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I made a controversial podcast appearance a few weeks ago and waited until now to post about it. I've even shared clips from it but never actually linked to the full episode until just now 🤦♂️ So here is my official plug to catch me on From A to B with Shiva Manjunath where I was initially trolled for my belief in "spaghetti testing", and within 35 minutes had turned the tables and steered us into a career growth b̶u̶t̶t̶k̶i̶c̶k̶i̶n̶g̶ coaching session. You can listen here: https://lnkd.in/gPYwUE6u Or if you're looking for something shorter, maybe this quick 5-minute appearance on The Lantern from MGMT Boston - if you've ever wondered why I continue to choose Boston as my homebase for my experimentation evangelism mission (and not, say, SF), I'll break it all down for you real quick: https://lnkd.in/g45wetrV And if you want the *full* briefing on Building Self-Serve Experimentation and Decreasing Cost-Per-Experiment, you've gotta invite me to give a 30min talk to your company. I'll do two of these free of charge for the first folks to get it on the calendar. DM me 😎

Just a small bit of news this morning: Eppo has been acquired by Datadog! You can read our official announcement in the blog post below, but I wanted to share a personal take on the news and why I'm thrilled: I've been running experiments for over a decade. Somewhere along the way, I decided that just running experiments wasn't enough for me - I wanted to focus on accelerating the overall adoption of experimentation in business. I first got the chance to do that as a consultant at Cro Metrics, working with 5-10 companies at a time and helping them build experimentation programs. I joined Eppo to scale my impact 10-100x. When I placed this bet, Eppo was the only warehouse-native experimentation tool on the market, and I saw a founding team equally dedicated to statistical rigor and widely-accessible UI. I knew this could enable end users at a scale far beyond what other products on the market were capable of, and I was right: today many Eppo customers successfully run thousands of experiments/year across dozens of teams in their organizations. And now it's time for another 100x leap, because now we can take the mission to 30,000+ existing Datadog customers. This is obviously not just a huge win for us, this is a big moment for the experimentation space too. It's the biggest bet yet from a wildly successful, publicly traded company who sees the exact same vision we do: a *lot* of growth and opportunity ahead for experimentation leaders, programs, and companies. There will be a lot more to come over the next few weeks but for now, suffice to say: our focus is still on building industry-leading experimentation capabilities, and it will the same Eppo team building the product for years to come. The acquisition also unlocks huge improvements in scaling feature flags and canary tests (necessary infra underlying all experimentation use cases) and some pretty exciting forward-looking capabilities for Agentic AI. New digs, new teammates, and a way bigger war chest - but the mission remains the same. Let's get everybody experimenting. https://lnkd.in/eX_Cs8jV

🥉🥈🥇 Toward Real-Time Medallion Architecture One of the biggest advantages of open table formats like Delta Lake and Apache Iceberg is that you can query the same data with multiple engines. No vendor lock-in, just pick the best tool for each job. Case in point: We’re working with a customer who built their Medallion architecture using Apache Spark and Delta Lake. But as their business evolved, so did user expectations—especially around latency. Spark’s batch processing couldn’t keep up with the demand for fresh insights. The solution? We replaced periodic Spark jobs with the Feldera Incremental View Maintenance (IVM) engine. Feldera picks up changes to lower-tier tables and updates higher-tier tables in real-time, reducing the end-to-end latency of the pipeline from hours to minutes. Prior to open table formats, in the world of legacy databases, such a migration would require completely re-architecting the entire data stack in order to use a new query engine. But thanks to the use of Delta Lake, the transition was completely seamless to the rest of the company: ✅ The data hasn't moved and can be read using Spark, Trino, etc., just like before the migration. ✅ Same ingestion pipeline from Kafka to the Bronze layer. ✅ Existing BI dashboards reading the Gold layer stayed untouched—only now they show near-real-time data! Want to see how it works? Join Gerd Zellweger's webinar on replacing batch analytics with Feldera: https://lnkd.in/eFDqXb5Q

🔄 Incrementally Incremental Computation Incremental View Maintenance has always felt a bit sci-fi to me: a record changes in a SQL table, and the system instantly ripples that update through hundreds of SQL operators, refreshing every affected view. But what if not just the data could change incrementally… but the queries themselves? Traditionally, modifying queries meant throwing away all prior computation and starting from scratch. Not anymore. Feldera now supports incremental query modifications. This is an incredibly powerful capability. You can make arbitrary changes to your program — add, delete, or modify views — even if other views depend on them. Feldera will: 🔍 Identify all affected views ♻️ Discard any state invalidated by the modification, while preserving all other state 🧮 Use existing state in the system to compute new and modified views. The result is that many pipeline reconfigurations can be performed in a fraction of the time it would take to backfill the pipeline from scratch. One of our customers described this as "incrementally incremental computation." What a nice way to summarize a complex feature in three words! Learn more here: https://lnkd.in/gNSZY2j8

Summertime makes me think of a few things: relaxing on the beach, catching some sweet waves, and client-side OpenTelemetry. What, just me? We're excited to host another fun panel discussion with key members of the Android, Kotlin, and Browser SIGs to cover the current state of OTel support for mobile and web. If your engineering team is investing heavily in OpenTelemetry and you want to bring that same standard into your frontend observability practices, then this panel is for you. Signup link: https://shorturl.at/NAkDz Date: Wednesday, July 22 at 10AM PT Our awesome panelists: Hanson Ho: Android architect at Embrace, OTel Android approver, OTel Kotlin approver Martin Kuba: Staff software engineer at Grafana Labs, OTel JavaScript SDK approver and OTel Browser SDK maintainer Jason Plumb: Senior software engineer at Splunk, OTel Android maintainer, OTel Java maintainer, OTel Kotlin maintainer Jared Freeze: Senior software engineer at Embrace, OTel Browser SDK maintainer We'd love to answer any questions you have about mobile and web support in OTel as well. Feel free to send them in when you sign up or share them in the comments below! #OpenTelemetry #OTel #CNCF #Observability #MobileObservability #BrowserObservability

I'll be joining a panel discussion on July 22 about the current state of client-side OpenTelemetry. Over the past year, the OpenTelemetry community has made significant progress in browser and mobile observability - from new semantic conventions and browser instrumentation to improvements in Android and Kotlin support. We'll discuss what's production-ready today, what challenges we're still working through, and where client-side OpenTelemetry is headed next. If you're interested in bringing OpenTelemetry to browser and mobile applications, I think you'll find the discussion useful. Sign-up link: https://shorturl.at/XDflC 🗓️ Date: Wednesday, July 22 at at 10AM PT Panelists: Hanson Ho: Android architect at Embrace, OTel Android approver, OTel Kotlin approver Martin Kuba: Staff software engineer at Grafana Labs, OTel JavaScript SDK approver and OTel Browser SDK maintainer Jason Plumb: Principal software engineer at Splunk, OTel Android maintainer, OTel Java maintainer, OTel Kotlin maintainer Jared Freeze: Senior software engineer at Embrace, OTel Browser SDK maintainer #OpenTelemetry #OTel #CNCF #Observability #MobileObservability #BrowserObservability

Re:Invent this year was my 9th, and it was as crazy as I've ever seen it. The pace of innovation and the mentality of every business around optimizing for AI upside while accepting manageable AI risk - it is energizing. The reality is that generative AI code/building is undeniably valuable, and today it is phenomenal at getting you a service (or code within an existing service) that is about 50% production ready. In order to be ready for primetime (scalability, reliability, observability, monitoring, remediation), AI Teammates are the missing piece. They are the future of running healthy production environments. ✅ Packed booth with 1,000+ great conversations with innovators ✅ Evening happy hour with engineers from all over the world ✅ AI Teammates talk on stage from Emrah Samdan ✅ Poker tournament with "back room" convos in their truest sense Top 3 Favorite Quotes of Amazon Web Services (AWS) Re:Invent 2025: "Wait - aren't you all the Telemetry Pipelines people? So you built a real-time data streaming solution, which is now what every real-time AI use case needs. That's pretty lucky..." - from another Founder & CEO (that sounded totes jelly), sometimes it's better to be lucky than good "Those glasses look horrible on your face" - from third place finisher of the poker tournament, who was tactically trying to convince the second place finisher to swap his Meta glasses prize. The creative jabs ultimately failed :) "Using an MCP tool to try to temporarily grab streaming data from any of the observability or security platform APIs has an extremely limited potential." - from a F100 technical fellow, it's really nice to hear validation of our approach #reinvent #observability #monitoring #otel Edge Delta Splunk Datadog Dynatrace Elastic New Relic Sumo Logic Grafana Labs

Lots of AI tools make big claims, but few deliver real, measurable impact for sales teams today. Owner just raised a huge Series C at a $1B valuation and is one of the fastest growing tech companies selling to SMBs. Kyle Norton their CRO took an early bet on DataLane and has seen huge benefits: ✅ DM connect rates increased by 2.5x ✅ Deal cycles shortened significantly ✅ Reps made 50% more calls when not tasked with contact research Decision maker contact data has been notoriously hard to find but AI has completely changed the game. There’s a lot of noise about AI SDRs and intent products, but if you’re selling to SMBs, getting in touch with decision makers directly will have by far the biggest impact. Want your outbound team to bring in $72K ARR CW per month? Check out datalane.com

GTM for the local economy requires a completely different playbook. Unlike traditional B2B GTM, selling into hospitality, home services, healthcare, and other local verticals has always lacked the data infrastructure to make TAM mapping, scoring, intent, and contact coverage actionable. With AI, that gap is closing. The best sales teams in local verticals are scaling faster than ever. Excited to be doing a deep dive into this topic on the GTM panel at Vertex: The Vertical Software Conference. Highly recommend you check out the event if you're in the Bay Area on Tuesday!

There’s so much AI and “AI agent” content out there lately that it’s hard to tell what’s actually useful anymore. That’s why I’m really looking forward to the Decodo × n8n webinar - it’s all about real and practical automations you can use in your daily marketing work. No buzzwords or future-selling, just effective workflows that saves your time and makes you a better marketer. See you there – https://lnkd.in/dfr-RnvZ

We've been working on something useful with our friends at n8n 🚀 In the upcoming webinar on December 4th, we'll show you how to build automated workflows for system monitoring, security tracking, and brand mentions using our very own Decodo node. You can easily learn more and later get 5000 free requests with our Scraping API product! Register here -> https://lnkd.in/dgt6CtXx

Most HubSpot CRMs I've seen are half-empty. Sales reps manually researching company size, headcount, industry. Guessing on ICP fit. Spending hours on accounts that were never going to close. So I built a workflow to fix it. 👇 Using the Coresignal n8n node, every new company created in HubSpot now gets automatically enriched with multi-source B2B data – no manual research, no stale records. Here's how it works: → A new company is created in HubSpot → A short delay ensures the record is ready → Coresignal fetches enriched company data in real time → HubSpot is updated automatically Took me 10 minutes to build. Saves the team hours every week. If your reps are doing this manually, they shouldn't be. Workflow 👉 https://lnkd.in/g-Za2x9B #CRMenrichment #salesautomation #B2Bdata #n8n #coresignal #hubspot

The Kafka agent isn’t the moat. The context and permissioning layer around it is. A capable model is useful, but it doesn’t operate in a vacuum. In production, the hard part is the surrounding system: what the agent can observe, what it can reason over, and what it’s allowed to change. In Kafka, that means more than seeing a lag spike. It means understanding schemas, lineage, ownership, ACLs, recent changes, and how services depend on each other. Without that context, the agent’s guessing. Without the right permissions, it’s a faster path to an outage. That’s where agentic Kafka operations gets interesting: not just better agents, but better context and control around them.

Some headlines make you pause. Meta's $250B data center in Louisiana was one of them for me this week. The most important name in that project isn't Zuckerberg. It's Blue Owl. An asset manager owns 80% of the financing behind the campus now. The signal? Wall Street is now structuring AI infrastructure the way it structures real estate. Think about what that means. When engineers own infrastructure, utilization is a dashboard metric. When financiers own it, utilization is a covenant. Meta is even reportedly exploring renting spare capacity to outside customers, because financed capacity is not allowed to sit idle. And this discipline won't stay at the top. Once the largest infrastructure projects on earth are financed against efficiency, that expectation reaches every company running its own data platforms. At Conduktor, we see the downstream version of this every day. Five years ago, nobody asked what their streaming infrastructure cost per team, per topic, or per use case. Today it's a board question, and storing data is just one step of a larger goal. Our customers are now constantly asked to prove their data is used, and that every part of the platform earns its cost. I believe this is a good thing. Accountability forces clarity, clarity forces focus, and focus is how great platforms get built. If Meta has to prove utilization to Blue Owl, your CFO will ask you to prove it too. The teams who can answer are the ones who will get to keep building. #meta #ai #datacenter

Every time a new company lands in your HubSpot CRM, someone has to go research it. Headcount, location, website, description... all manual, all repetitive, all avoidable. This n8n workflow fixes that automatically 👇 → Triggers on company creation → Waits for the record to be ready → Pulls the company domain → Enriches it with Coresignal B2B data → Writes everything back into HubSpot So by the time anyone opens the record, all the work is already done. Works well for sales, RevOps, and VC teams dealing with a constant flow of new companies. The template is free to use 👉 https://lnkd.in/g-Za2x9B #n8n #coresignal #hubspot #CRM #automation #salesops #B2Bdata

New data: Growth endurance in software is down to only 43%. This is a wake-up call for SaaS 👀 - Fewer than 1-in-5 startups maintained or improved their growth rate from 2024 to 2025. - A startup growing between 25-49% in 2024 had a 1-in-25 chance of accelerating to 100%+ growth in 2025. This data comes from SaaS metrics & growth platform ChartMogul, where I'm an Analyst-in-Residence. We used a dataset of 700+ private software companies that had at least $10k in MRR, had data going back to 2023 or earlier, and that grew at least 20% YoY in 2024. The 2025 growth endurance numbers are a wake-up call, and these numbers were notably worse than in 2024. But, against all odds, some startups seem to be aging in reverse. Sara Archer and I reached out to some of the the exceptional startups that inflected their growth. They did 3 things differently from everyone else: 1. Reset GTM velocity and increased the pace 2. Shifted from one-off marketing tactics to recurring campaigns 3. Took a big pricing and packaging swing I unpacked growth endurance & my favorite examples of anti-aging startups in today's Growth Unhinged newsletter. Read it here: https://lnkd.in/ergdidsQ --- THANK YOU to the startups who shared their growth stories: Alessandro Colombo (influencers.club), Adam Crandall (Benji Pays), Eric Dunn (Aesthetix CRM), Brandon Gell (Every Inc.), James Hinkson (AskElephant), Stacy Schmitz (Liveblocks) & Tanmay Ratnaparkhe (Predis.ai).

2025 was a year of near-endless channel experimentation. The average software company had 5 core GTM channels & another 5.5 GTM channel experiments. It feels like the volume of work keeps piling up just to achieve the same (or worse) results. New data from 700+ companies via ChartMogul shows that the median growth endurance in SaaS is now just 43%. Put differently, a software company that grew revenue 65% in 2024 (the median in the dataset) only grew 28% in 2025. (Top quartile companies see 80%+ growth endurance.) The startups who are winning say they’re getting more mileage out of their best channels by shifting from one-off tactics to recurring campaigns. The highest bang-for-your-buck places to double down right now (via the State of B2B GTM report w/ Maja Voje): Early-stage startups with <$1M ARR: 🥇 LinkedIn* 🥈 Warm outbound 🥉 Founder brand *Ideally all three of these compound: Share POV on LinkedIn > generate pipeline from followers > repurpose best hits off LinkedIn Growth stage co's with $1-10M ARR: 🥇 Warm outbound 🥈 LinkedIn 🥉 Intent-based outbound Scaleups with >$10M ARR: 🥇 Large conferences 🥈 SEO 🥉 Paid ads --- Read the growth inflection report w/ data + real examples from companies that bent the growth curve back up: https://lnkd.in/ergdidsQ

Tu ouvres trop la data aux métiers, c'est vite le chantier. Tu fermes trop, tu deviens le goulot d'étranglement et ton client interne n'est pas satisfait.... Bienvenue dans le quotidien de chaque DSI ! Le "self-service data", sur le papier c'est séduisant. En vrai, c'est un curseur à placer et plein de questions à régler: → Qu'est-ce qu'on laisse en autonomie ? → Qu'est-ce qu'on cadre côté IT ? → Comment on structure ça sans que ça devienne du shadow BI systématique ? On a posé ces questions à Matthieu Vermillard, Directeur IT qui gère ce sujet au quotidien dans un environnement industriel exigeant (aftermarket automobile — données produit, logistique, multi-sites). Ce qu'on va couvrir en 30 min : 🔹 Le déclic — Pourquoi ouvrir la data aux métiers ? 🔹 La réalité du terrain — Entre enthousiasme et prise de conscience 🔹 Les choix structurants — Produit, process et gouvernance 🔹 5 ans de recul — Ce qui a marché, ce qui reste à faire Format conversation. Pas de slides. Du concret entre pairs. 🗓️ Jeudi 16 avril — 11h | En ligne | 30 min 🔗 Lien d'inscription en commentaire Que vous soyez dans l'industrie, la distribution, les services ou le retail — si vous gérez la tension "accessibilité vs. maîtrise" côté data, c'est pour vous. ♻️ Un·e collègue DSI/CTO qui est sur le sujet ? Taguez-le.

En Aftermarket, la data c'est pas un sujet stratégique. C'est un sujet de survie opérationnelle. Références produit par dizaines de milliers. Catalogues à synchroniser. Stocks multi-dépôts. Équipes terrain qui veulent "juste un tableau de bord" pour piloter. Et au milieu de tout ça, l'IT qui doit arbitrer : → Qu'est-ce qu'on rend accessible directement aux métiers ? → Qu'est-ce qu'on garde sous contrôle pour éviter la catastrophe ? → Comment on fait ça avec des moyens de PME/ETI, pas de grand groupe ? Un Directeur IT du secteur a accepté de partager son retour d'expérience, sans filtre. Il vit exactement les mêmes contraintes que vous : 🔹 Des données produit complexes (TecDoc, équivalences, compatibilités) 🔹 Des équipes commerciales et logistiques qui veulent de l'autonomie 🔹 Un SI qu'il faut faire évoluer sans tout casser 🔹 La pression du "on veut nos chiffres en temps réel" 30 min de conversation franche : Comment il a structuré l'accès data pour ses équipes métier, où il a mis les garde-fous, ce qui a marché, et ce qu'il ne referait pas. 🗓️ 16 Avril — 11h | En ligne | 30 min -> 🔗 Lien d'inscription en commentaire Si vous êtes DSI, Resp. IT ou Data Manager dans la distribution de pièces, l'Aftermarket ou l'équipement auto, c'est un des rares webinars pensés vraiment pour votre réalité terrain. Partagez dans vos réseaux aftermarket - ça concerne tout le monde (IAM / FEDA / FIGIEFA)

🤯 $570 million in sales on TikTok Shop from Thanksgiving through Cyber Monday. WOW! Some quick hits from our report Charm.io: 📈 Cyber 5 sales on TikTok Shop were up 73% from 2024, showing how TikTok Shop continues to pull share from traditional marketplaces. 🏆 Top Shop: QVC led the way with its cross-category success. 🔥 Top Product: Medicube’s '8-piece glass glow skincare set' is one of the season’s most-talked-about beauty items. 💄 Top Category: Beauty & Personal Care… by a mile. The combination of creators + real-time discovery + frictionless checkout is changing how holiday demand plays out. And brands that leaned into the platform walked away with serious wins 💰 ! If you want to see which brands drove the most revenue, which products actually converted, which creators drove the most sales, and what this means heading into 2026… 👉 Grab our full TikTok Shop Cyber 5 Report. It’s all in there: https://lnkd.in/e7eSBjJi #TikTokShop #Cyber5 #CharmData #Ecommerce #HolidaySeason #BeautyTok #RetailTrends #CreatorEconomy #BlackFriday #CyberMonday

The marketing team vs. Reality Three weeks ago, I tried to add a policy with our business insurance provider, The Hartford. What followed: multiple calls, voicemails that weren't returned, reps who weren't authorized, and a process that still isn't done. This landed in my inbox from The Hartford this morning... turn AI into Time-Saver

Prices and demand in CPG move week to week. Approvals often still take a month. That gap is the whole commercial argument for Decision Intelligence, and unlike most AI business cases, you can actually put a number on it. The white paper breaks down what the delay costs across pricing and promotions, and what a realistic first year of fixing it looks like. If you own any of those decisions, our Decision Intelligence paper for CPG is worth the read: https://lnkd.in/eEd4wjbJ

McKinsey surveyed more than 1,200 managers about how their organizations decide. Fewer than half said decisions get made on time. That matches what I see in CPG delivery. The models are fine, the pipeline is fine, and the number on the dashboard is correct. What takes the time is working out what sits behind it, and then the recommendation waits for a planning meeting, a sign-off, or someone to reconcile it against a spreadsheet nobody else can read. We spend a large share of the first phase of Decision Intelligence projects on that stretch specifically. Who owns the decision, what constraints have to travel with the recommendation, and how far in advance it has to arrive to be useful. Clients rarely ask about it upfront. It tends to be where the weeks go. Our C&F paper on Decision Intelligence in CPG covers it in detail: https://lnkd.in/dCfEfTKh

There’s an app for that ➡️ there’s an agent for that. The AI agent market exploded over 5x from 300 companies to 1,700+ in 8 months. We mapped 400+ leaders to help teams navigate what to build, buy, or partner with. Three patterns jumped out: → Agents race to traction and early success. Agents are the fastest adopted new technology in history. The median AI agent startup is less than three years old. Already, one-third of companies have Mosaic scores – CB Insights' success probability measure – in the top 5% of ALL private companies globally. → Revenue leaders are being crowned faster than ever. The top 20 revenue-generating AI agent companies are 3.8 years old on average. The top of the leaderboard is dominated by software development agents already at scale with specialized workflows and measurable outcomes – a blueprint for other categories. → M&A is on the rise. Acquisitions of agentic startups jumped 3x between Q1’25 and Q3’25. The AI agent M&A landscape reveals enterprise software incumbents racing to embed agentic capabilities before they're disrupted by them. Salesforce and Workday's acquisition pace (4 and 3 deals respectively) signals these platforms recognize AI agents as existential – either they own the agentic layer or risk being disintermediated by it. Acquisitions from AI infrastructure players see agents as the killer app that drives compute consumption. Agentic startups are also serving as external R&D labs for many enterprise leaders. Incumbents know they can't build this fast enough, so they're buying their way into agent capabilities. 60+ acquirers across security, customer experience, automation, and vertical SaaS shows agent capabilities becoming table stakes across enterprise software, not just a feature for the platform giants. This fragmented buyer landscape means AI agent startups have multiple exit paths, but also face a land grab where every software vendor is hunting for agent technology to avoid obsolescence. The firms moving fastest on AI agents aren't starting from scratch. They're looking at which categories already have proven revenue models, then deciding whether to build, acquire, or partner based on how mature the solution is. Which of the 1,700+ agents will accelerate your business? https://lnkd.in/e-CRzbWa