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ERP says one number. Planning says another. By the time Finance reconciles them, the window to act has closed. That's not a forecasting problem. It's a data foundation problem. Primient cut its forecasting cycle from 28 days to 4 hours — with one trusted, transaction-level data foundation.

In the time it takes to read this, thousands of payments succeeded — and some failed. See how real-time observability keeps revenue flowing.

Modern IT environments generate massive volumes of operational telemetry across applications, infrastructure, and services.

Op zoek naar een nieuwe uitdaging en klinkt werken voor een snelgroeiend bedrijf dat een sleutelrol speelt in de Europese data-economie als muziek in de oren? Dan is deze job als accountant op jouw lijf geschreven!

AI is moving fast. Governance is not. That gap is where many enterprise AI initiatives start to fail. AI does not hide governance problems. It exposes them. Outdated files. Overshared folders. Sensitive content. Old permissions. Unstructured data no one ever prepared for AI use. In our latest newsletter, Adrian Knapp, Founder & CEO of Aparavi, introduces Datasobereignty: a practical framework for controlling AI data exposure, reducing risk, and building governed knowledge bases that create real business value. Because the question is no longer just where your data lives. It is what your AI is allowed to use. Read the full newsletter ⬇ #EnterpriseAI #DataGovernance #AIGovernance #UnstructuredData #AIReadiness #Aparavi

Verklein je papierwinkel, vergroot het comfort voor jobstudenten en voldoe aan alle regels met onze digitale oplossing.

🚨 Is your SAP ECC system really ready for the 2027 deadline? Did you know 68% of SAP ECC systems exceed recommended data volumes before migration even begins? Migrating unnecessary legacy data can increase costs, delay timelines, and add complexity. ✅ Book a FREE 30-minute SAP Archiving Assessment to: • Identify archive-ready data • Uncover migration cost-saving opportunities • Get expert recommendations - no obligation 📅 Secure your free assessment today.

Archive360 helps separate valuable data from the systems that created it, so you can retire legacy applications while preserving governed access to the information you still need.

Your AI strategy may be moving forward. But critical enterprise data may still be tied to legacy systems that make it harder to access, govern, and use. AI did not create the legacy data problem. It is making it harder to ignore.

Axiom can run the same machine data workload for 10x less than Splunk Cloud, at list prices. Your teams keep working in Splunk. Axiom holds the data and does the heavy query work. Nothing changes in their workflow.

Swap the Splunk backend for Axiom's data plane, transparently. Your existing SPL, dashboards and detections keep working. Portal keeps the index names they already use. At list prices, the same workload costs 10x less than Splunk Cloud.

𝗗𝗶𝗲 𝗔𝗻𝘁𝘄𝗼𝗿𝘁 𝗱𝗲𝗿 𝗞𝗜 𝗶𝘀𝘁 𝗿𝗶𝗰𝗵𝘁𝗶𝗴. 𝗡𝘂𝗿 𝗱𝘂̈𝗿𝗳𝘁𝗲 𝗱𝗲𝗿 𝗡𝘂𝘁𝘇𝗲𝗿 𝘀𝗶𝗲 𝗴𝗮𝗿 𝗻𝗶𝗰𝗵𝘁 𝘀𝗲𝗵𝗲𝗻. Genau das macht Enterprise AI komplizierter, als es auf den ersten Blick scheint. Sobald KI auf SharePoint, Fileserver, Cloud Storage und andere interne Datenquellen zugreift, reicht es nicht, dass die Antworten gut sind. Die entscheidenden Fragen kommen vorher: - Was liegt überhaupt in unserer Datenlandschaft? - Wo befinden sich sensible Informationen? - Welche Daten sind relevant, redundant oder veraltet? Und vor allem: 𝗪𝗮𝘀 𝘀𝗼𝗹𝗹 𝘂𝗻𝗱 𝗱𝗮𝗿𝗳 𝗱𝗶𝗲 𝗞𝗜 𝘂̈𝗯𝗲𝗿𝗵𝗮𝘂𝗽𝘁 𝘀𝗲𝗵𝗲𝗻? Im Video zeigen wir anhand eines konkreten Falls, warum genau diese Frage entscheidend werden kann. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽 𝗳𝗼𝗿 𝗔𝗜. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗔𝗜. Erst sehen. Dann bewerten. Dann entscheiden. Wenn Sie vor einem ähnlichen Projekt stehen, wenden Sie sich an uns. 📅 𝗕𝗼𝗼𝗸 𝗮 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 und sprechen Sie mit uns über Ihre Datenbasis. https://lnkd.in/et9RF3P6 #EnterpriseAI #DataGovernance #AIReadiness #DataSecurity #Aparavi

"At a business level nothing is precious. Everything is disposable.” AI Builder #9 💫: Eli Schleifer | CEO & Co-founder, Trunk What changes when you can test a product idea in a day instead of spending months building it? For Eli, AI changes more than how fast engineers write code. It changes how quickly they can learn, change direction, and throw away something that doesn't work. His most effective teams today? “Teams of like two or three.” Smaller groups, larger areas of ownership, with each engineer guiding their own agents. And maybe the hardest skill to learn: getting comfortable deleting what you just built. As Eli puts it, “We should be very happy to throw away our most precious things if they're getting in the way.” Thanks Eli for joining us!

AI Builder #10 💫: Jeffrey Snover | PowerShell inventor, Google Distinguished Engineer For Jeffrey, the most underrated shift in AI is the dramatic reduction in cost. “Engineering’s all about trade-offs.” And the cost assumptions behind those trade-offs keep changing. His point: don’t just make the same system cheaper. Ask what you would build if you had 1,000 times more capacity. But cheaper doesn’t mean more reliable. At Harvard, he’s building a neurosymbolic system that combines LLMs with explicit rules and checks. Why? “There are things where the errors matter.” Being able to explain a decision matters too. And where would he put $10M? “I would short frontier models.” He thinks the efficiency gains ahead could undermine the economics of today’s most expensive models. Thanks Jeffrey for joining us!

The fastest-growing AI platforms trust B2 Cloud Storage. So can you. Cloud Storage for AI - the piece most teams overlook until it becomes the bottleneck.

GPU costs are high enough, your storage bill shouldn't be why margins shrink. No egress fees. No lock-in. Just S3-compatible storage built for Neoclouds.

If you're an AI Analyst, you've hit this wall: your prompts work for some queries but not others, you got stuck in loops, you try to guide the LLM through an analytical process and somewhere between the third and fourth step it loses the plot and you only realize it a few steps down the line… All these issues can be helped with better prompting and context engineering. This series explores prompt and context engineering specifically for analytics workflows: how to structure prompts, manage context, writing prompts manually and with tools and we are sharing the prompting framework we use in our application.

𝗛𝗼𝘄 𝗳𝗮𝘀𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗣𝗜 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲? If you’ve ever stitched together auth flows, pagination, schema mapping, and transformation logic, you know writing API connectors is… a whole experience. And most teams are buried in requests from every direction (read: SaaS tools, internal services, partner APIs, etc etc etc) all expecting data ~yesterday~. Join us for our next hands-on lab on December 17 focused on building custom API ingestion pipelines with AI agents. In this session, you’ll walk through real examples of how engineers can keep full control while letting agents speed up the assembly work: ➡️ Generate Python that handles ingestion across multiple API endpoints ➡️ Transform your data with SQL and Python ➡️ Deploy complete API pipelines following solid DataOps practices By the end, you’ll understand how agentic assistance fits into a production workflow and how to apply it to any data source your team throws at you. Register to attend live or receive the recording. https://lnkd.in/g39nG2Fc #dataengineering #dataops #apipipelines #agentic

Hands-on beats hype. Every time. We’re launching the Agentic Data & Analytics Bootcamp — a free, two-day virtual training. Over two focused days, data teams will learn to use AI-powered workflows to build production-ready data pipelines. You’ll get real experience. Sessions include: ➡️ Building agentic pipelines from ingestion through monitoring ➡️ Exploring workflows on Snowflake, Databricks, or MotherDuck ➡️ Live labs, expert-led guidance, and community learnings If your data team is ready to cut manual work, upgrade pipeline reliability, or launch AI-native data experiences this bootcamp is for you 🚀 Registration is free, and spots are limited. Reserve yours now ➡️ https://lnkd.in/g9hiWT-u

SAP Business Data Cloud ist da. Und jetzt? Wer heute mit BW, SAC oder S/4HANA arbeitet, steht vor neuen Architekturfragen: Ist BDC der logische nächste Schritt – oder ein teurer Umweg? Welche Rolle spielt BW künftig wirklich? Wie passen SAC, BDC und bestehende Landschaften zusammen? In unserem Webinar mix xax managing data & information GmbH geben wir einen kompakten, praxisnahen Überblick über die aktuellen SAP-Architekturoptionen – jenseits von Marketing-Folien. Für Data- & BI-Verantwortliche, die fundierte Entscheidungen treffen wollen. #SAP #BusinessDataCloud #SAPBW #SAC #DataArchitecture #Webinar

Snowflake costs don’t spike overnight. They grow quietly across queries, storage, and AI workloads. Anavsan helps teams identify cost drivers early, simulate fixes safely, and improve usage visibility across accounts. Make every credit count.

Snowflake shows where credits go. Anavsan shows what to optimize first. Identify repeated expensive queries, simulate improvements safely, track storage growth early, and monitor Cortex usage across teams. Try Anavsan today.