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Traditional MQLs are dead, and the funnel has been rewritten by AI search. Get a clear view of how your brand appears in AI recommendations to ensure your pipeline doesn't dry up.

Your next big deal is likely researching you via AI search right now. If you can't see your own AI visibility, you're blind to the top of your modern funnel. Start measuring the signals that actually drive today's revenue.

Category planning starts with cost visibility. See how commodity prices have moved and where they could be heading. Expana is an independent market intelligence provider for the global agrifood sector, with 40,000+ commodity prices and 25,000+ analyst price projections.

A supplier that passed onboarding last year can look very different today. Speya keeps checking the whole supplier base and shows the source for every change.

Pas besoin de remplacer toute votre stack de logs en une seule fois pour adopter OpenTelemetry. Une migration peut se faire progressivement : commencer par quelques sources, introduire le Collector, standardiser les flux, puis faire évoluer les backends au rythme de vos contraintes. Ce guide vous donne une méthode concrète pour construire cette transition sans « big bang ». ✓ architecture cible ✓ correspondances avec vos outils actuels ✓ étapes de migration ✓ check-list opérationnelle Téléchargez gratuitement le guide OpenTelemetry de Centreon.

Who is Expana? An independent market intelligence provider for the global agrifood sector. 40,000+ commodity prices across ingredients, packaging, logistics, energy and labour, plus 25,000+ analyst price projections up to three years out. Transparent, neutral data to inform sourcing, category planning and supplier cost reviews.

OpenTelemetry s’impose progressivement comme un standard de l’observabilité. Mais entre Collector, pipelines, logs, métriques, traces et backends, le passage à la pratique peut sembler complexe. Ce guide a été conçu pour les équipes IT et exploitation qui veulent comprendre concrètement comment intégrer OpenTelemetry à leur environnement. Au programme : ✓ le parcours complet d’un log ✓ les principaux composants à connaître ✓ des exemples avec syslog, ELK et Graylog ✓ une méthode pour démarrer progressivement Un point de départ concret pour passer de la théorie à l’architecture.

Join queries and scan queries reordered the engines more than compute size did. Filter 191,176 query executions based on your workload.

SIEM costs are climbing and your SOC still can't see everything that matters. DataBahn gives CISOs one control plane to cut cost, close visibility gaps, and own their data — on their terms.

Every "best master data management software" list I have read this year has the same problem. It was written for enterprise data teams reconciling customer records. Finance was an afterthought. Finance has a different job. The chart of accounts, legal entities, cost centers, and hierarchies have to stay correct across the ERP, the EPM applications, and the warehouse, on a close calendar that does not move. So we wrote the comparison we could not find: nine platforms, judged on the six things finance actually needs. 1. Governance workflow. Can a change be requested, approved, and deployed through an audited path? 2. Data quality. Is bad metadata blocked at entry, or found during close? 3. Enterprise integration. Does it reach Databricks, SAP, Oracle EPM, OneStream, and your ERP without a build? 4. Business-user ownership. Can finance run it without a ticket? 5. Audit and versioning. Who, what, when, why. 6. Cost and licensing. Does the pricing model punish growth? We covered Informatica, Profisee, Semarchy, SAP MDG, Oracle EDM, Ataccama, Reltio, Stibo, and EPMware. Yes, EPMware is ours, and we say where each of the others is the better choice. The pattern that emerged: most of these are excellent platforms built for IT. Only a couple are built for the close. Full guide in the comments. #MasterDataManagement #FinanceTransformation #OfficeoftheCFO #DataGovernance

Choosing MDM software for finance? Check who will run it day to day. Your team needs to keep accounts, entities and hierarchies aligned across systems. That means knowing who can approve a change, how it gets checked and how it reaches every application. EPMware’s buyer’s guide compares nine platforms on finance ownership, integrations, audit history and licensing. Use it to build a shortlist around the work your finance team actually needs to do.

Can you see your entire electricity network? Accurate asset information helps teams understand where maintenance is needed, identify changes and plan future upgrades. But that information is only useful when it reflects the current network. UNE for ArcGIS helps more authorised employees contribute network updates, supporting a more accurate view of your assets and infrastructure. See how UNE can help you get more from your existing ArcGIS Utility Network.

Jefferies is one of the world’s leading full-service investment banking and capital markets firms. Their teams run complex statistical and time-series analysis across historical data and embed ClickHouse directly into applications. You’ll learn how they: - Run complex statistical and time-series analysis across historical market data - Support trading-desk analytics and application workloads on the same database - Backtest strategies across high-volume pricing and trade data - Use ClickHouse Cloud for autoscaling, backups, and managed storage to simplify operations

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Equiti Group, a global fintech leader in advanced trading technology, physical commodities, and payment software is expanding into institutional and wealth management. They built a managed store for pricing, trade, and machine learning data on ClickHouse Cloud for Azure, giving its teams faster access to historical analysis while reducing operational overhead. You’ll learn how they: - Run complex statistical and time-series analysis across historical market data - Support trading-desk analytics and application workloads on the same database - Backtest strategies across high-volume pricing and trade data - Use ClickHouse Cloud for autoscaling, backups, and managed storage to simplify operations

Votre IA est aussi performante que les données qu'elle utilise. Connectez Cognism à vos workflows via MCP pour gagner en efficacité, sans multiplier les outils.

Your agent calls many different tools, BUT some are dangerous! Now you can review a given tool call before any disaster 👇 Airflow just introduced tool-call approvals 🤗 Works on @task.agent and AgentOperator available in Airflow 3.3+. How does it work? Simple: ✅ toolset.approval_required(func) on any toolset, SQLToolset and MCPToolset included ✅ Tool(refund_order, requires_approval=True) inside a FunctionToolset for a single function Let's say we have a refund_order tool. The function gets the tool name and the arguments, and requires human review for refunds over $50 in one line. What happens at the call: 1️⃣ The task pauses in awaiting_input 💡 No worker slot held, like a deferred task 2️⃣ The request shows up on Required Actions 💡 Tool name and arguments, secrets masked Then you: 3️⃣ Approve 💡 The call runs, the agent carries on 4️⃣ Reject 💡 The task doesnt fail. The agent is told your reason and carries on without the call You can specify tool_approval_timeout to define the wait. On timeout, you get fail or deny, and you can even set tool_approval_assigned_users to who can decide 😎 Now you can finally better control your agent! Try it, you gonna love it Enjoy ❤️ P.S: Like and share to help your teammates #airflow #apacheairflow #dataengineer #dataengineering

Tools allein reichen nicht. Entscheidend ist, was sich in Ihrer Organisation wirklich verändert.

Most organizations shipping AI have the same blind spots: fragmented visibility, compliance risk, rising costs, and unpredictable behavior. Learn why Gartner® says AI observability is becoming essential.

Dataciders hilft Ihnen dabei, aus Strategie messbare Ergebnisse zu machen.

Traditional monitoring leaves AI teams flying blind. Gartner® explains why AI observability is now essential for governing agentic AI, controlling costs, and scaling production workloads with confidence.

Fragmented data and inconsistent business logic can make it difficult to trust analytics and AI. Join Fivetran and dbt Labs on 17 November for a 15 minute live demo of how they help teams move, transform and test data. https://lnkd.in/ef36_pYb

👻 Sneak peak from the webinar this Thursday: Debugging Apache Airflow's scariest issues! It is spooky season and Kenten Danas and I selected some of the spookiest errors we've encountered to tackle together with our agentic friends! You'll learn how to tackle the kind of issues where the error trace does not help (or does not exist) as well as how to give your agents the best shot at being helpful assistants in debugging Airflow. Join us live on Thursday Oct 29th at 11am ET / 4pm CET! Or register and watch the recording afterwards (but this one is much more fun live 😄 ) Link in the comments!

Fragmented data, inconsistent business logic and unreliable pipelines make it difficult to trust analytics and AI. Register for this 15 minute live webinar to learn how Fivetran and dbt Labs can build a reliable data foundation. https://lnkd.in/ef36_pYb