talk-data.com

Join us online for an exploration of Quint, a modern language for modeling and reasoning about distributed systems, and how it relates to TLA+ and other formal specification tools. We’ll start with a guided introduction to Quint’s design and syntax — what problems it solves, how it differs from TLA+, and how it helps express complex distributed behaviors clearly and precisely. What we’ll cover:

• 🧩 What is Quint? A practical modeling language inspired by TLA+, designed for composability, strong types, and developer-friendly syntax.
• ⚙️ Modeling Distributed Systems Describe safety and liveness properties, model concurrent transitions, and reason about correctness.
• 🔄 Comparing Quint and TLA+ When to use which, and how Quint builds on TLA+ ideas while simplifying everyday use.
• 🧠 Hands-On Practice Try modeling a small distributed system — a message queue, consensus protocol, or leader election — either solo or with others in breakout rooms.
• 💬 Open Discussion How formal modeling can help surface design flaws early, and what makes a good specification approachable.

Why attend: This is a friendly, interactive online session for anyone curious about formal modeling or distributed systems. No prior experience required — just bring curiosity and a willingness to think rigorously about correctness.

Are dashboards dead? For complex enterprise use cases, the answer might be yes. In this episode, I'm joined by Irina Malkova (VP Data & AI at Salesforce), to discuss her team's transformational journey from building complex dashboards to deploying AI-powered conversational agents. We dive deep into how this shift is not just a change in tooling, but a fundamental change in how users access insights and how data teams measure their impact.

Join us as we cover: The Shift from Dashboards to Agents: We discuss why dashboards can create a high cognitive load and fail users in complex scenarios , and how conversational agents in the flow of work (like Slack) provide targeted, actionable insights and boost adoption.What is Product Telemetry?: Irina explains how telemetry is evolving from a simple engineering observability use case to a critical data source for AI, machine learning, and recommendation systems.Why Standard RAG Fails in the Enterprise: Irina shares why typical RAG approaches break down on dense, entity-rich corporate data (like Salesforce's help docs) where semantic similarity isn't enough, leading to the rise of Graph RAG.The New, Measurable ROI of Data: How moving from BI to agents allows data teams to precisely measure impact, track downstream actions, and finally have a concrete answer to the ROI question that was previously impossible to justify.Data Teams as Enterprise Leaders: Why data teams are uniquely positioned to lead AI transformation, as they hold the enterprise "ontology" and have experience building products under uncertainty.

L'IA est en train de remodeler le mode de fonctionnement des entreprises, mais son succès ne dépend pas que du simple déploiement d'un logiciel avancé - il nécessite une stratégie globale qui rend vos données prêtes pour l'IA, régit vos modèles de manière efficace et favorise la transformation organisationnelle. Au cours de cette session, Precisely sera rejoint par le groupe Elia qui partagera son parcours réel IA, y compris ses objectifs, ses défis et les mesures concrètes qu'il prend pour les surmonter. 

Les points clés:

- Identifier les piliers des données prêtes pour l'IA, en garantissant l'exactitude, l'exhaustivité et la fiabilité.

- Apprendre les considérations qui différencient le suivi et la gestion de la gouvernance des modèles d'IA qui s'alignent sur la réglementation de l’UE.

- Apprendre des méthodologies éprouvées et reproductibles qui intègrent à la fois des logiciels innovants et des stratégies de gestion du changement pour favoriser le succès au sein d'équipes diverses et encourager une culture d'amélioration continue.

Expert-led talks featuring four Angular Core Team speakers from Google: Alex Rickabaugh, Miles Malerba, Pawel Kozlowski, Doug Parker.

Natural language is quickly becoming our most powerful programming abstraction, perfectly suited to capture the inherent fuzziness and complexity of real-world problems. But despite the power of AI chatbots, endlessly brainstorming in conversational interfaces rarely leads to clarity or reliable results.

This session explores how structured, document-based natural language is uniquely positioned as the ultimate interface for humans to precisely describe complex systems. We'll discuss why conversational interfaces often fail at forcing clarity, and how shifting to a document-driven model ensures that humans articulate their intent clearly and rigorously.

Attendees will learn:

Why natural language (not code) is the most intuitive way to describe complex systems

How documents inherently force clarity, rigor, and structured thinking compared to chatbots

Real-world examples of document-based programming for building reliable, deployable AI systems

Practical insights into transitioning from conversational brainstorming to structured document-driven workflows

In this decades-spanning episode, Tristan Handy sits down with Lonne Jaffe, Managing Director at Insight Partners and former CEO of Syncsort (now Precisely), to trace the history of the data ecosystem—from its mainframe origins to its AI-infused future. Lonne reflects on the evolution of ETL, the unexpected staying power of legacy tech, and why AI may finally erode the switching costs that have long protected incumbents. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

In this episode, we rewind to one of biology’s biggest plot twists: RNA interference (RNAi). Scientists found that injecting double-stranded RNA into Caenorhabditis elegans could silence genes powerfully and precisely—far beyond anything single strands could achieve.

This game-changing discovery revealed:

How dsRNA triggers targeted gene shutdown Why only a few molecules can silence thousands of cells How gene silencing spreads across tissues The first clues toward RNA-based therapies that would change medicine forever

📖 Based on the research article: “Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans” Andrew Fire, SiQun Xu, Mary K. Montgomery, Steven A. Kostas, Samuel E. Driver & Craig C. Mello. Published in Nature (1998). 🔗 https://doi.org/10.1038/35888

🎧 Subscribe to the WoRM Podcast for more whole-organism stories that changed the world!

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠www.veerenchauhan.com⁠ 📧 [email protected]

Join Amanda Cavallaro for a workshop to learn more about fraudulent attacks and ways to mitigate them. We will begin by discussing the types of fraud and how the 5G Network APIs can help us combat them.

The entire workshop has tools for you to learn it in different ways: step-by-step tutorials, videos, learning from participating, and asking questions. We will also have some networking and refreshments.

You can learn how to improve your multifactor authentication using our Vonage SIM Swap and Verify APIs. We also use Firebase Services to store our functions with Cloud Functions, data in the Cloud Firestore database, and our web app using Firebase hosting.

Agenda

Speaker

Amanda Cavallaro - Vonage (Developer Advocate)

Developer advocate @VonageDev | Community Manager @ Bots Brasil, @GDGCloud | @GoogleDevExpert | LinkedIn Learning Instructor (she/ her).

Hosted By

Amanda Cavallaro, GDG Organizer

I'm an Aikidoka, Developer Advocate, Software Developer, Google Developers Expert, Linkedin Learning Author and a Full Stack Web Development Specialist.

Saverio Terracciano, GDG Organizer

Stefano Le Pera, GDG Organizer

Lorenzo Turrino, GDG Organizer

Natalie Godec, GDG Organizer

Systems/DevOps/Cloud engineer and a resident GCP expert. Women TechMakers Ambassador, Google Developer Expert in Cloud and Champion Innovator. I photograph, drink tequila and collect designer handbags ✨

Bruno Ripa, GDG Organizer

I am an italian software architect, in the industry since 2006. I have been in entrepreneurship in Italy, for 6 years, and then continued my career in United Kingdom, in London (2012), a city (or, better, the City) which I consider as my second home. I have worked in several industries (gaming, fintech, digital asset management) and in many companies, with a 3 years parenthesis in Spain (2017-2020), precisely in Barcelona, where I have worked as a contractor for a few USA startups and an european company working in IoT. In March 2020 I made my way back in London, working for Erlang Solutions. I am actually an independent consultant, working in the IoT market,

Carol Pestitschek,

Working with design for more than 11 years, I’m a multidisciplinary designer specialized in UX and Product Design. Passionate about identifying and solving problems, I help companies develop projects that positively impact people's lives. When there are no problems, we know we can always improve.

Kubra Harmankaya, Android Developer

Partner

Vonage (https://developer.vonage.com/) Integrate SMS, voice, video, and two-factor authentication to your apps with Vonage communication APIs.

Complete your event RSVP here: https://gdg.community.dev/events/details/google-gdg-cloud-london-presents-network-apis-workshop/.

In the face of siloed systems and fragmented processes, Helvetia, a leading international insurance group, sought to increase efficiency and innovation. Discover how improving the integrity of location data, centralising it on a scalable platform, and automating workflows led to significant business transformation. With streamlined spatial operations integrated seamlessly with pricing and underwriting, Helvetia eliminated manual work, accelerated regulatory response, revealed new cross-sell opportunities, and enabled greater AI relevance and accuracy.

The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just more efficient but actually increases engagement with underlying content. How might your organization benefit from more targeted access to insights? What valuable information might be buried in your existing research that AI could help surface? With over 30 years of experience in marketing, media, and technology, Dan Coates is the President and co-founder of YPulse, the leading authority on Gen Z and Millennials. YPulse helps brands like Apple, Netflix, and Xbox understand and communicate with consumers aged 13–39, using data and insights from over 400,000 interviews conducted annually across seven countries. Prior to founding YPulse, Dan co-founded SurveyU, an online community and insights platform targeting youth, which merged with YPulse in 2009. He also led the introduction of Globalpark’s SAAS platform into the North American market, until its acquisition by QuestBack in 2011. In addition, Dan has held senior roles at Polimetrix, SPSS, PlanetFeedback, and Burke, where he developed cutting-edge practices and products for online marketing insights and transitioned several ventures from early stages to high-value acquisitions. In the episode, Richie and Dan explore the creation of an AI chatbot for market research, addressing customer engagement challenges, the integration of AI in content consumption, the impact of AI on business strategies, and the future of AI in market research, and much more. Links Mentioned in the Show: YPulseConnect with DanHaystack by DeepsetUnmanaged: Master the Magic of Creating Empowered and Happy Organizations by Jack SkeelsSkill Track: AI FundamentalsRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYURewatch sessions from RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Today I am going to try to answer a fundamental question: how should you actually measure user experience, especially with data products—and tie this to business value? It's easy to get lost in analytics and think we're seeing the whole picture, but I argue that this is far from the truth. Product leaders need to understand the subjective experience of our users—and unfortunately, analytics does not tell us this.

The map is not the territory.

In this episode, I discuss why qualitative data and subjective experience is the data that will most help you make product decisions that will lead you to increased business value. If users aren't getting value from your product(s), and their lives aren’t improving, business value will be extremely difficult to create. So today, I share my thoughts on how to move beyond thinking that analytics is the only way to track UX, and how this helps product leaders uncover opportunities to produce better organizational value. 

Ultimately, it’s about creating indispensable solutions and building trust, which is key for any product team looking to make a real impact. Hat tip to UX guru Jared Spool who inspired several of the concepts I share with you today.

Highlights/ Skip to 

Don't target adoption for adoption's sake, because product usage can be a tax or benefit (3:00) Why your analytical mind may bias you—and what changes you might have to do this type of product and user research work (7:31) How "making the user's life better" translates to organizational value (10:17) Using Jared Spool's roller coaster chart to measure your product’s user experience and find your opportunities and successes (13:05) How do you measure that you have done a good job with your UX? (17:28)  Conclusions and final thoughts (21:06)

Quotes from Today’s Episode

Usage doesn't automatically equal value. Analytics on your analytics is not telling you useful things about user experience or satisfaction. Why? "The map is not the territory." Analytics measure computer metrics, not feelings, and let's face it, users aren't always rational. To truly gauge user value, we need qualitative research - to talk to users - and to hear what their subjective experience is. Want meaningful adoption? Talk to and observe your users. That's how you know you are actually making things better. When it’s better for them, the business value will follow. (3:12) Make better things—where better is a measurement based on the subjective experience of the user—not analytics. Usable doesn’t mean they will necessarily want it. Sessions and page views don’t tell you how people feel about it. (7:39) Think about the dreadful tools you and so many have been forced to use: the things that waste your time and don’t let you focus on what’s really important. Ever talked to a data scientist who is sick of doing data prep instead of building models, and wondering, “why am I here? This isn’t what I went to school for.” Ignoring these personal frustrations and feelings and focusing only on your customers’ feature requests, JIRA tickets, stakeholder orders, requirements docs, and backlog items is why many teams end up building technically right, effectively wrong solutions. These end user frustrations are where we find our opportunities to delight—and create products and UXs that matter. To improve their lives, we need to dig into their workflows, identify frustrations, and understand the context around our data product solutions. Product leaders need to fall in love with the problems and the frustrations—these are the magic keys to the value kingdom. However, to do this well, you probably need to be doing less delivery and more discovery. (10:27) Imagine a line chart with a Y-axis that is "frustration" at the bottom to "delight" at the top. The X-axis is their user experience, taking place over time. As somebody uses your data product to do their job/task, you can plot their emotional journey. “Get the data, format the data, include the data in a tool, derive some conclusion, challenge the data, share it, make a decision” etc. As a product manager, you probably know what a use-case looks like. Your first job is to plot their existing experience trying/doing that use case with your data product. Where are they frustrated? Where are they delighted? Celebrate your peaks/delighters, and fall in love with the valleys where satisfaction work needs to be done. Connect the dots between these valleys and business value. Address the valleys—especially the ones that impede business value—and you’ll be on your way to “showing the value of your data product.” Analytics on your data product won’t tell you this information; the map is not the territory. (13:22) Analytics about your data product are lying to you. They give you the facts about the product, but not about the user. An example? “Time spent” doing a task. How long is too long? 5 minutes? 50? Analytics will tell you precisely how long it took. The problem is, it won’t tell you how long it FELT it took. And guess what? Your customers and users only care about how long it felt it took—vs. their expectation. Sure, at some point, analytics might eventually help—at scale—understand how your data product is doing—but first you have to understand how people FEEL about it. Only then will you know whether 5 minutes, or 50 minutes is telling you anything meaningful about what—if anything—needs to change. (16:17)

[External Ticketing]

Join us at the Vonage London Office as we celebrate International Women's Day with a roundtable discussion about the issues faced by women in the tech industry today. What can we do about it, and what should we expect from others?

Places are strictly limited, and a waiting list system will be in place.

Agenda

Hosted By

Amanda Cavallaro, GDG Organizer

I'm an Aikidoka, Developer Advocate, Software Developer, Google Developers Expert, Linkedin Learning Author and a Full Stack Web Development Specialist.

Saverio Terracciano, GDG Organizer

Stefano Le Pera, GDG Organizer

Lorenzo Turrino, GDG Organizer

Natalie Godec, GDG Organizer

Systems/DevOps/Cloud engineer and a resident GCP expert. Women TechMakers Ambassador, Google Developer Expert in Cloud and Champion Innovator. I photograph, drink tequila and collect designer handbags ✨

Bruno Ripa, GDG Organizer

I am an italian software architect, in the industry since 2006. I have been in entrepreneurship in Italy, for 6 years, and then continued my career in United Kingdom, in London (2012), a city (or, better, the City) which I consider as my second home. I have worked in several industries (gaming, fintech, digital asset management) and in many companies, with a 3 years parenthesis in Spain (2017-2020), precisely in Barcelona, where I have worked as a contractor for a few USA startups and an european company working in IoT. In March 2020 I made my way back in London, working for Erlang Solutions. I am actually an independent consultant, working in the IoT market,

Carol Pestitschek,

Working with design for more than 11 years, I’m a multidisciplinary designer specialized in UX and Product Design. Passionate about identifying and solving problems, I help companies develop projects that positively impact people's lives. When there are no problems, we know we can always improve.

Kubra Harmankaya, Android Developer

Partner

Vonage (https://developer.vonage.com/) Integrate SMS, voice, video, and two-factor authentication to your apps with Vonage communication APIs.

Complete your event RSVP here: https://gdg.community.dev/events/details/google-gdg-cloud-london-presents-accelerateaction-in-celebration-of-iwd-2025/.

After getting started in construction management, Anna Jacobson traded in the hard hat for the world of data products and operations at a VC company. Anna, who has a structural engineering undergrad and a masters in data science, is also a Founding Member of the Data Product Leadership Community (DPLC). However, her work with data products is more “accidental” and is just part of her responsibility at Operator Collective. Nonetheless, Anna had a lot to share about building data products, dashboards, and insights for users—including resistant ones! 

That resistance is precisely what I wanted to talk to her about in this episode: how does Anna get somebody to adopt a data product to which they may be apathetic, if not completely resistant?

At the end of the episode, Anna gives us a sneak peek at what she’s planning to talk about in our final 2024 live DPLC group discussion coming up on 12/18/2024.

We covered:

(1:17) Anna's background and how she got involved with data products (3:32) The ways Anna applied her experiences working in construction management to her current work with data products at a VC firm (5:32) Explaining one of the main data products she works on at Operator Collective (9:55) How Anna defines success for her data products (15:21) The process of designing data products for "non-believers" (21:08) How to think about "super users" and their feedback on a data product (27:11) How a company's cultural problems can be a blocker for product adoption (38:21) A preview of what you can expect from Anna's talk and live group discussion in the DPLC (40:24) Closing thoughts from Anna (42:54) Where you can find more from Anna

Quotes from Today’s Episode

“People working with data products are always thinking about how to [gain user adoption of their product]... I can’t think of a single one where [all users] were immediately on board. There’s a lot to unpack in what it takes to get non-believers on board, and it’s something that none of us ever get any training on. You just learn through experience, and it’s not something that most people took a class on in college. All of the social science around what we do gets really passed over for all the technical stuff. It takes thinking through and understanding where different [users] are coming from, and [understanding] that my perspective alone is not enough to make it happen.” - Anna Jacobson (16:00) ​​“If you only bring together the super users and don’t try to get feedback from the average user, you are missing the perspective of the person who isn’t passionate about the product. A non-believer is someone who is just over capacity. They may be very hard-working, they may be very smart, but they just don’t have the bandwidth for new things. That’s something that has to be overcome when you’re putting a new product into place.” - Anna Jacobson (22:35) “If a company can’t find budget to support [a data product], that’s a cultural decision. It’s not a financial decision. They find the money for the things that they care about. Solving the technology challenge is pretty easy, but you have to have a company that’s motivated to do that. If you want to implement something new, be it a data product or any change in an organization, identifying the cultural barriers and figuring out how to bring [people in an organization] on board is the crux of it. The money and the technology can be found.” - Anna Jacobson (27:58) “I think people are actually very bad at explaining what they want, and asking people what they want is not helpful. If you ask people what they want to do, then I think you have a shot at being able to build a product that does [what they want]. The executive sponsors typically have a very different perspective on what the product [should be] than the users do. If all of your information is getting filtered through the executive sponsor, you’re probably not getting the full picture” - Anna Jacobson (31:45) “You want to define what the opportunity is, the problem, the solution, and you want to talk about costs and benefits. You want to align [the data product] with corporate strategy, and those things are fairly easy to map out. But as you get down to the user, what they want to know is, ‘How is this going to make my life easier? How is this going to make [my job] faster? How is it going to result in better outcomes?’ They may have an interest in how it aligns with corporate strategy, but that’s not what’s going to motivate them. It’s really just easier, faster, better.” - Anna Jacobson (35:00)

Links Referenced LinkedIn: https://www.linkedin.com/in/anna-ching-jacobson/

DPLC (Data Product Leadership Community): https://designingforanalytics.com/community