June Dershewitz

🔍 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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Huge win on #Quarterly Earnings for Palantir Technologies (#PLTR +13.26% pre-market) If you were on the #Earnings #Call, you might have heard Shyam Sankar talk about their core product: #Data #Ontology. The CTO describes how Ontology gives Palantir an unprecedented 10+ year advantage in the #AI space, not for competing with the #ChatGPTs models of the world, but by actually providing enterprise framework/software to deploy 3rd party AI models beyond your design, content writing and #meme producing teams. We are talking about creating your JARVIS from Iron Man; scaleable to every end user in your company - with access points, seamless resource sharing, and actionable analytics with tailored cross-department recommendations. And it all starts with Ontology. We've written about Data Ontology and how this can be deployed in a Marketing environment. Granted this is child's play compared to PLTR's Apollo, Foundry, and Gotham offerings; yet this is a Day 1 thinking approach to a complex and ever-evolving endeavor. Let me know what you think: https://lnkd.in/evtcWeTb

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In the past, it has been a taboo for businesses to share their data with advertising giants like Google & Meta, fearing that the latter may expand horizontally to create a competing business model with all these shared data insights. To an extent that they rather use self-host on-premise analytics platform, than the cloud version 😓 As cookies are going away soon, there will be a surge in call for use of S2S tracking & conversion APIs to share more data with the advertising platforms for better campaign measurement accuracy. Is data sharing still a taboo or to make way for the future cookie-less environment?

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Are you familiar with #semantic #data? If you're an advertiser or publisher you should be. Quo Vadis interviewed Luke McGuinness, one of the founders of Above Data, the leader in the semantic data space. They know a thing or two about data... they built LiveRamp's data marketplace. A semantic data layer translates data that is unusable for marketing purposes and makes it usable. That's a big deal. Check out the interview here: https://lnkd.in/d-CPvSAn

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Want to learn how to build a #generativeAI chatbot in #BigQuery? Learn how in this blog:

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AI’s progression toward agentification, even directionally, is really starting to make the case for wider use of ontological data management. How else will you scale data efficiently enough to keep up with the rate of change. I see Palantir Technologies at the beginning of a major growth phase. Their platform was ahead of its time and until recently found their niche in high dollar high ROI applications within oil and gas, government, military and medical, among others. Places where the volume of data combined with complexity of data contxtualization made an ontology layer necessary for scale. The truth is that every company currently has this challenge. What they don’t have is the business case to justify the cost of Palantir Technologies Foundry. That is about to change. Soon the business case will be an existential matter of survival as opposed to one of incremental improvement at near parity with their competitors. Existential threats are usually funded as the business case was case for survival far trumps any other priority. Why will there be existential threats? It’s fairly simple. The companies that invest ahead in data management will be first to market with emerging AI capabilities. Those that don’t will either adjust rapidly or loose market share and/or simply fail. The future is not AI. It’s the ability to connect your trained agents to the data they need to be successful. It requires a scalable architecture. I’m not saying it has to be Foundry but it does have to be something. How are you thinking about data management in the age of rapidly evolving AI? #AI #Business #Data

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Gen AI Skeptics - Yes it's overhyped, hallucinates, & gives outdated answers. BUT seeing 93.7% accuracy & <2.7% hallucination with Docket's AI. How's that possible? By ensuring responses are based on the provided data (& not the wild wild web!), RAG (Retrieval Augmented Generation) models hallucinate minimally. Too technical? Here's a simple explanation - Docket's AI is centered around an organization's data (maybe Slack, Notion, Highspot, Gong, Confluence, Google Drive, Intercom, Website + 100s of curated &/or uncurated data sources) But many of these sources are outdated or contradictory, at 95% orgs, if not all! How can Docket be that accurate then? By sifting through all data in seconds & accounting for 3 factors- 1. Recency Emphasizes recent information over older information 2. Frequency More frequent occurrences are likely true when there are conflicting information sources 3. Authority Considers information provided by subject matter experts as more reliable Can't believe it? See it yourself or you can hear from our customers directly! Fun fact 1. --> Ask it an irrelevant question - it'll say I don't know! Not make up an answer! Fun Fact 2 --> If source data is recent + frequent + credible - it calls out the conflict & doesn't assume randomly. P.S. I asked Docket this question on our slack. Check its answer below. Want to ask questions to Docket? Ping me! (It's our AI Sales Engineer that knows the product in & out!)

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Stellar reporting by Jack Neff who undertook a six month investigation, submitting access requests to various data brokers to understand what data they held on him. His findings echo my own experience. Much of the data marketers rely on for ad targeting is inaccurate, contradictory, questionable, or all of the above. Questions to ask yourself: ✅Do you know what providers your agency is working with, or what in-platform segments they’re using? Is this outlined in your media plan? ✅Do you understand the provenance of the data? From where was it sourced? With permission? Is it stale? Is it compliant? ✅If your customers knew what audience segments they were in, or what data you used to reach them, would they find it invasive or creepy? (If we want to talk about what real brand safety challenges are…they look like this.) Today, marketers are still wasting money on bad data to reach fake people on unsafe sites, while newsrooms shutter and democracy hangs in the balance. All of this substantiated by equally dodgy metrics, enabled by opacity, benefitting adtech companies and data brokers, at the expense of truth and trust. A reminder to marketers: Check Your Data. Make this access request experiment a standard part of your RFIs and vendor selection criteria. Link: https://lnkd.in/eFHC3FVm Thank you to Ad Age for the opportunity to share my thoughts.

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Data is language. So which large language model -- #ChatGPT or #Claude -- does the best at data analysis? Was humbled to have E'Narda McCalister help out on this one! https://lnkd.in/dHdkhW7P

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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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something to get lost in over a long weekend: this is, hands down, the best collection of public datasets, if finding stories in data is your thing: Jeremy Singer-Vine's Data is Plural newsletter I have been following going on 5 years now, and week for week, it is mind blowing how many interesting datasets are compiled. Stacker has told 100s of stories sourced from these archives over the years, and you can too. We constantly tout the value of data driven stories - this is a must follow for anyone looking to tell stories from data: https://lnkd.in/e_yMUkKR

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Introducing Mantle, a game-changing solution for startups struggling with time-consuming equity management. Mantle is an AI-powered platform that automates the process, eliminating manual data entry and errors. By leveraging Google Cloud 's advanced AI capabilities #Gemini and #VertexAI, Mantle promises to save startups valuable time and resources, enabling them to focus on growth and innovation. #startups #equitymanagement #AI #GoogleCloud #VertexAI #Gemini #automation #efficiency #innovation

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️‍🔥📰 Hot off the press: Ramp's summer 2024 spending data, covering billions of dollars in aggregated and anonymized transactions over the past 3 months. My favorite finding: AP spending with AI vendors jumped 375% year over year. Companies are making longer-term AI commitments, and moving their spend from experimental (💳) to operational (bill/AP). So who are the fastest-growing AI vendors on Ramp? Check it out here: 👀👀 https://lnkd.in/eVCwAJap

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One of my favorite Maven Showcase projects this week is the LA County Crime Mapping Tool by Tai C. Here's what I love about this one: 👉 The analysis is directly tied to a clear and specific objective – targeting locations in LA County to launch decriminalization ad campaigns. 👉 The output is simple (a single map visual) but provides the exact information that stakeholders need 👉 The interactivity gives users the ability to drill up or down to explore trends at different levels of granularity My only feedback might be to include some specific takeaways and recommendations in the project description itself – such a great way to showcase those analytical thinking skills 💪 Link to the project details in the comments – check it out and leave Tai some love!

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💡 AI Adoption at data.world: Aligning with Industry Trends and Leading in Experimentation 💡 As generative AI gains momentum, enterprises are navigating its integration with a mix of excitement and caution. At data.world, we’re thrilled to see our approach aligning closely with key industry trends—and in some areas, leading the way. From robust training programs and active experimentation to meaningful adoption metrics, we’re fostering a culture that doesn’t just keep up with AI advancements but drives them. 📈 In a recent report by AI at Wharton and the GBK Collective ( https://t.co/LEPWQ69uoY), our own journey mirrors the broader trends they highlight, with data showing our teams are actively embracing AI tools like ChatGPT at levels well above industry averages. Curious about how we’re achieving this? Read on to see how data.world is setting the standard in responsible, innovative AI integration. #AI #GenerativeAI #Innovation #dataworld #AIAdoption #FutureOfWork https://lnkd.in/gWdVu_ZH

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