B2B ad examples

Access all of your observability data for 12 month. Short retention windows lead to blind spots, but teams are forced to accept it to keep costs down. With Bronto, all of your data is retained for 12 months by default. No extra costs or rehydration – just all of your data instantly searchable.

Investigations rely on evidence. If you're sampling down your observability data, you could be throwing away important clues. If your logs are only searchable for a few days, you lose visibility into performance trends over time or recurring issues. With Bronto, you can keep all of your logs, traces, and metrics in full-fidelity for 12 months, retained always hot and instantly searchable. Solve every case with confidence.

Only optimizing the top 10% of your Amazon listings? ⚠️You’re likely losing revenue — every month (without even realizing it). Reason? Unoptimized ASINs drag down your visibility, conversions, and ROI across your entire catalog. Now your team can manage the other 90%, and do it 80% faster! 👉 Meet Amazon Portfolio Management: AI, powerful reporting & dedicated support in one.

The VertiGIS Annual Breakfast Seminar is touring six cities this October. Hear the latest from FME, see where VertiGIS solutions is heading, and hear how organisations like Acciona, Western Power and Decca are putting their spatial data to work.

Hear from industry experts and customers, discover how organisations are using data and automation to drive better outcomes, and get the latest updates from the FME ecosystem. Join us in Adelaide or Perth.

Most AI attempts stall on real data, not the model. In three weeks we run one real use case on your own past work, measure it, and give you a build, defer or stop for the board.

Copilot, ChatGPT, an agent your team built. Nobody can tell the board how good it is. We measure one use case against your own past wins and losses, inside your own Microsoft 365, in three weeks.

Have you ever sat through same boring eCommerce demo? I know I have. I've given it a billion times and was ready for something new. But how was I going to change that same old demo? I would have to build a fake demo dataset, transform that data in dbt Labs, model and create content in Omni, and build a fully functional embedded web application. My skill set only covers one of those four. This is where Cursor came in. Using Cursor I got to moonlight as a data engineer, analytics engineer and a software engineer in the same IDE. Check out my blog below walking you through how I used Cursor, dbt and Omni to build a fully functional AI powered data application for a fictional SaaS company, Data Blobby, that our SE teams could demo to prospects. Omni is excited to be partnering with SpaceX Super Intelligence in their Partner Network launch! See how he built it 👉 https://lnkd.in/g-4zSkf7

More output isn't more impact. Amplitude ADVANTAGE Berlin: Wed 2 Dec, 14:00–19:00 CET, The Delta Campus. Request your spot.

Your AI strategy is only as strong as your data foundation. Assess gaps, strengthen governance, and unlock scalable enterprise AI.

AI investment is booming. Enterprise expectations are outpacing delivery. See what 300 ISVs are doing to close the gap.

Tableau Visionary Lisa Trescott shares her biggest Tableau takeaways—from tracking the "why" behind higher-ed metrics to vibe-coded apps for admins by Tableau Ambassador Eric Summers. Learn more: tabsoft.co/4i9NYkI

Time-series data, machine events, JSON, and operational data do not have to live in separate systems. CrateDB brings different data types together in one SQL engine.

Turn production and sensor data into applications your teams can actually use. Build real-time quality analytics without adding another data silo.

We’re #1! 🎉 Axiobench named Import.io Best Overall for Data Extract Software in its 2026 ranking. Thank you to our team and customers! https://lnkd.in/eV4tgdBK

𝗖𝗼𝗺𝗼 𝗮𝘂𝗺𝗲𝗻𝘁𝗮𝗿 𝗮 𝗽𝗿𝗼𝗱𝘂𝘁𝗶𝘃𝗶𝗱𝗮𝗱𝗲 𝗲 𝗿𝗲𝗳𝗼𝗿𝗰̧𝗮𝗿 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗶𝗱𝗮𝗱𝗲 𝗱𝗮𝘀 𝗲𝗺𝗽𝗿𝗲𝘀𝗮𝘀 𝗽𝗼𝗿𝘁𝘂𝗴𝘂𝗲𝘀𝗮𝘀? Junte-se à Informa D&B e ao Jornal de Negócios para uma manhã de partilha de conhecimento, apresentação de indicadores e debate sobre um dos temas mais relevantes para o crescimento da economia nacional. 🔗 Saiba tudo: https://lnkd.in/e59MbwbY

Combien d'animaux vivent dans votre code postal ? Le Panorama des animaux de compagnie en France permet d'explorer leur présence à l'échelle locale et de comparer les territoires grâce à des données inédites. Collectivités, professionnels du secteur animalier, assureurs, vétérinaires : découvrez les indicateurs clés de votre territoire. Consultez gratuitement l'étude.

Combien d'animaux vivent dans votre code postal ? Le Panorama des animaux de compagnie en France permet d'explorer leur présence à l'échelle locale et de comparer les territoires grâce à des données inédites. Collectivités, professionnels du secteur animalier, assureurs, vétérinaires : découvrez les indicateurs clés de votre territoire. Consultez gratuitement l'étude.

We helped fix Exa’s code. It was time for them to return the favor.

Honeycomb has always been about teams. 🐝 From day one, we’ve believed that strong teams are built when a group of people with different roles, instincts, and expertise can get to a shared understanding of what’s happening around them. (That belief is rooted in the delta between me and Charity: two very different engineers, trying to make sense of the same messy system.) The agentic era changes who’s on the team, but to me it’s another variation on the same theme. Everyone has their own agent these days (and should!)—and that raises a fun set of questions about how all the pieces fit together: how humans and agents with different views of the world and different expertise collaborate, hand work off, and pass along what they’ve learned... while staying grounded in the same view of what’s real. Back in the early days, my LinkedIn bio described Honeycomb as “the next best member of your team.” I still think about it that way... there are just a lot more teammates in the picture now. Charity and I are digging into all of this Nov 3–4, and we’ll be announcing some very cool stuff along the way ✨ Day 1: Agents on your team. Instrumentation, context, and building trust in what your agents are doing. Day 2: The AI ecosystem at scale. Visibility across the whole fleet, keeping costs in check, and what platform teams need to observe all of it. There’s a live Q&A, so bring your questions (or your "well, actually"s). Join us! https://go.hny.co/4dsMocn

A modern vehicle assembly line generates more telemetry before lunch than most historians were built to hold, and each new torque tool or weld sensor adds another per-tag line item. Automotive manufacturers are keeping the historian for SCADA and regulatory data, and sending new telemetry to InfluxDB instead. In our latest piece, we cover the two patterns they use: a parallel feed for new instrumentation, and consolidation across plants that each run their own historian. Read the article to walk through how each one works. ⤵️

What should an agent do at a venture fund, and what should stay with the investor? Max Ruderman, Keshvi Radia, Dr. Andre Retterath and Yohei Nakajima have opinions. Save your spot: https://luma.com/0b5yswul

I just pulled every long-tenured OpenAI engineer (name, title, start date and LinkedIn). This is where the startup mafias come from. Two reasons why: 1) I imagine that most of these engineers will leave soon to go back to 0 to 1 building 2) They are likely fully vested so they do not have to stay at OpenAI to receive their huge payouts I got the below list using Harmonic’s research tool. I then put this into an active list so that I could get more enriched data on each person that updates automatically (it updates once a week with new details from Linkedin). This info is tablestakes in today's venture game. In case you want to check it out yourself: www.harmonic.ai/try cc: Harmonic #harmonicpartner #venturecapital #sourcing #startup

Is manual data reconciliation slowing down your insurance operations? Property & Casualty (P&C) insurers rely on accurate, consistent data across financial systems, production environments, and business applications. But as data ecosystems grow, validating information across hundreds of interfaces and pipelines can become complex, time-consuming, and resource-intensive. For one U.S. property insurer, managing 600–800 data interfaces and pipelines created a significant need to streamline financial and production data reconciliation. The solution? Automated data validation and reconciliation with Datagaps. By automating key testing and reconciliation workflows, the insurer achieved measurable improvements: ✅ 90% reduction in test execution time — accelerate validation cycles and reduce manual effort. ✅ 2X expansion in line-of-business coverage — extend testing coverage across more business areas. ✅ 50% reduction in testing team size — optimize resources while supporting broader testing needs. These results demonstrate how automation can help insurers modernize data quality processes, improve operational efficiency, and build greater confidence in business-critical data. Could your insurance organization benefit from automated data reconciliation? Discover how datagaps helped this U.S. property insurer streamline financial and production data reconciliation—and explore what a similar approach could mean for your organization.