B2B ad examples

🌐 Catena-X promotes sovereign data exchange throughout the entire automotive industry value chain, from the smallest raw material supplier to the largest original equipment manufacturer (OEM). However, getting started can seem complex, especially for smaller companies. The Data Space Accelerator, a publicly funded study, lowers these barriers to entry. Participating companies receive financial compensation for their first steps in the data space. Depending on the selected use case, companies can receive a lump sum of €15,000 or €30,000 to cover all the initial costs required to launch productive data exchange in Catena-X. 🏛️ The “Data Space Accelerator” is a project funded with 23 million euros by the German Federal Ministry for Economic Affairs and Energy. The project will run exclusively in 2026. Eligible use cases include certificate management, product carbon footprint, digital battery pass, and more. Learn more and apply 🔗 https://lnkd.in/eRz6K2rv

Are you thinking about joining Catena-X? Up to €30,000 in funding is now available for this purpose. Many companies in the automotive industry face the same challenge: sharing data with business partners securely, efficiently, and in a standardized way. Catena-X lays the foundation for secure data exchange along the value chain. Companies decide for themselves which data to share with which partners, while retaining control over their information at all times. In 2026, a government-funded study will support companies in getting started: 💶 €15,000 or €30,000 in funding, depending on the use case ✅ Support with initial setup and implementation costs ✅ Particularly interesting for companies looking to implement their first Catena-X use cases Possible areas of application include Product Carbon Footprint (PCF), certificate management, capacity management, and the Digital Battery Passport. 🏛️ The Data Space Accelerator is funded by the Federal Ministry for Economic Affairs and Energy and has a total funding volume of 23 million euros. Funding is available exclusively in 2026. If you’re planning to get started with Catena-X, now is a good time to take the next step. 🔗 Learn more and apply: https://lnkd.in/eRz6K2rv

Quais riscos sua instituição está assumindo por falta de informação? Uma visão incompleta do cliente pode comprometer a análise e levar à aprovação de operações mais arriscadas do que parecem. A Datahub fornece dados sobre perfil, comportamento e risco para ajudar sua equipe a avaliar clientes com mais critério e apoiar decisões financeiras mais seguras. Clique em Saiba mais e conheça as soluções da Datahub para sua instituição.

Para instituições de pagamento, crescer sem qualificar dados não é escala. É risco operacional. Cada nova relação comercial depende de informações confiáveis. Quando os dados estão incompletos ou inconsistentes, aumentam o retrabalho, os atrasos no onboarding e a insegurança nas decisões. A DataHub apoia o ecossistema de pagamentos com soluções de: • KYC • Validação cadastral • Enriquecimento de dados • Processamento em escala Qualifique sua base antes que o aumento do volume também amplifique os problemas. Conheça as APIs da DataHub.

Doing nothing costs money, but spending money makes more money. Such is the paradox of in-app analytics. But that's only the first paradox. There are two, and both show up for just about every product engineering team that starts looking into an in-app analytics module. If you work in B2B SaaS and your customers ask for dashboards, you know the first one well, and you're almost certainly living the second. The plot is as predictable as Campbell's hero's journey: 1/ You're a small team building B2B SaaS. Fewer than 100 people, serving, let's say, around 1,500 customers, SMBs and enterprises both. 2/ Your Customer Success team starts getting the same kind of request over and over: data. At first you point them to the export button. 3/ Then they ask whether they can slice and dice it. Filter the numbers down to one product, compare them across time periods. 4/ Now Customer Success is buried in data requests they aren't equipped to handle. They take it to product engineering, who have their own fires to put out. 5/ Product weighs buy against build and, like any proud product engineering team, decides it can't be that hard to build. 6/ It turns out it isn't too hard, but it just takes A LOT of time to get it right (not looking like a random dashboard bolted onto your UI, row-level permissions, cross-filter and drills), time you could have spent on the core features of your product. 7/ So it drags on, neither here nor there. You ship a half-baked analytics module while the rest of your roadmap gathers cobwebs, and customers start threatening to leave for your competitors. The money is on the table, and it should have been collected much much sooner. 8/ Customer Success, meanwhile, is stuck between two teams: passing analytics requests to product, and still bugging engineering or the data team for one-off dashboards. Which leads us to the second paradox. It's this: if you want to know how well your in-app analytics is performing, you don't just ask product or data. You ask Customer Success too. Because when the analytics is doing its job, a whole category of message goes missing: the feature requests, the "can you pull this for me" asks, the support threads that used to stack up high. They're simply gone. What replaces them is a far easier ask, and a better one: “more data, please”. And, “Where do I upgrade to the premium plan with the analytics?” That's how it went for our customers. It can go that way for you too, with Holistics.

(This is how data teams are buying BI tools: see the screenshots) Half the data teams we spoke with over the last three weeks asked about the same set of questions. The wording varies, but it's always some version of: 1/ Do you have a semantic layer? 2/ How expressive is your semantic layer? 3/ Do you have a code-first development flow? And in most cases, our answer is the same: Yes. Let me show you. Yes. And they're skeptical of that answer, as any good data practitioner should be. They know all too well how B2B vendors over-promise and under-deliver. They know how easy it is to bolt a natural language interface onto legacy BI and call it AI analytics. Stuck between hearing the same AI pitch for the hundredth time and fielding the hundredth Slack DM asking for a quick metric pull, they've gotten good at filtering out options fast. So they ask us what they need to ask. They ask about the semantic layer to ground AI in shared metric definitions. They ask about a code-based development flow so they can govern and version-control it, the way they would in software engineering. And they ask about expressiveness because they want one that can also handle the harder questions, like nested aggregation or period-over-period analysis, when most semantic layers handle the OLAP basics (lookup, slice-and-dice, filter/group, top-N). They already know that if the layer can't express a question natively, AI either falls back to raw SQL (bringing back hallucination risk) or just returns nothing. They ask what they need to ask. And these are good, important questions, because without them, they'd never find and try out Holistics, and they'd never tell us what they told us: "Holistics AI is surprisingly good, even when we hadn't done much work contextualizing our data." -- P.S. Like any good data practitioner, you should be skeptical of this post too. But if you want to see how we win over even the biggest skeptics, book a demo with us.

Your data estate runs different access models across platforms. That gap shows up at audit time when no one can fully explain it.

OpenAI and Anthropic have mapped out safer agents. The next step is deciding what each agent can access. That’s the Data Authorization Layer.

Coupler.io gets every ad account into BigQuery in the time it takes to make coffee. Connect the accounts, pick the columns, set the refresh. Done ✅ Google, Meta, TikTok, LinkedIn — joined into one table, on the schedule you set. No SQL. Start free — 7 days, no card.

Fuusor on rakennettu kattamaan vaativankin talousraportoinnin tarpeet. Fuusor kerää, yhdistelee ja jalostaa liiketoimintatiedon eri lähteistä yhdelle alustalle – täysin automatisoidusti ja haluamassasi muodossa. Tietoa pystytään myös rajaamaan automaattisesti eri käyttäjien ja tarpeiden mukaan. Visuaalinen palvelu kiteyttää datamassan selkeiksi näkymiksi, raporteiksi ja mittareiksi, joiden avulla pystyt tekemään parempia päätöksiä ja oivalluksia. Tutustu ja varaa demo jo tänään!

Manually moving data from one system to another to keep track of what's going on? 🏗 Try a better way of managing construction projects. No painful migration, no new software – your existing systems, but synced and cut to the chase. See a prototype with your real data in just 48 hours, free of charge. Request now.

Fuusor on rakennettu kattamaan vaativankin talousraportoinnin tarpeet. Fuusor kerää, yhdistelee ja jalostaa liiketoimintatiedon eri lähteistä yhdelle alustalle – täysin automatisoidusti ja haluamassasi muodossa. Tietoa pystytään myös rajaamaan automaattisesti eri käyttäjien ja tarpeiden mukaan. Visuaalinen palvelu kiteyttää datamassan selkeiksi näkymiksi, raporteiksi ja mittareiksi, joiden avulla pystyt tekemään parempia päätöksiä ja oivalluksia. Tutustu ja varaa demo!

300 Beteiligungen. Zwei getrennte Systeme. Ein Single Point of Truth. So hat die Raiffeisen-Landesbank Steiermark ihr Beteiligungsmanagement mit dem Legal Entity Manager von Goldright neu aufgestellt – mit konsistenten, tagesaktuellen Stammdaten und Reporting auf Knopfdruck. Sehen Sie in der Success Story, wie.

Your AI agents just showed up to the party... and they brought thousands of friends. Every agentic workload wants its own database. Multiply that across your whole fleet of agents, and suddenly your infra team is drowning. Enter Cockroach Continuum: built to create, isolate, and run many databases without your infrastructure (or your DBAs) breaking a sweat. Here's how: -> Disaggregated storage scales compute and storage independently. -> Virtual Clusters consolidate isolated databases on shared private hosts. -> Aegis reduces estate-management overhead. We break down Cockroach Continuum's full architecture here: https://lnkd.in/evMdkt_a

Dear partners, let's show your best work. The first Inphinity Partner Awards are here. More than 90 partners now make up our community. Behind that number are inspiring people building projects that help customers turn data and Qlik into action. We see your work and we’re proud to work with you. These awards celebrate what you’ve achieved: 🏆 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 𝗼𝗳 𝘁𝗵𝗲 𝗬𝗲𝗮𝗿 overall business performance 🏆 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗬𝗲𝗮𝗿 customer transformation and innovation 🏆 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗼𝗳 𝘁𝗵𝗲 𝗬𝗲𝗮𝗿 strongest business growth We’ll promote every winner and show what makes them and their work stand out. But Impact of the Year needs your help. Think of the customer project you’re most proud of. Did it make a clear difference for your customer? Did you build an innovative solution with Inphinity? Share your solution with us and be the first Impact of the Year winner. Learn more about the awards and how to submit your project: https://lnkd.in/d3s8eKDh

Vor Kurzem habe ich mit einem Interessenten unseren ROI Calculator live im Call ausgefüllt. Zehn Minuten, einige Zahlen – und ein sehr stiller Moment auf der anderen Seite. Es ging um eine ziemlich normale Ausgangslage: mehrere hunderttausend Stammdatensätze, sieben Quellsysteme, ein Team, das laut eigener Schätzung „irgendwie“ viel Zeit mit Datenpflege verbringt. Wir haben gemeinsam die Zahlen eingetragen: - Anzahl der Datensätze, - Anzahl der Systeme, die integriert werden sollen, - geschätzter derzeitiger Zeitaufwand pro Woche für manuelle Datenerfassung und Fehlerbehebung, - Kosten für externe Berater, die immer wieder dieselben Aufräumarbeiten übernehmen, usw. Am Ende standen unter anderem vier wichtige Werte auf dem Bildschirm: - der Zeitpunkt, ab dem sich die Investition rechnet, - der ROI über drei Jahre, - die Kosten, die über drei Jahre entstehen, wenn sich nichts ändert, - und der Netto-Vorteil pro investiertem Euro. Keine der vier Zahlen war eine Schätzung von uns. Es waren seine eigenen Zahlen – nur endlich einmal zusammengerechnet, statt über IT-Budget, Fachbereich und Beratungskosten verteilt zu bleiben. Genau das ist für mich der eigentliche Wert der Berechnung mit Hilfe unseres ROI Calculators: nicht ein weiteres Argument für MDM, sondern die Rechnung, die intern sonst niemand aufmacht. Wer diese Rechnung für die eigene Systemlandschaft sehen will: DM oder Kommentar mit „ROI“ und ich schick den Link direkt zum selbst ausprobieren durch.

Apache Spark 4.0 gave you the VARIANT type. It will not make your queries faster. Shredding will. Every post about it says the same three things: no schema, no from_json, 8x faster. Teams are already planning to move their JSON columns over. VARIANT is stored as one binary blob inside Parquet. Parquet is fast for one reason. Every column keeps min and max values per row group, and a filter uses them to skip most of the file without opening it. A blob has no stats for the fields inside it, so your filter skips nothing. Every block gets read, then decoded. You stop paying to parse. You start paying to read. So what is shredding? At write time, Spark pulls chosen paths out of the blob and writes each one as its own typed column, next to the blob. user.id becomes a real BIGINT column with min and max stats. Whatever you did not choose stays in the binary leftover. That is how skipping comes back. The decision: ✅ Still on STRING plus from_json? Move to VARIANT. Clear win. ✅ Already have typed columns in your gold tables? Leave them alone. ✅ Putting VARIANT where people run queries? Shred it, then verify. If the value column still holds binary data for that field, it never got shredded and you are scanning blobs. VARIANT did not delete the cost of JSON. Shredding moves it out of your query path. #ApacheSpark #DataEngineering #Lakehouse

Pulling data, building dashboards, updating them by hand — it adds up. Get an always-fresh, accurate data layer you can build reports from and ask AI questions about.

Pokud řešíte, jak z dat vytěžit zlato pro byznys, potkejme se 15. října v Praze na Better Data Evening!🤖 Letos jsme připravili program postavený hlavně na zkušenostech z praxe. Potkáte se s našimi experty i datovými lídry z firem, kde řeší data, plánování a AI v každodenním provozu. Dozvíte se například jak: - jak Národná banka Slovenska staví Open Data platformu - jak PPA Controll využívá AI chatbot nad interními daty a dokumentací - proč AI nad firemními daty někdy selhává a co s tím - jak přední světová antivirová značka dál rozvíjí Business Planning - jak v RS Group probíhá přestavba data foundation za plného provozu Po odborném programu budeme mít prostor pro otevřenou diskusi a networking během společné večeře. 📅 15. 10. 2026 📍 OREA Hotel Angelo, Praha Akce je určena pro: CEOs, CFOs, COOs, CIOs, CDOs, Head of Data, BI a další business, finance, planning a operations leadery. Budu rád, když se na místě potkáme! ☺️ Registrace je zdarma: https://lnkd.in/dNKPxRsh

Máte data. Máte AI. Proč stále neděláte lepší rozhodnutí? To je otázka, kterou otevřeme na konferenci Better Data Evening, 15. října v Praze. Čekají vás konkrétní projekty z praxe, pohledy lidí z vedení firem i diskuse o tom, kde technologie pomáhá a kde samotná technologie nestačí. A nebudeme zůstávat jen u diskuse. Ukážeme čtyři konkrétní projekty z praxe: 👉 Národní banka Slovenska – připravovaná otevřená datová platforma 👉 PPA Controll – AI chatbot v Microsoft Teams nad interními daty 👉 přední světová antivirová značka – další rozvoj plánování a jeho role v řízení firmy 👉 Sellier & Bellot – propojení obchodního a finančního plánování Kromě panelové diskuse Better Decisions tak uvidíte, jak se data, plánování a AI řeší v různých typech firem a situacích. Better Data Evening 15. 10. 2026 | OREA Hotel Angelo, Praha Pokud řešíte data, plánování, AI nebo digitální transformaci, potkejme se v Praze. Podívejte se na celý program 👉 https://lnkd.in/dZ_c2RMr

𝐌𝐞𝐫𝐜𝐡𝐚𝐧𝐭 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐈𝐒𝐕𝐬 𝐬𝐜𝐚𝐥𝐞𝐬. 𝐅𝐢𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐈𝐒𝐕𝐬 𝐝𝐨𝐞𝐬𝐧'𝐭. Company classifications describe industries, not software markets, so vertical software vendors are hidden in the industries they serve. 𝐈𝐒𝐕𝐖𝐨𝐫𝐥𝐝 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐅𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬™ adds applications, technologies, verticals and ecosystems. 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 →

𝐆𝐞𝐧𝐞𝐫𝐢𝐜 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐝𝐚𝐭𝐚 𝐜𝐚𝐧'𝐭 𝐭𝐞𝐥𝐥 𝐲𝐨𝐮 𝐰𝐡𝐢𝐜𝐡 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐛𝐮𝐢𝐥𝐝 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞, 𝐨𝐫 𝐰𝐡𝐚𝐭 𝐤𝐢𝐧𝐝. So ISV payment partner lists end up missing relevant vendors. 𝐈𝐒𝐕𝐖𝐨𝐫𝐥𝐝 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐅𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬™ classifies vendors by what they build: vertical, function, technology stack. 𝐒𝐞𝐞 𝐰𝐡𝐢𝐜𝐡 𝐈𝐒𝐕𝐬 𝐚𝐫𝐞 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐭𝐚𝐫𝐠𝐞𝐭 𝐯𝐞𝐫𝐭𝐢𝐜𝐚𝐥𝐬 →

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Discover where AI can deliver real value and how to scale it effectively. The ICIS Executive Exchange: The Future of AI in Chemicals is an exclusive forum designed for senior decision-makers looking to explore AI's role in commercial performance, operational efficiency, data strategy, and business transformation. Engage in confidential discussions, hear real-world perspectives, and gain practical insight into the opportunities and challenges shaping AI adoption across the chemicals sector.