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Discover how to build digital trust and resilience in banking with insights from industry leaders. Join our live session featuring Forrester Analyst Alyson Clarke and Catchpoint Field CTO Gerardo A. Dada as they share actionable strategies for enhancing operational reliability amidst growing technological complexity.

If you’re building analytics for AdTech or MarTech, you’ve probably felt the pain of slow dashboards, heavy pipelines, and infra bills growing faster than your team. Demandbase has been there too. On their ClickHouse setup, that mix pushed them into extreme denormalization, heavy ETL, and 49 separate clusters just to keep up. So they stepped back and redesigned the architecture. The payoff: they cut hardware costs by 60%, slashed storage by 90%, saved millions, simplified operations for thousands of tenants, and still improved performance on complex, JOIN‑heavy workloads. Their engineering team walks through the concrete changes, trade‑offs, and architecture decisions in the case study. Check out how they pulled it off 👇

Not sure if your use case and dataset are suitable for synthetic generation? Get clarity in a commitment-free session with a BlueGen specialist. No sales, no slides. Just an honest assessment of accuracy, privacy, and feasibility.

How reliable is synthetic data? Synthetic data is rising fast, but one question matters more than anything else: can you trust it? High quality synthetic datasets can mirror real world patterns with remarkable fidelity, often achieving over ninety percent statistical resemblance when generated and validated correctly. But that level of reliability is never automatic. It depends on the quality of your source data, the sophistication of the generative models and the rigour of your evaluation process. Reliable synthetic data consistently proves itself in three areas: 🔹 It preserves core statistical relationships that underpin your analyses 🔹 It performs in downstream machine learning tasks with accuracy close to real data 🔹 It protects privacy by eliminating direct links to individuals and preventing leakage through inference The key is validation. Organisations that treat synthetic data as a pipeline rather than a black box see the strongest results. With proper resemblance testing, utility evaluation and privacy checks, synthetic data becomes a dependable asset that enables faster experimentation, safer collaboration and more resilient models. You can read the full breakdown in the link below https://lnkd.in/dcfikmuu Want to see how reliability is measured in practice? Request a demo: https://lnkd.in/dAv8YSKk

Join our live webinar with our data engineering expert and learn: ✅ Why agents raise the governance bar beyond traditional BI and analytics ✅ The "Agent-Ready" Data Checklist: data quality, metadata & cataloging, access rights, and lineage ✅ What happens when you skip it - real-world scenarios: hallucinations, GDPR exposure, agents acting on wrong inputs ✅ How to pick your first governance pilot and build iteratively - start small, ship in weeks Save your seat!

AI agents promise automation, speed & smarter decisions - but they're only as good as your data. Learn what to fix first before you deploy. Join Daryna Kučerová - Google Cloud Certified Professional Data Engineer at BizzTreat - for a practical session built around one question: is your data agent-ready? No theory overload, just what actually matters when you're ready to deploy. What you'll cover: → How AI agents differ from traditional BI - and why data quality, access control, and lineage suddenly matter more → The "Agent-Ready" Data Checklist: data quality, metadata & cataloging, access rights, and lineage → What happens when you skip it - hallucinations, GDPR exposure, agents acting on wrong inputs → How to pick your first governance pilot and build iteratively - no 2-year transformation required 📅 May 26, 2026 | 10:00-11:00 CET

Bruin understands your churn, LTV, ARPAU, and acts on it.

Claude or Chatgpt can't touch your Firebase. Bruin can. It connects to 1000s of data sources and turns them into answers, dashboards, briefs, alerts, and auto-fixes.

"Your travel AI does not need more prompt engineering. It needs better data. Building a smart travel assistant requires a continuous, real-time feed of flight fares, hotel availability, and car rentals. But maintaining the scrapers to get that data takes weeks of engineering time you don't have. Bright Data runs the collection layer for you. We manage the pipelines, monitor the sources, and deliver structured, AI-ready travel data from day one. Skip the scraping phase. Go straight to building."

Yes, your team can build a travel scraper. But you didn't hire senior engineers to spend their week patching scripts, bypassing bot blocks, and figuring out why hotel availability data stopped arriving. When travel data collection is a DIY project, it drains your roadmap. Bright Data fully manages your travel data pipelines. We handle the site changes, the validation, and the delivery. Your engineers get their time back, and your product gets clean, structured data.

ChatGPT & Codex can now connect to your SQL Database! Features like data governance, accuracy testing, business & database context, and analytics guardrails make it the perfect upgrade for ChatGPT & Codex users who have important business information in their SQL Database.

Claude can now connect to your SQL Database! Features like data governance, accuracy testing, business & database context, and analytics guardrails make it the perfect upgrade for Claude users who have important business information in their SQL Database.

You already have the data, what you need is clarity. Brightmetrics helps Genesys Cloud users move beyond assumptions with the visibility to act decisively, train smarter, and lead with confidence.

You already have the data, what you need is clarity. Scan Health Plan's contact centers use it to retain agents, meet compliance targets, and deliver measurable outcomes across remote teams. Find out how.

Entenda como dados, machine learning e GenAI estão transformando o trabalho de Customer Success, liberando profissionais de tarefas manuais para focarem em ações estratégicas, como personalização da jornada, expansão de receita e fortalecimento do relacionamento com os clientes.

A transformação do marketing já é realidade: dados, machine learning e IA generativa estão permitindo que gestores deixem tarefas operacionais para agentes inteligentes e concentrem energia em decisões estratégicas.

Learn how we transition leading organisations to cloud-based research infrastructure and understand how we deliver unlimited scale, advanced computing capabilities. View our case studies below

When AI stays in pilot phase, transformation stalls. We implement enterprise solutions that move beyond ChatGPT into real workflow automation.

Wholesale values declined again, with cars down 0.91% and trucks/SUVs down -1.11%. Buyers remained engaged but highly selective. Clean-conditioned units and hybrids drew the strongest activity. Luxury and EV models weakened further despite some incentive support. Older clean SUVs and premium vehicles remained steady with consistent retail interest. Crossovers and mid-size utilities recorded the sharpest declines, averaging -1.3%. Front-line inventory continued to outperform overall market trends. Captive and closed consignments achieved stronger results than open lanes. Market tone remained stable but cautious heading into late Q4. Further declines are expected through November as the holidays approach. Auction conversion rates slipped to 56%, down 1% week-over-week. Full Market Insights: https://heyor.ca/2Uwsty #WholesaleValues #MarketInsights #CarDepreciation

The only thing you can guarantee about auto data is that it’s going to change, in one direction or another. Unfortunately, standard data tools don’t reflect the constant churn of new information, leaving dealers in the past, operating on outdated stats. The older your data, the higher your risk. But it doesn’t have to be that way anymore. Black Book is setting a new standard in the automotive data and analytics arena, going deeper but faster than everything else. We’re bringing data for the future, not just last quarter. See Black Book in action here: https://heyor.ca/vUFTY8 #vehiclevalues #residualvalue #autodata #automotiveindustry #autoinsights

See how your credit union compares to national trends with a personalized CU Index Scorecard. Get insights into member behaviors, emerging financial signals, and areas where your CU can grow.

Discover where your credit union excels and where you’re falling behind. Your CU Index Scorecard breaks down national trends, member behavior shifts, and growth signals to help your teams prioritize their next moves.

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„Kein Mensch braucht ein Data Warehouse!“ Stimmt. Wenn dich nicht interessiert, was in deinem Unternehmen passiert. Wir kennen diese Aussagen: „Unser ERP reicht.“ „Power BI direkt auf die Datenbank.“ „Historie brauchen wir nicht.“ „KI machen wir später.“ Doch diese Aussagen kommen fast immer von Unternehmen, die Entscheidungen aus dem Bauch treffen – und sich wundern, warum Zahlen nie zusammenpassen. Aber was dann passiert, kennt jeder IT-Leiter: Excel-Listen als „Single Source of Truth“. Berichte, die sich widersprechen. Preise, Kunden, Strukturen ohne Historie. Und KI-Projekte, die mit 600 Datensätzen scheitern. Nicht, weil die Tools schlecht sind, sondern weil das Fundament fehlt. Ein Data Warehouse ist kein Trend. Es ist auch kein Tool. Es ist eine Entscheidung. Willst du Meinungen oder belastbare Antworten? Genau darüber sprechen wir in unserem neuen YouTube-Video. Erfahre, wann ein Data Warehouse wirklich Sinn macht und wann du dir den Aufwand sparen kannst. Den Link zum Video findest du in den Kommentaren. Wie trefft ihr aktuell Entscheidungen – mit Daten oder mit Hoffnung?