B2B ad examples

Wie wird aus KHTF-Förderung nachhaltige Digitalisierung? Nicht mit Einzelprojekten, sondern mit einem digitalen Fundament, das mitwächst.

Your virtualization strategy will change. Your storage shouldn’t be a blocker. Evolve with a data storage platform built for what’s next.

Everpure a été classé 2 fois parmi les leaders du Gartner® Magic Quadrant™ 2026 pour les plateformes de stockage d’entreprise. Explorez le rapport.

Transform your enterprise data into a single source of truth with OpenDQ Matrix360. Our AI-powered platform integrates Data Quality, Master Data Management, and Knowledge Graphs to deliver actionable insights that drive smarter decisions. Experience unparalleled accuracy and trust in your data while enhancing operational efficiency—without the burden of licensing costs. A comparable Data Quality + MDM + Knowledge Graph + AI solution from legacy vendors typically costs $50K–$1 million dollars in annual licensing alone. OpenDQ Matrix360 delivers enterprise‑grade capabilities with zero licensing fees and no scale penalties. You get 100% data validation, 99.9% data de‑duplication, and hyper-scalable trusted data at a fraction of the cost. Take a look at what we have to offer! Demo today!

A great product can't outrun a broken onboarding flow. Watch exactly how new signups interact with your software, and eliminate the UI hurdles standing between them and their "aha" moment.

Discover the power of OpenDQ Matrix360 by Infosolve Technologies, Inc. This AI-driven platform unifies Data Quality, Master Data Management, and Knowledge Graphs to create a single source of truth for your organization. Deliver trusted data across the enterprise without the constraints of traditional licensing. If your organization needs clean, governed, and reliable data, you need a unified, advanced technology stack that can scale. OpenDQ Matrix360 delivers exactly that — on a zero‑license‑cost platform. Build trust in data with outcomes backed by measurable results: 100% Data Validation across all sources 100% Data Corrected using deterministic, audit‑ready rules 99.9% De‑Duplicated Records with AI powered identity resolution Scale from millions to billions of records on a highly advanced, unified platform built on the Trust Triad: Data Quality + Master Data Management + Knowledge Graph Register for a demo or download the software today.

Weg von Excel? Aus IT-Sicht heißt das: weg von verstreuten Dateien und manuellem Kopieren, hin zu einer Planung, die in Fabric entsteht und dort bleibt, mit Audit und Status je Planungsphase. Wie das aufgebaut ist, zeigt der Leitfaden.

Noch mehr Berichte? Fabric kann mehr! Auf OneLake und der Medaillon-Architektur lassen sich Planung, Simulation und KI-gestützte Zweitmeinung aufsetzen, nativ in Fabric und ohne Add-ons. Was dafür nötig ist, zeigt unser Leitfaden.

Enterprise AI is moving fast. Trust isn’t keeping up. On October 6, we’re bringing together AI, data and technology leaders in New York City for the Collibra AI Summit to talk about what it actually takes to close that gap. How do you give AI the business context it needs to make better decisions? How do you maintain control as agents become more autonomous? And how do you move from promising pilots to AI you can confidently put into production? We’ll hear directly from leaders working through these questions today and share what we’re learning along the way. If you’re responsible for making AI work in the enterprise, I hope you’ll join us. October 6 | New York City Register: https://lnkd.in/e5WsANn5 #EnterpriseAI #AIGovernance #Collibra

Nothing cements your status as a data analyst quite like staring blankly at a screen during a client call while someone asks, "Hey, where did you get this number?" Queue the awkward silence as nobody on your team knows. AI made it up, you trusted it, and now you're breaking into a cold sweat. 😱 At about this moment your client's trust flew out the window. 💔 AI reporting demos look like magic. But AI gets raw tables with no context: it doesn't know which metrics the client cares about, no definition of a "lead," no idea which campaigns are dead. So it fills the gaps with confident fiction. On October 22, Coupler.io's Olexander Paladiy and Veronika Tamaio Flores are running a free session on how to fix that: •Where AI reporting stands: what changed this year, and where it still fails multi-client agencies. •AI Business Context: the layer between raw data and an answer you can defend, and why most setups skip it. •Real agency examples: how Coupler.io clients rebuilt their reporting so AI works with reliable data and context. It's built for agency owners signing off on numbers they can't verify, analytics teams rebuilding the same dashboards every month, and RevOps or marketing ops teams standardizing reporting across clients. Register for the free session here: https://lnkd.in/dRzY5NDX

Data foundation rebuild with the lights on. Hear RS Group’s story. Miriam Vizvary, Group Chief Data & Analytics Officer, will share the challenges of modernising data foundations while keeping the business running. 𝐁𝐞𝐭𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐁𝐫𝐚𝐭𝐢𝐬𝐥𝐚𝐯𝐚 · 𝟐𝟖 𝐎𝐜𝐭𝐨𝐛𝐞𝐫 Explore the programme 👉 https://lnkd.in/d7uCZWBW Free attendance, subject to approval.

Krijg direct inzicht in het risicoprofiel van je cliënt. Neem sneller onderbouwde beslissingen op basis van actuele en betrouwbare data.

Bespaar uren per cliënt. Automatiseer je cliëntenonderzoek en ga van handmatig werk naar een gestroomlijnd proces in 5 minuten.

What do Qlik users do after reading a dashboard? Our use-case analysis answers that ↓ We serve 400 customers, and we have mapped 105 use cases. 63.0% in operational data and reporting. 60.9% in planning and forecasting. 32.6% in data management and governance. 10.9% in workflows and approvals. Many customers use Inphinity in more than 1 domain. Here's the important distinction: Writeback describes the movement of data. It does not describe the form people use, the validation rules, the calculations, the permissions, the audit history or the actions that follow. Put those pieces together inside Qlik and you have a business application. A Qlik-native application layer built around governed data interaction. So the question has shifted from: “Do you need writeback?” To: “What business process still leaves Qlik for a spreadsheet, email, or disconnected legacy application?”

Did you know that DDW offers Individual Cow Data Marts, or ICDMs? Dairy data covers many different types of animal events. When everything sits in one large dataset, accessing only the information needed for a specific application can become more complex. ICDMs organize individual-cow data into functional domains such as milking, reproduction, movement or feeding. Through API access, customers can work with the relevant animal-event data in their own solutions. This creates a clearer route from standardized cow-level data to monitoring, analytics, and customer applications. Not every use case needs the entire data warehouse, sometimes you only need specific data.

Co musí fungovat, aby se firma skutečně rozhodovala na základě dat? Na Better Data Evening budu moderovat panelovou diskusi, kde se na toto téma podíváme ze čtyř různých perspektiv. V panelu se potkají: Tomáš Hubík (Chief Information Officer, Foxconn Česká republika), Ondrej B. (Chief Digital Officer, SCONTO Nábytek Česká republika), Martin Krc (Head of Statistical Data Services, Národná banka Slovenska), Martin Kostic (Chief Executive Officer, EMARK). Budeme mluvit o důvěře v data, interní spolupráci, praktických zkušenostech z projektů i AI podpoře rozhodování. 𝟏𝟓. 𝟏𝟎. 𝟐𝟎𝟐𝟔 | 𝐁𝐞𝐭𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐄𝐯𝐞𝐧𝐢𝐧𝐠 | 𝐎𝐑𝐄𝐀 𝐇𝐨𝐭𝐞𝐥 𝐀𝐧𝐠𝐞𝐥𝐨, 𝐏𝐫𝐚𝐡𝐚 Registrujte se zdarma na 👉 https://lnkd.in/dNtUqdx5

The potential of machine learning in dairy starts long before the prediction. It starts with quality data. Farm data often comes from multiple systems and sources. When that information is fragmented or inconsistent, it becomes harder to compare performance, recognize meaningful patterns, and generate reliable insights. Before machine learning can help farmers and advisors identify risks or make more proactive decisions, the underlying data must be collected, cleaned, standardized, and regularly updated. That is where Dairy Data Warehouse comes in: transforming fragmented farm data into reliable, comparable, and actionable information. As Pablo Janowicz puts it: "machine learning depends on good quality data". Find out how DDW can help you get better data.

Many AI initiatives fall short—not because of the model, but because they're built on inaccurate, incomplete, or inconsistent data. Every broken pipeline, schema change, or unnoticed data quality issue increases the risk of unreliable AI outputs. Before you scale AI, make sure your data is ready. With automated data validation and continuous testing, you can: ✅ Ensure data accuracy across pipelines ✅ Detect issues before they impact AI models ✅ Validate transformations and migrations ✅ Build confidence in every AI-driven decision Trusted data isn't just a data quality goal—it's the foundation of successful AI. Prepare your data. Power your AI with confidence. Learn how to turn trusted data into AI-ready data.

Many AI initiatives fall short—not because of the model, but because they're built on inaccurate, incomplete, or inconsistent data. Every broken pipeline, schema change, or unnoticed data quality issue increases the risk of unreliable AI outputs. Before you scale AI, make sure your data is ready. With automated data validation and continuous testing, you can: ✅ Ensure data accuracy across pipelines ✅ Detect issues before they impact AI models ✅ Validate transformations and migrations ✅ Build confidence in every AI-driven decision Trusted data isn't just a data quality goal—it's the foundation of successful AI. Prepare your data. Power your AI with confidence. Learn how to turn trusted data into AI-ready data.

What if your scrapers fixed themselves when a website changed? 🤔 Building a scraper is quick. Keeping it alive is the hard part. Selectors break, requests get blocked, and nobody notices until the data stops. Join our live webinar + Q&A: Stop fixing broken scrapers: build self-healing scrapers with Scraper Studio In this live session, we'll turn a public website into a hosted data API from a single prompt and run it on Bright Data's infrastructure with unblocking built in. Then we'll show Auto Self-Healing repairing the scraper when the site changes, with the option to review the fix before it goes live. You'll learn how to: ✅ Build a working scraper from your coding agent (Claude Code, Cursor or Codex) with one CLI command ✅ Let Self-Healing repair a broken scraper, then review or roll back the fix ✅ Trigger self-healing automatically when your scraper's success rate drops ✅ Configure how many healing runs happen per job and per day 📅 Wednesday, October 14, 2026 🕓 4PM CEST 🎤 Hosted by Anil Kumar Krishnashetty, Technical Product Marketing, Bright Data 👉 Save your spot: https://lnkd.in/diRHAMV8 #WebScraping #DataEngineering #AI #Automation #BrightData #ScraperStudio

The same property can appear differently across listing platforms. Datamam helps you compare prices, fees, availability, and property details across selected sources in a normalized format.

Choose the markets, sources, properties, data fields, collection frequency, and delivery method. Datamam engineers the real estate data infrastructure you actually need for your project.

Finance zegt 10. Sales zegt 12. Wie heeft gelijk? Als afdelingen op andere cijfers sturen, is het tijd om uit te zoeken waar die verschillen ontstaan. Dat kan komen door verschillende definities, systemen of rapportages die niet op elkaar aansluiten. Bespreek het met een van onze dataspecialisten om inzicht te krijgen in de oorzaak en weer te sturen op eenduidige cijfers.

Staat je data verspreid over verschillende systemen en kost het je veel tijd om alles bij elkaar te zoeken? Bekijk in een demo hoe je gegevens uit verschillende bronnen samenbrengt en omzet in één helder overzicht met bruikbare inzichten voor betere beslissingen.