keynotes, Masterclasses, and Panels on cutting-edge topics from industry leaders and innovators: https://2024.connected-data.london/…

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Enterprise Knowledge Graphs: Breaking Through Organizational Inertia to Reimagine Data Management | Panel Discussion 39:03

25 weeks ago39:03

39:03

Industry leaders from Accenture, Johnson & Johnson, and the Enterprise Knowledge Graph Foundation dive deep into the transformative potential of knowledge graphs, exploring how these semantic technologies are revolutionizing enterprise data management. Featuring Mike Atkin, Laurent Alquier and Teresa Tung. The conversation reveals a critical shift from traditional data processing to a more nuanced, context-rich approach that prioritizes data meaning and reusability. Participants discuss how organizations are moving beyond experimental pilots to enterprise-wide implementations, driven by a growing recognition that data incongruence is a significant liability in today's data-driven business landscape. The discussion unveils the key challenges of knowledge graph adoption: * Overcoming organizational inertia * Bridging technological gaps, and * Fundamentally changing mindsets about data representation. Experts share insights into the importance of telling compelling stories about knowledge graphs, focusing on business value rather than technical complexity. They emphasize the need for incremental implementation, collaborative approaches, and the crucial role of knowledge engineers who can translate between technical capabilities and business needs. We've arrived at a pivotal moment for enterprise knowledge graphs: the technology has matured, business leaders are increasingly receptive, and there's a growing understanding that these semantic technologies offer more than just another IT solution. Knowledge graphs represent a fundamental reimagining of how organizations can capture, understand, and leverage their data—moving away from the myth of a single version of truth towards a more flexible, context-rich approach that allows multiple perspectives to coexist. For businesses looking to remain competitive in a data-driven world, the message is clear: the time to start building knowledge graphs is now. -- Michael Atkin has over 30 years of experience as a strategic analyst to financial institutions, regulators and market authorities on the principles, practices and operational realities of data management. Dr Laurent Alquier's current role is to shape the architecture, design and development of J&J’s Knowledge Sharing ecosystem to further enable Emerging Technologies and Innovation management, Enterprise Architecture, and other IT strategic capabilities. Teresa Tung is a Managing Director at Accenture Labs responsible for taking the best-of-breed next-generation architecture solutions from industry, start-ups, and academia, and for evaluating their impact on Accenture's clients through building experimental prototypes and delivering pioneering pilot engagements. -- For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024…

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Rebooting AI: Adding Knowledge to Deep Learning | Gary Marcus 38:53

29 weeks ago38:53

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Gary Marcus argues for a shift in research priorities, towards four cognitive prerequisites for building robust artificial intelligence: Hybrid architectures that combine large-scale learning with the representational and computational powers of symbol-manipulation Large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledge Reasoning mechanisms capable of leveraging those knowledge bases in tractable ways And rich cognitive models that work together with those mechanisms and knowledge bases. Although there are real problems to be solved here, and a great deal of effort must go into constraining symbolic search well enough to work in real time for complex problems, Google Knowledge Graph seems to be at least a partial counterexample to this objection, as do large scale recent successes in software and hardware verification. -- Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. He is the author of five books, including The Algebraic Mind, Kluge, The Birth of the Mind, and The New York Times best seller Guitar Zero, as well as editor of The Future of the Brain and The Norton Psychology Reader. Gary has published extensively in fields ranging from human and animal behavior to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence, often in leading journals such as Science and Nature, and is perhaps the youngest Professor Emeritus at NYU. His newest book, co-authored with Ernest Davis, Rebooting AI: Building Machines We Can Trust aims to shake up the field of artificial intelligence. -- For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024…

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The Enterprise Knowledge Graph | Omar Khan and David Newman 32:34

33 weeks ago32:34

32:34

Join Omar Khan and David Newman as they canvas the Enterprise Knowledge Graph, and how you can apply it using its cornerstones of: Foundational building blocks Information model expressivity Machine understandable representations Transcending the relational model How an EKG expands on a graph and a knowledge graph Provides an infrastructure for Machine Learning Contrasting an unlinked with linked data environment Question and answering model emergence Semantic similarity & embedding Focused UI --- David Newman provides leadership and expertise for the advancement of knowledge graph solutions at Wells Fargo. His team develops innovations that employ key knowledge graph capabilities, including ontology models, semantic and property graph databases, graph analytics, knowledge graph embeddings and graph visualization techniques. David’s core mission is to actualize the potential of knowledge graph at Wells Fargo by creating a collaborative knowledge graph modeling community, developing enterprise standards and best practices, and creating operational pipelines for the ingestion, transformation and consumption of data using knowledge graphs. David’s initiatives include leveraging knowledge graph technology to fulfill business use cases by creating expressive enterprise and line of business ontologies, knowledge driven data asset catalogs, linked operational knowledge graphs and applying machine learning algorithms that train on knowledge graphs. David also chairs the Financial Industry Business Ontology (FIBO) initiative, a collaborative effort of global banks, financial regulators and vendors, under the auspices of the Enterprise Data Management Council (EDMC). Their goal is to semantically define a common language standard for finance using ontologies. Omar Khan is presently a member of Data Management & Insights, fostering Wells Fargo efforts and building applications as Technical Lead in Knowledge Graph & Semantic Technologies. Prior to his current role, Omar built novel solutions for the business during an 11-year tenure as a consultant and full-time employee within Brokerage Technology. While with Brokerage Technology, Omar helped to develop many key applications, and led efforts contributing to a majority of the IT portfolio in Wealth and Investment Management. A few years ago he became known for contributing to proof of concepts in areas unexplored, but necessary for future changes in direction for various lines of businesses. Omar successfully implemented game-changing software development ideas, and this helped form a foundation to allow me to join Innovation Group's R&D, and subsequently Data Management & Insights, specializing in Enterprise Knowledge Graph technologies. Emerging technology was and still is his specialty and passion. -- 👉 For more on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology, join Connected Data London this December - Book Your Ticket Now…

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Deep Learning on Graphs: Past, Present, And Future | Michael Bronstein 29:33

38 weeks ago29:33

29:33

Graph representation learning has recently become one of the hottest topics in machine learning. One particular instance, graph neural networks, is being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design. Despite the promise and a series of success stories of graph deep learning methods, we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In this Michael Bronstein outlines his views on the possible reasons and how the field could progress in the next few years. -- Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. -- 👉 For more Deep Learning on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology, join Connected Data London this December - Book Your Ticket Now…

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Connected Data London 2024 Call for Submissions Roundtable | Panel Discussion 1:26:36

42 weeks ago1:26:36

1:26:36

Connected Data is coming back to London in 2024, on December 11-13. Join us for a tour de force in all things Knowledge Graph, Graph Analytics / Al / Data Science / Databases and Semantic Technology. Call for submissions and volunteers, program committee, chairs, and initial lineup have been announced. This online roundtable highlights the Connected Data landscape and how it's reflected in our Call for Submissions, while it also goes over the event's format and answers audience questions. Key topics: How taxonomy, ontology and knowledge graphs can help GenAI and Large Language Models: Graph RAG and beyond The Knowledge Graph Development Lifecycle: Building, Consolidating and Managing Knowledge Graphs Featuring Connected Data Founders George Anadiotis and James Phare, Program chairs Amy Hodler and Paco Nathan, and Program Committee Members Panos Alexopoulos, Giuseppe Futia, Heather Hedden, Juan Sequeda, Ivo Velitchkov and Andrea Volpini. Call for submissions: https://www.connected-data.london/call-for-submissions…

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The Momentum Behind Semantic Reasoning and Why It’s Here to Stay | Panel Discussion 1:31:21

47 weeks ago1:31:21

1:31:21

What does reasoning have to offer? How does it add so much value to data? Who is using it and why should I care? All questions that we’re delighted to answer. Access to data has exploded over the last decade, but it leaves us asking what to make of it all? Often lacking quality, reasoning is required to enrich data by adding context and insights, serving up knowledge, not just numbers. This expert panel will explore the who, what, why, and how of reasoning: Its foundations, its advancements over the years, and its bright future. Google, Amazon and Facebook are just a few of the giants implementing reasoning today to great effect. With that said, this is not a tool exclusive to the Fortune 500—intelligence is buried in data everywhere, a valuable asset at any scale. Key Topics A technical introduction to reasoning Reasoning in industry Getting started with reasoning The future of reasoning Target Audience Developers Data Engineers Technical Leaders Management CxOs Investors Goals To introduce the concepts of reasoning & the theoretical foundations that support it. To explore the role of reasoning in industry—who, what, why, and how. To examine the opinions of sector leaders as to the future of reasoning.What is the current state of the art, how and where is it used in the wild? Session outline: Meet the panel An introduction to reasoning What separates a knowledge graph from a simple graph? What is semantic reasoning? Logical consequences, facts & axioms The reasoning standards and beyond Where is reasoning used in production? What kind of problems are being solved with reasoning? Who uses this technology? What is the current state of reasoning in industry? From data modelling to the technical stack What are your options for reasoning today? Where can reasoning be deployed? From Cloud to Edge to on-device What performance can you expect from a system with reasoning? Where do I start? What do I need before I start reasoning? Who has the skills I need? My company is resistant, why should they change? Reasoning a future What does the future of reasoning look like? What are the challenges? How will we get there? Format A series of short presentations by reasoning experts, each followed by a discussion from the panel, coordinated by moderator. Audience interaction and questions are encouraged. 2 hours running time. --- Panel By Haonan Qiu , Ian Horrocks , Ora Lassila , Marcus Nölke , Peter Crocker And Tara Raafat --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london To keep up with updates in Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology subscribe for Connected Data London (CDL) blogs, newletters. Meet over 1,000 industry professionals by registering to attend Connected Data London (CDL) held from 11-13 of December in London. The 2024 edition will be our finest and biggest event to date, featuring our tried and true recipe of bringing together leaders and innovators in Masterclasses, Keynotes, Presentations, Workshops and Panels, plus lots of new and exciting features such as Networking and Unconference sessions, a Gala Dinner and Speaker Lounge.…

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Data Revolution: The Emergence of the Decentralized Enterprise Knowledge Graph | Tony Seale 31:40

51 weeks ago31:40

31:40

An AI tsunami is on the rise, and the past few months have only amplified it. To survive it and thrive in tomorrow’s economy, organizations big and small must rethink the way they do business. To do this, a radical shift in the way they work with their data is needed. And no, we don’t mean Big Data. By now, most organizations have gotten their Big Data. And that is a problem. Not because we can’t accommodate Big Data, but because the more data you have, the harder it becomes to connect it and use it. We need to go beyond Big Data, towards Connected Data. We’ll show how enterprises can use decentralized Knowledge Graphs to vastly increase the connectivity of their data, drawing on hard won experience of architecting and successfully delivering innovative technical projects for the world’s largest financial organisations. Large enterprises that want to survive the AI tsunami must undergo a profound transformation in the way they think about their data. It starts by accepting that they need to link a large percentage of ALL their data together into a unified whole. Achieving this will require a radical rethink of some established ideas about enterprise data integration. The truth is meaningful data doesn't exist in isolation; everything is positioned within the context of everything else. That is why the future of data is graph shaped … but what are graphs and what is so great about them? Knowledge Graphs are a really powerful tool, but on their own, they are not enough to transform enterprise data integration. We also need to get our heads around the complex idea of decentralisation. In a decentralised data mesh, the responsibility for data integration is pushed down to the individual applications. Unbeknownst to most people, a third of all web pages now contain little islands of data that help the search engines build their knowledge graphs. Enterprises do not need to reinvent the wheel to build themselves a Decentralised Enterprise Knowledge Graph. They can just take this battle-hardened web tech and use it behind their firewall to connect their internal data. In other words, the tools for this job already exist but enterprises are not yet using them internally. In this talk, we’ll share the hard won experience of how this was done for the world’s largest financial organizations. --- Tony Seale is a Knowledge Graph Architect at UBS. An experienced software architect and polyglot programmer with a proven track record of successfully delivering Knowledge Graphs into production for Tier 1 investment banks. He has been exclusively focused on building decentralized Knowledge Graphs for the last ten years and has given talks, produced videos and written articles to promote the technology. --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london…

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Graph Machine Learning - Research and Industry Applications | Panel Discussion 1:35:38

1 year ago1:35:38

1:35:38

Graph-based technologies became first-class citizens in various industries and many practical applications. Still, building performant and reliable machine learning pipelines over graph data, e.g., graph machine learning applications and products, remains a non-trivial task. This panel discussion brings together academic and industrial experts from fields where Graph ML yields significant gains and greatly improves traditional processes. In addition to highlighting successful business cases, the panel concentrates on questions often dismissed or hidden behind the curtains of modern Graph ML applications. In particular, we will talk about the origins of graph data, its modeling, organization, and processing aspects; best communication interfaces; bridging a gap between products and ML algorithms as well as measuring their practical impact. On a higher level, the panel will discuss upcoming trends in industrial Graph ML and prospective disruptive applications. Key Topics Graph Machine Learning Deep Learning Graph Data Management Knowledge Graphs Graph ML in Production Target Audience Machine Learning Practitioners Data Scientists Data Modelers CxOs Investors Goals Explore the interplay between machine learning and knowledge based technologies How to get the “actionable” knowledge from the graph data? How can those approaches complement one another, and what would that unlock? What is the current state of the art, how and where is it used in the wild? NLP Biomed Industry (say, Ad Tech) What are the next milestones / roadblocks? Where are the opportunities for investment? We know drug discovery is on its highs, NLP is being democratized rapidly, what else? Session outline: Introduction Meet and Greet Setting the stage Knowledge Graphs, meet Graph Machine Learning It’s all about the data: How do you create, maintain, and process graphs? Databases or tabular sources? Do you consider data modeling aspects? Best communication interface: natural language or structured query languages? How can machine learning help create and populate graphs (including KGs)? Cover some of the current state of the art An edge - does it appear naturally or derived from node similarities? What kind of problems can we solve by using it? NLP, Biomed, Industry (say, adtech) How academic datasets align with real-world tasks Where is this used in production? Success stories and business cases What are the major roadblocks / goals, how could we address them, and what would that enable? How to bridge the gap between business goals and Graph ML models? How do we measure the impact of applying ML models in real-world tasks? Metrics, A/B testing, generally about setting things in production Who are some key players to keep an eye on? Both from industry and research Panelists: Mikhail Galkin. Researcher, Mila | McGill University Dr. Tiffany Callahan. Researcher, University of Colorado, Anschutz Medical Campus Andreea Deac. Researcher, Mila | Université de Montréal Dr. Charles Hoyt. Researcher, Harvard Medical School, Laboratory of Systems Pharmacology Sergei Ivanov. Research Scientist, Criteo AI Lab --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london…

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Systems that learn and reason | Frank Van Harmelen 28:16

1 year ago28:16

28:16

After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks. In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set. -- Frank van Harmelen leads the Knowledge Representation & Reasoning group in the CS Department of the VU University Amsterdam. He is also Principal investigator of the Hybrid Intelligence Centre, a 20Μ€, 10 year collaboration between researchers at 6 Dutch universities into AI that collaborates with people instead of replacing them. -- Slides available at: https://www.slideshare.net/slideshow/systems-that-learn-and-reason-frank-van-harmelen/267008886 --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london…

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Novel AI Hardware Architectures for Graph Processing | Panel Discussion 1:35:24

1 year ago1:35:24

1:35:24

What do graphs have to do with novel hardware architectures for AI workloads? Graph processing is the key to unlocking new architectures, as much as new architectures can boost execution of graph-oriented workloads. As machine learning-powered applications are proliferating, the workloads that are created in order to serve their requirements are taking up an ever increasing piece of the compute pie. An IDC study found that Data Management, Application Development & Testing, and Data Analytics workloads represented more than half of all IaaS and PaaS spending already in 2018. IDC notes that this was driven in part by initial adoption of artificial intelligence and machine learning capabilities. The rise of generative AI means that as adoption grows, data and AI workloads will dominate. This is why we see NVIDIA earnings skyrocket, as well as a renaissance of novel hardware architectures designed from the ground up to serve the needs of data and AI workloads. More specifically for data analytics, understanding relationships among data points is a challenging but essential capability. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks and draw conclusions from the observed patterns. This is why DARPA set out to develop a graph analytics processor with the HIVE Project. Furthermore, all machine learning models are best expressed as graphs. This is how machine learning libraries such as TensorFlow work. Efficient processing of graph-based networks involves large sparse data structures that consist of mostly zero values, and next generation architectures should avoid unnecessary processing. This panel explores the interrelationship between graph processing and novel AI hardware architectures. Hosted by ZDNet's Tiernan Ray with panelists from some of the most groundbreaking AI hardware companies: Blaize, Determined AI / HPE, Graphcore, and SambaNova. --- Tiernan Ray. Contributing Writer, ZDNet Tiernan Ray has been covering technology & business for 27 years. He was most recently technology editor for Barron's where he wrote daily market coverage for the Tech Trader blog and wrote the weekly print column of that name. He has also worked for Bloomberg, SmartMoney, and for the prestigious ComputerLetter newsletter covering venture capital investments in tech Val G. Cook. Chief Software Architect, Blaize Val G. Cook is Chief Software Architect at Blaize. An AI visionary and authority on the design of graphics and visual computing architectures, Val possesses two decades of experience in graphics and multimedia algorithms and software architecture. He is responsible for the Blaize Graph Streaming Processor software programming environment. Carlo Luschi. Director of Research, Graphcore Carlo is responsible for the study and development of algorithms for machine intelligence. Prior to Graphcore, Carlo was a Member of Technical Staff at Bell Labs Research, Lucent Technologies, and more recently Director of Algorithms and Standards at Icera Inc., which was acquired by NVIDIA in 2011. Raghu Prabhakar. Software Engineer, SambaNova Raghu Prabhakar is a senior principal engineer and one of the founding engineers at AI innovation platform SambaNova Systems. His research interests are in the areas of programming models, compilers, and hardware architecture for reconfigurable dataflow architectures. Evan Sparks. Founder, Determined AI, an HPE Company Evan Sparks, Vice President of Artificial Intelligence and High Performance Computing at HPE, co-founded Determined AI (now an HPE company). His group helps businesses get better AI-powered solutions to market faster and delivers the open source Determined Training Platform for large scale AI model development.…

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Graph Abstractions Matter | Ora Lassila 22:15

1 year ago22:15

22:15

While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models. RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different. Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin). -- Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web. -- Presentation slides available at https://www.slideshare.net/slideshows/graph-abstractions-matter-by-ora-lassila/266140641 --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london…

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Taxonomies: Connecting Data with Duct Tape | Mike Dillinger 31:28

1 year ago31:28

31:28

Taxonomies are the duct tape of connected data. They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains. But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are very difficult to create, to scale, to adapt, to align, and to build on. They don't work well for larger or more complex domains and use cases. Experienced talent and flexible tools for creating them are hard to find and to develop. Often taxonomies are built then abandoned for other, more robust approaches to knowledge organization. It is essential to re-evaluate your connected data strategies in the context of alternative approaches to knowledge organization. ------ Mike Dillinger. Technical Lead for Taxonomies and Ontologies, AI Division, LinkedIn Mike Dillinger, PhD, focuses on teaching machine learning algorithms about the world of work at LinkedIn. Before that, he was Technical Lead for LinkedIn’s and eBay’s first machine translation systems, and an independent consultant specialized in deploying translation technologies for Fortune 500 companies. --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london…

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Investing in Connected Data | Panel Discussion 1:48:06

1 year ago1:48:06

1:48:06

What is Connected Data, and how is it interesting from a market point of view? Knowledge Graphs have reached peak Gartner hype. Graph data science and graph AI are the fastest growing areas in AI. Graph databases are the fastest growing category in enterprise software. Add to this the historical foundations of graph algorithms and analytics and semantic technology, which have been invigorated and are seeing widespread adoption, and you get the burgeoning Connected Data landscape. While there is ongoing technical innovation happening in the domain, how does this translate to market value and opportunities for investment? How is this market defined, and what is driving its growth? Join us as we define and explore this landscape, discuss technology and use cases, challenges and opportunities for growth and investment, and where the future may take us. Join George Anadiotis , Panos Papadopoulos , Bob van Luijt and Konstantin Vinogradov from our Connected Data World 2021 panel discussion as they address the following: Key Topics Defining the Connected Data technology and market landscape Exploring the Connected Data market Providing an outlook for the future Target Audience Entrepreneurs Technical people with entrepreneurial spirit CxOs Decision makers Investors Goals Define and explore the Connected Data landscape for people who are interested in it from a market perspective Answer questions that matter How is this market defined? What are some key drivers for growth? Where are the opportunities for investment? What is the outlook for the future? --- Connected Data London 2024 has been announced!. December 11-13, etc Venues St. Paul’s, City of London Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london…

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JSON-LD as the pidgin of enterprise data integration | Panel Discussion 1:06:12

2 years ago1:06:12