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"Our data is fairly complete" isn't good enough anymore. 🔍 CDQ's free AI Readiness Scorecard replaces the guesswork with a measurable score across 5 weighted dimensions - so you know exactly where you stand before you scale AI. ✅ Free assessment 📊 Score 0-100, three clear tiers 🗺️ A roadmap, not just a report 🎁 See where CDQ Genius fits based on your result

Most teams assume their data is "good enough" for AI. Few have actually measured it. 📊 CDQ's free AI Readiness Scorecard gives you a clear, fact-based score before you invest further in AI initiatives. ✅ Free · no sign-up hassle 🎯 5 weighted dimensions, one clear score (0–100) 🗺️ Low / Moderate / High - plus what to do next ⏱️ Takes minutes, not weeks

Will a new retail destination draw visits away from the city centre, or could both places benefit? Christian suggests comparing similar locations and footfall patterns before assuming one will threaten the other. Watch the full discussion.

What can an asset manager learn before the pitch even begins? Christian Betov explains why a focused first look at an asset's data can reveal opportunities that owners and competing bidders may have missed. Watch the full conversation.

Many teams still spend hours every week building reports to answer questions that take seconds to ask. “Why did win rate fall last month?” Answering it usually means opening a reporting tool, building a new report or query, trying different breakdowns and comparing periods until you find an explanation. An AI assistant should make that easier. But it can only do the job properly if it has access to calculated metrics. Connect one directly to an API and it may receive thousands of individual records instead. It then has to retrieve all the relevant data, navigate pagination and API limits, and decide how to calculate the metric itself. That can be slow, expensive and error-prone. The Geckoboard Metrics MCP takes a different route. Geckoboard calculates the metrics before passing them to your AI assistant. You can ask: “Investigate the fall in win rate last month and show me where it was concentrated.” Your AI assistant can start with the metric, investigate what changed and turn the findings into a report or another useful resource. You get the answer without building another report, and your AI assistant doesn’t have to reconstruct the metric from raw records. See how the Geckoboard Metrics MCP works. Link in comments.

The leadership meeting starts in 20 minutes! What does the support update need to cover? In this example, an AI assistant uses the Geckoboard Metrics MCP to compare demand, SLA performance and backlog health. Demand and SLA performance have recovered, while 34 Tier 1 web-form tickets remain at least seven days old. The result is a concise weekly brief that captures the recovery and makes the unfinished work clear.

Grist Mill is proud to be a part of the NATO DIANA Decision Superiority for NATO Warfighters challenge. Grist Mill's Jennifer Obernier and Ben Crocker discuss in this video how commercial data can strengthen operational readiness, improve decision making, and help the Alliance stay ahead of an increasingly complex security environment.

Interest and demographic segments reach a lot of people. But is it the right audience? Start with place instead. Pick the stores, venues, and trade areas that matter to your brand, set a time window, and compose the audience. Then send it to your DSP, SSP, or social platform. Bring one brief to a 20-minute call and we'll scope it with you.

Golf brands: an active golfer audience built from course visitation. Restaurants: an audience around your trade area. Retailers: audiences shaped by real-world activity around your stores and your competitors' locations. Local, regional, or national. For brands, a more confident way to plan and buy media. For agencies, a new capability to offer your clients. Bring one brief to a 20-minute call and we'll scope the audience with you.

The new Coalesce local agentic data engineering experience is now GA! 🎉 About nine months ago, the team started a top-to-bottom rethink of Coalesce with a simple question: how do we build the best agentic SQL editing experience while keeping the core product values our customers love? Well… we've been busy! Today we're announcing new tools that feel familiar (and are fully compatible with your existing Coalesce Projects!), but are built for teams using Claude Code, Cursor, and Cortex Code (CoCo) to build SQL pipelines on Snowflake, Databricks, and BigQuery: - Local editing in the coa CLI—runs off your local files and gives the agent immediate feedback as it makes changes 🚀 - Coalesce Desktop (Mac, Windows, and Linux)—a visual review app that helps you see and understand the AI's changes in real time 🤩 - Native .sql file support—for teams who prefer a SQL-native approach 🔥 - Coalesce MCPs and platform skills 😎 Thank you to our customers for their early feedback, and huge shout out to everyone at Coalesce that made this happen! 🙌 https://lnkd.in/gSjiCPt9

Replacing customer names with "John Doe" and email addresses with "[email protected]" might feel like you’ve checked the compliance box. But it hasn't. It is what I call lazy masking. And it’s a direct threat to both your data security and your release velocity. When you apply basic, surface-level masking to complex enterprise databases: ❌ Integration tests break because cross-system referential integrity gets destroyed. ❌ QA teams lose days debugging phantom errors that turn out to be data issues. We recently ran a Proof of Concept with an enterprise client, and they uncovered a staggering statistic: 50% of all reported pipeline bugs were not software defects at all. They were caused entirely by bad, inconsistent test data. Under regulations like GDPR and DORA, real compliance isn't just about hiding a name. It requires deterministic masking logic and metadata-driven rules across your entire landscape. Real Test Data Management means achieving 100% compliance without breaking your integration pipelines or slowing down your developers. Curious what a modern, enterprise-ready data masking solution actually requires? I've outlined the 6 non-negotiable features in a blog article. Link in comment.

HR and finance migrations aren't like other system transitions. Payroll, leave, super, the general ledger: these are the systems that employees, managers and stakeholders rely on every day, making data quality and reliability essential. At BizData, we look after the data architecture, mapping and integration that sit beneath these transitions. In this short video, we share how we approach it and a few real-world examples. If an HR or finance transition is on your agenda, find out more here: https://lnkd.in/g97s38Qs Or see some of the work we've delivered for our customers: https://lnkd.in/gjyEFKdA https://lnkd.in/gxNeYQjG https://lnkd.in/g4SRWFtC #DataMigration #HR #FinanceSystems #DataArchitecture

Stop wasting sprints on manual triage. Luciq's agentic AI closes the full fix lifecycle from detection to release.

Luciq surfaces silent friction and resolves it autonomously before it hits your app store ratings, retention, or revenue.

Modern IT environments generate massive volumes of operational telemetry.

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

Sharing a single test database across multiple teams isn’t collaboration. It’s an accident waiting to happen. Here is a scenario I see at enterprise organizations all the time: 1. Team A executes a test run that alters or corrupts state data. 2. Team B’s automated pipeline fails 10 minutes later because the data was altered. 3. Developers waste 3 hours debugging "ghost bugs" caused by dirty data instead of broken code. 4. Everyone lodges a ticket with the DBA team for a refresh and waits... again. When test environments are centralized and shared, speed dies. To scale software delivery without blowing up your infrastructure costs or triggering GDPR privacy risks, you need to decouple your teams. How? 🔹 Ditch full terabyte production copies. Reduce databases to 10% representative subsets. 🔹 Give every team their own isolated, virtual copy of that subset that resets in seconds, not hours. 🔹 Let QA engineers refresh their own clean state via CI/CD pipelines without waiting on DBAs. When teams own their test data in isolation, workarounds disappear, release delays drop, and test coverage actually hits 100%. Read the full breakdown on organizing test data across enterprise teams. Link in comments.

Le vrai coût d'un piratage se mesure en confiance perdue. Regardez la vidéo complète pour savoir comment la protéger.

Quanto tempo pode o seu negócio estar parado? Veja o vídeo completo e defina o tempo de recuperação certo.

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!

𝐊𝐈? 𝐂𝐡𝐞𝐜𝐤. ✅ 𝐃𝐚𝐭𝐞𝐧? Ä𝐡𝐦 … Das KI-Projekt ist beschlossen. Die Technologie steht bereit. Der Use Case ist definiert. Und dann liegen bei Ihnen Jahrzehnte an Konstruktionsdaten, Prüfberichten und Wartungsdokus. Verteilt auf Fileserver, Altsysteme und Netzlaufwerke. 𝐖𝐞𝐥𝐜𝐡𝐞 𝐝𝐚𝐯𝐨𝐧 𝐬𝐨𝐥𝐥 𝐝𝐢𝐞 𝐊𝐈 𝐧𝐮𝐭𝐳𝐞𝐧? Und welche davon darf sie überhaupt sehen? Genau hier wird aus einem KI-Projekt plötzlich ein Datenprojekt. Warum das oft erst viel zu spät auffällt, zeigen wir im Video. 👇

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.