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Customer risk, supplier checks, bank details: every new partner needs several checks before it reaches SAP. Done manually, they slow down your teams and let errors slip through. dataxess for SAP brings verified Altares Dun & Bradstreet data into your onboarding process, so every Business Partner is created on solid ground.

Get the full picture of your account from a single prompt. Pull EU compliant, verified contact information into your existing workflows and tech stack with Cognsim MCP.

Get the full picture of your account from a single prompt. Pull EU compliant, verified contact information into your existing workflows and tech stack with Cognsim MCP.

Clean Core, oui. Mais vos métiers ont toujours besoin de plus de données dans SAP. Risque, fournisseurs, conformité : chaque nouvelle connexion spécifique alourdit un peu plus votre environnement. dataxess for SAP est conçu nativement sur SAP Business Technology Platform pour intégrer des données tierces fiables, sans multiplier les développements dans votre core

Une faute de frappe, et votre client existe en double dans SAP. Une adresse obsolète, et c’est votre facturation qui en pâtit. Avec dataxess for SAP, vos équipes recherchent l’entreprise dans les données Altares Dun & Bradstreet et créent le Business Partner avec des données vérifiées dès leur intégration

Faster issue resolution has a dollar value. Fortune 100 companies run Redgate Monitor, and 93% of Gartner Peer Insights reviewers recommend it. Put your own numbers into the ROI calculator and see the financial picture for your estate.

Your team already knows what good monitoring is worth in hours. Your stakeholders want it in dollars. The ROI calculator turns hours recovered and workarounds retired into a dollar figure, so you can build the business case from your own numbers.

Places tell a story: where activity gathers, when it peaks, and how it changes week to week. Audience Composer turns that story into audiences. Choose your places, set a time window, and compose. Then send it straight to your DSP, SSP, or social platform. Bring one brief and see its exact reach in 20 minutes.

Customer risk, supplier checks, bank details: every new partner needs several checks before it reaches SAP. Done manually, they slow down your teams and let errors slip through. dataxess for SAP brings verified Altares Dun & Bradstreet data into your onboarding process, so every Business Partner is created on solid ground.

Novartis Operations and TechOps drive supply reliability out of Ireland and Slovenia while keeping data maturity at 5+. Snowflake standardizes pipelines and puts supply data in front of AI agents, so plants get value in weeks. It shouldn't take a year to deliver AI.

If you read nothing else: Omni will soon support 100% custom data viz. That means if you can describe it, our AI can build it, and it runs on the definitions in your semantic layer. I’ve spent over 20 years working on data visualization software. Data visualization sits at the intersection of computer science, design, and data analysis. Because chart libraries are usually consumed by highly opinionated viewers, building a single chart that satisfies everyone is already difficult. In BI, coupling chart building to a generic tool is even harder - your library must handle data shapes it hasn't seen before. To use a video game analogy: this isn't hard mode, it's nightmare mode. You can’t make everyone happy all the time, so eventually you need a custom data viz escape hatch. That’s exactly what we’re building. Want a bespoke version of classic cartesian charts? Now you can have it. Want to create a one-off data viz to tell a story for your report? Go for it. Want to replicate your CEO’s favorite Economist chart aesthetic? Yep, you can do that too. Of course, we’re coupling this to Blobby, our AI agent. We're also making it available through our MCP connectors so you can use AI to build whatever you want, wherever you want. All of this stays backed by our governed data model, ensuring the right data is visualized for just the right viewers.

Omni is a launch partner in the SpaceX Super Intelligence Partner Network 🤝 We love building in Cursor, and so do many of our customers. Omni's Cursor plugin and MCP server bring governed data into those workflows, so you can query, define new metrics, and build data apps on the shared definitions your team trusts without leaving your preferred workspace 🛠️ Here's what that looks like end to end. Peter on our Solutions Engineering team built a demo customer portal for our sales team, starting from one paragraph in Cursor and ending with embedded dashboards and AI chat 👉 https://lnkd.in/g3u9RfAH Thanks to the SpaceX team for the partnership, and check out this blog from Peter for a deeper dive on Omni + Cursor.

The thinking isn't the slow part of an impact assessment. It's the chasing, the reminders, the handoffs, the wondering where a case got to and who has it now. Our Workflow Solution automates that coordination. Each case runs on a visual workflow you can see, edit, and control, with AI agents moving it from one stage to the next so the work doesn't stall between people. You review the agents' settings and set the controls, so the automation runs the process the way your team works, and you keep the final say. Regulatory updates land ready to assess, cases carry their own owners and due dates, and the whole thing stays in one place. Instead of managing the process, your team spends its time on the impact assessment itself. Assessments move faster, and the manual chase disappears. See where the Workflow Solution gives your team its time back.

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

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

Byggt i Göteborg av folk som har levt med problemet. Över 100 organisationer i Norden kör SQL Server på autopilot med DB24. Support på svenska.

No one told it to. It learned from biased training data. See what ungoverned AI looks like, then watch the full webinar on demand.

Boards are racing to deploy AI without fully understanding the risks. Hear why accountability can’t be delegated, then watch on demand.

Your context layer was correct on the day you built it. That much is most likely true, but here is what likely happened since: -> An engineer renames a column in your most used table. The layer still points at the old name. -> Finance changes how a subscription tier is bucketed. The metric definition is now silently wrong. -> A new SKU category launches. Every "by category" breakdown is missing it. None of these are edge cases, they are Tuesday morning cases. A context layer is as far from "one-off" effort as it can get. It's a living system with a maintenance surface, which means at minimum: 1. Schema drift detection. When the warehouse changes underneath, the affected definitions are flagged, not discovered by a wrong answer. 2. Change-aware analyses. If you rerun an analysis from last month and the context it depended on has changed since, you should see an alert, not a different number with no explanation. 3. Version history on every definition. Who changed what, when, and what did the previous version say. Rolling back a metric definition should be as easy as rolling back a commit. 4. Sync with whatever semantic or context layer already exists. Most companies already have some of the knowledge in tools like dbt, Tableau, or alike. And on top, a smart harness with an efficiency layer that tells the agent where exactly to dig in the context, to avoid draining the tokens. The uncomfortable truth: even the most sophisticated agent needs constant care - someone, or something, or both, monitoring and testing the context continuously.

Everyone talks about how the context layer drives accuracy. Almost nobody talks about what it costs to use one. In our opinion, retrieval efficiency - how the agent finds what it needs in the layer - deserves the same attention as the context itself. The tokens spent on a question are set by how much the agent has to read before it finds the right definitions. Take "what was ARPU by region last quarter, excluding internal test accounts". Everything needed is in the layer. The cost depends entirely on how the agent gets to it. With naive retrieval, the agent is like a new employee on day one - smart, but with no one to ask, so it reads everything to answer anything: 1. Finds four revenue-related definitions. Reads all four to decide which is the source of truth. 2. Reads the dimension definitions to learn how region is represented, and whether country, geo or market is the live one. 3. Looks for the test-account rule. It is in the layer, but filed under "internal traffic exclusion", so the search misses it and the agent reads on. 4. Generates, hits an error on a column name it read fifty turns ago and no longer has in context. Re-reads, regenerates. Every step is paid for. The answer may well be correct, but it costs many times what it needed to, takes longer, and gets less reliable as the window fills with material the question never needed. This is why, in our view, retrieval is not a layer you bolt on. It is part of the harness - the tools the agent works through. How it searches. What it uses. When to search again, when to stop, and what to do when nothing is good enough. Done right, the agent receives, before generating anything: - The single source-of-truth definition, not four candidates - The valid dimensions and their real column names - The business rules in scope, even those stored under a different name - The relevant skills: proven ways to answer this, including the SQL All this searching and filtering runs as plain code, outside the model. Code is cheap. Model calls are not, so the less the model reads, the less the question costs. Three more things that keep the cost down: -> Work with the models' cache. Prompt caching rewards stable, well-ordered context. Making the repeating parts hit the cache is a real cost lever, and easy to get wrong. -> Turn research into context. When a metric is not in the layer, the agent researches it from scratch. If people keep asking, that research and the warehouse queries behind it repeat. The agent should notice and propose adding the metric to the layer. -> Promote repetition out of the agent. If "weekly retention by cohort" is asked every Monday, it should stop being an agent query. The agent can notice the pattern and propose a dashboard, same for repetitive reports. The savings depend on schema size and question complexity, but the gap between naive and efficient retrieval is real money. It is the difference between a tool everyone can use freely and one the company has to restrict.

A new era of observability is being born. And yes, we're throwing it a birthday party at KubeCon NA! 🎂 Join the party and see observability worth celebrating in action! Zero instrumentation, faster investigations, and full context into everything happening in your environment. 📍 Booth #111 | The groundcover Birthday Party

A new era of observability is being born. And yes, we're throwing it a birthday party at KubeCon NA! 🎈 We're talking birthday gifts, scratch cards, and observability that actually gives you a reason to celebrate. 📍 Booth #111 — just look for the bouncy castle on the expo floor!

Mining Ops - real-time tracking of trucks using #IconMap with #RealtimeIntelligence in #MicrosoftFabric. In earlier posts I showed tracking aircraft via ADS-B, then trains in Spain via GTFS. This example also uses our vehicle tracker map layer, but this time showing trucks as they travel up and down a mine in Chile. Using satellite imagery from Mapbox and Icon Map's built in digital elevation model (DEM) we can show the realtime locations within the mine highlighting any alerts from sensor data. Selecting on a truck can reveal additional information in a popup panel. We also support the ability to provide your own elevation models, 3D vehicle models and drone imagery for the most up to date and realistic digital twin. For more information sign up for the preview: https://lnkd.in/ecpnG2nU #IconMapForFabric #KQL #EventHouse #3DMaps