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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.

In my previous article I have mentioned how wrong data generates agent/LLM hallucinations. Recently I realized there is more to it. A new term: cognitive surrender. Have you heard about it? Read the article. Or the BLUF if you have no time.

One insight from Qlik Connect has stayed with me. The average AI project now involves 6.3 organizations. Think about that for a moment. The era of a single vendor delivering customer outcomes is behind us. AI success depends on an ecosystem. I've always believed the best customer outcomes come from bringing together the right expertise. In today's AI landscape, that belief matters more than ever. At Qlik, I think of it as the Power of Three: • Qlik provides the trusted, connected data foundation that makes AI possible. • Technology alliances like AWS, Databricks, ServiceNow, and Snowflake extend what's possible through complementary innovation. • Channel partners bring the industry expertise, implementation skills, and trusted customer relationships that turn technology into business outcomes. Miss any one of those, and the equation changes. Great technology without a partner who understands the customer's business becomes a demo. A trusted advisor without the right data foundation can only go so far. And even the strongest technology alliance only creates value when it's implemented in the context of a customer's unique goals. The conversations I'm having with customers have shifted. They're no longer asking whether they should invest in AI. They're asking whether their data, systems, and operating model are ready to create measurable business value from it. No single company has all of those answers. That's why the future of AI belongs to ecosystems that collaborate, innovate, and execute together. I am excited about what we are building with our partners, and even more excited about what we'll accomplish together.

About 150 files like this arrive on Tuesday, go-live is Friday. Good luck to all the Implementation and Ops Managers out there. What’s the worst file a customer has ever sent you? (yes, the data is anonymized)

Everyone I talk to right now is building something with agents. (We are too) At some point, nobody on the team has done the work themselves. If an agent makes a mistake, will someone know enough to catch it? A payroll expert can tell a migration is broken because they’ve mapped loads of customer data by hand. Take that person away and the error can end up on someone’s paycheck. People usually learn these skills in entry-level jobs, but those jobs are now disappearing. Why hire a junior when an agent can do the task in minutes? Maybe juniors will just learn how to give tasks to agents and check the answers. But will that teach them enough to know when the answer is wrong? Checking work isn’t the same as doing it. So what happens to a company when everyone knows how to delegate but nobody understands how the work actually gets done? Eager to hear your thoughts.

Educação transforma vidas! A Informa D&B acaba de lançar a 2ª edição do Programa INFORMA EDUCA 2026, que apoia projetos educativos de instituições sem fins lucrativos. Representa ou conhece uma instituição com este propósito? Partilhe e/ou candidate-se!

Teams keep telling me their AI projects aren't delivering, so we go and look at the model. The model is almost never where it breaks (exceptions aside). It's usually underneath, in the data foundation everyone assumed was fine, or that got blatantly ignored under the pressure to ship. Hidden technical debt rarely announces itself, causing silent data dependencies with pipelines that got tangled over years of "we'll clean it up later." Or things like a metric that quietly means something different for different departments in different countries they operate in. None of this shows up as a crisis. Rather it shows up as friction, with reports taking longer and business processes that quietly stop behaving the way they used to. Confidence in the numbers erodes a little at a time. Every new use case costs more than the last, because they're building on things nobody wants to touch. Then someone suggested to put AI on top, and AI doesn't forgive ambiguity the way a human analyst does. It scales whatever is underneath it, including the mess. Getting themselves stuck... Sorting it out is mostly unglamorous and unpopular work. It takes clear ownership of data, definitions that hold across business units and markets, pipelines you can change without holding your breath, and a AI-ready data stack solid enough to cope with the supercharger you're strapping on top. With governance built in from the start rather than bolted on later makes a huge difference. All of that is slower than building a demo, of course, but it's the version that survives contact with production. We wrote a more in-depth piece on what this technical debt looks like up close, why it compounds. Worth the read if you're scaling on a stack you're not fully sure of: https://lnkd.in/eFJ3eGNX And if any of this feels familiar, DM me or reach out to the team at https://lnkd.in/e39cCm8e. Happy to talk it through.

This week we published yet another article that goes deeper into the challenges of building Enterprise grade AI and the Data stacks that need to support it. Excuse the cheesy AI generated illustration, but someone in my team thought it was nice... We found that most organisations still treat AI like 'software'. They evaluate it, buy it, switch it on, expect it working by Friday. That mental model is where the trouble starts. Someone provided us with a very nice 'garden' analogy in one of our earlier articles, which we are keen on borrowing. You see, AI behaves less like a product you install and more like a crop you grow. It draws everything it needs from the ground beneath it. If that ground is thin or compacted, nothing you plant on top will grow, no matter how good the seed (at least not in the long run). And by today's standard, the seed is rarely the problem. Capable models are abundant and often free to start with. The reason so many AI initiatives stall in the same place, somewhere between an impressive pilot and anything that runs in daily operations, is that the ground was never prepared. Our latest piece looks forward, at the foundation worth building on purpose: why a warehouse is not a platform, what governance actually speeds up, and why roughly half of an AI-ready platform is not technology at all. If there is one lesson to take with you: AI tools are not magic, and outcomes are engineered. A capable model on a weak foundation produces fragile results that never earn the trust required to run a business. A modern, governed, well-owned data platform produces the opposite, and it keeps producing long after any single project ends. The most useful question isn't whether the models are good enough. They almost certainly are. It's whether the ground beneath them is ready. Read the full article below. https://lnkd.in/eqZYEwND

Excited to once again meet with industry colleagues at IBC - International Broadcasting Convention 2026, in just a couple of weeks' time! Team Hydrolix will be at the show in force, both on our own booth (Hall 14, C47), and via various collaborations with our awesome partners Accenture, Amazon Web Services (AWS), Bitmovin, CacheFly, EINBLIQ.IO, Fastly, G&L Systemhaus, GlobalDots, Touchstream, TrackIt, and Varnish Software amongst others! We're also looking forward to participating in the Rise 4K for the Future annual charity race - always a great event, and always a nice way of connecting with the media technology community at large. Link in the comments! If you'd like to set up some time to catch up whilst in Amsterdam, ping me or use the link below. Look forward to seeing old friends and making new ones at the show. *text generated by a human / image generated by an LLM. I blame the prompter. https://lnkd.in/eYS373vH.

I'll be speaking at #IBC2026, setting the agenda for the global media and entertainment industry from 11-14 September at the RAI Amsterdam. 👉 Join me! https://lnkd.in/erFqg94f

This one hits different. I grew up playing hurling. To the uninitiated, it’s Ireland’s somewhat lawless, reckless-looking cousin of baseball, and the fastest field sport in the world. If you haven’t seen it before, look it up on YouTube. You’ll see what I mean. While I’m no Billy Beane, I am a sports fanatic, so when a team as legendary as the Boston Red Sox scheduled a call with our team, they had my attention right away. From the very first conversation, everything clicked: ✅ Clear, high-impact use case ✅ Smart, motivated team ✅ Perfect fit for our platform ✅ Instant rapport between both teams When everything aligns like that, it’s not just a customer <> vendor relationship, it’s a partnership. Shoutout to Ben, Sukriti, Jon and the rest of the Red Sox team for being such a delight to work with. And to our own team, who knocked it out of the park (sorry, I had to!!) once again.

Ah, mas eu quero o contato do decisor! Vamos começar pelo óbvio: saber o WhatsApp, o endereço completo, o perfil do LinkedIn, o gosto musical, o que prefere no café da manhã ou qualquer outra informação sobre o decisor de uma empresa não vai adiantar nada se o seu #ICP (Ideal Customer Profile) estiver errado! Na IdMax, acreditamos que realizar uma #venda é um processo. E, como em qualquer processo, é preciso seguir as etapas na ordem certa. Pular para a Etapa B ou C antes de concluir a Etapa A compromete o processo e, como consequência, a venda não acontece. Pode parecer óbvio, mas muitas vezes, na prática, isso é ignorado! Imagine este cenário: “Minha empresa fornece produtos de automação de frotas para o agronegócio”. Logo, preciso dos contatos de Gerentes de Frotas no máximo de empresas possíveis do setor agro, certo? ERRADO! A diferença entre modelos de frotas e o estilo de gestão do consumo de combustível, como 2 bons exemplos, de uma empresa que cultiva soja e outra que cultiva laranja é, no mínimo, considerável (para não dizer enorme). Se sua empresa está em uma situação genérica como essa, volte duas casas e refine melhor o seu ICP! Dados como faturamento estimado, se está em crescimento, se pertence a um grupo econômico, qual a data de abertura da sua filial mais recente, características tecnológicas de operação e/ou idade dos seus sócios não são meros detalhes. São dados de análise obrigatória para definir um bom lead para sua empresa investir tempo e dinheiro. Clique aqui e saiba mais: https://lnkd.in/dbrX2UEs

Não está batendo meta? O ano vai acabar mal? 😧😔 Bater a meta de vendas é um dos maiores desafios de qualquer time comercial. Vamos combinar: todo mês surgem novos obstáculos que parecem jogar contra o fechamento de negócios. Aqui vou trazer os principais desafios e como a Inteligência Artificial #AI tem ajudado a acelerar esse cenário: 1️⃣ Leads sem qualidade Nada mais frustrante do que gastar tempo e energia em leads que não estão prontos para comprar. Com #BigData e IA, é possível filtrar melhor esses #leads, considerando comportamentos online e offline, e até prever a chance de #conversão. Resultado? Menos tempo perdido e mais #negócios fechados. 2️⃣ Segmentação ruim Sabe quando a #segmentação é muito ampla ou muito restrita? Você acaba falando com quem não deveria e deixando #oportunidades reais passarem. Com a análise avançada de #dados, a IA ajusta a segmentação de forma dinâmica, encontrando os clientes certos com a oferta certa. 3️⃣ Gaps de dados que atrapalham Muitas vezes, falta aquela #informação crucial sobre o cliente que poderia fazer toda a diferença. Big Data ajuda a identificar esses gaps, permitindo que sua equipe tenha uma visão completa do lead antes de fazer o #pitch. 4️⃣ Escolha do produto certo Não adianta empurrar o #produto X para o cliente Y se ele não está interessado ou não precisa. A IA ajuda a prever o que cada cliente quer e quando quer, cruzando dados de #CRM com outras fontes. Isso aumenta as chances de acertar na #oferta, no momento certo. No fim, o uso inteligente de Big Data e IA é o segredo para otimizar a estratégia de #vendas, melhorar a #performance e, claro, bater a #meta com mais frequência. Clique e saiba mais: https://lnkd.in/dbsjAaY7 #AI #b2b #neowaypartners

At Integration Fox, we speak your language when it comes to making HubSpot the heart of your clients’ tech stack 💡 Whether you're working with MYOB Acumatica, Acumatica, MYOB EXO Business, Simpro Software, AroFlo Software or NetSuite — we’ve got your back with both Blueprint integrations (fast, scalable, proven) and Custom integrations (tailored for complex workflows). Why does this matter for you and your clients? ✅ Drives stickier HubSpot adoption ✅ Increases platform ROI ✅ Reduces manual work + human error ✅ Streamlines ops from marketing to finance ✅ Ultimately… saves money and boosts growth 💰 👀 Watch the video to see how we can help you deliver more value and deepen your client relationships through smart, strategic integrations and email me [email protected] to find out more or book a call with me, link in comments below. #HubSpot #HubSpotPartners #IntegrationFox #Acumatica #MYOB #NetSuite #Simpro #AroFlo #iPaaS #CustomIntegrations #BlueprintIntegrations #SaaS #BusinessEfficiency #RevOps

🚀 Integrations that power your business At Integration Fox, our Blueprint Integrations make it easier than ever to connect the platforms you rely on — helping you eliminate double handling, streamline workflows, and keep every team on the same page. Here are some of our most popular Blueprint integrations: 🔹 HubSpot ↔ Simpro Software Seamlessly sync customer, quote, and job data so your sales and operations teams always have the full picture. 🔹 Simpro Software ↔ MYOB Acumatica Automate invoicing and financial data flow for faster, error-free accounting and better business visibility. 🔹 MYOB Acumatica ↔ HubSpot Bridge the gap between finance and sales to deliver accurate customer insights and smarter forecasting. 🔹 HubSpot ↔ AroFlo Software Connect marketing and field service data so you can nurture leads through to job completion with zero manual entry. 🔹 HubSpot ↔ Retail Express by Maropost Align your marketing and retail operations by syncing customers, sales, and loyalty data for a 360° customer view. 💡 Our blueprint Integrations are designed for fast deployment, ongoing reliability, and simple scalability — so you can focus on growing, not juggling data. Want to find out how we can support you or your customers? Email me [email protected] or book a call with me link in the comments below. #Integrations #HubSpot #Simpro #MYOB #Acumatica #Aroflo #RetailExpress #Automation #IntegrationFox HubSpot Acumatica MYOB Acumatica Retail Express by Maropost AroFlo Software

This is huge! AI agents can now find, access, and use your data. Watch the video below to see what that looks like 👇 Harbr Data has made it possible to use AI tools - like ChatGPT and Claude - to: - Search for your data products - Request access and subscribe to them - Query and analyse them - Deliver/Share them, including Databricks delta shares Crucially, this AI-driven access is governed by enterprise-grade approval workflows, entitlements, automatic enforcement, and auditability, meaning access can be securely extended to people - and agents - outside your organisation. AI has already removed the barriers for people to use data. Harbr Data has now removed the barriers for people to access trusted data in a secure and managed way. As AI becomes the primary way people - and agents - work with your data, it will increasingly be how they expect to discover and access it. For data owners, this raises the question of how to respond to what is likely to be one of the fastest growing distribution channels we've ever seen. Interested to hear people's - and agent's 😉 - thoughts.

Come meet the Harbr Data team, at London Data Summit! You’ll catch us at Convene this Thursday 26th March, for Neudata’s biggest summit yet. 600+ data professionals, 6 core themes, and a full-day agenda packed with workshops and talks. We're looking forward to exploring the challenges and opportunities facing alternative data businesses right now, swapping notes with data buyers, and digging into how data delivery can be a true differentiator.

Campagneresultaten in GA4. Google Ads. Meta. Allemaal anders. Mijn tip. 👇 Baseer je resultaten altijd op cijfers in je backend. Je hebt één waarheid die gekalibreerd is met de rest van je organisatie. ✅ Je voorkomt discussies. ✅ Je optimalisateert op bedrijfsresultaat. ✅ Je kan samenwerken buiten marketing. Credits voor Daniël Cuperus ⚙️🔩 voor de afbeelding.

Heb jij €10M+ ecommerce omzet? En lukt het je niet om te groeien in Google Ads? Ik vroeg Iwan hoe hij zijn grootste e-commerce klanten helpt om in de volgende groeifase te komen. Dit waren zijn 5 stappen: 𝟭. 𝗟𝗮𝗯𝗲𝗹 𝗷𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗲𝗻 𝗼𝗽 𝗺𝗮𝗿𝗴𝗲 𝗲𝗻 𝘄𝗶𝗻𝘀𝘁 ProductHero en soortgelijke tools kijken primair naar omzet en ad spend, maar je mist dan cruciale informatie: • Marges • Inkoopcondities • Handling & fulfilment De extra marge is advertentiebudget die je opnieuw kan inzetten. Dat zorgt voor extra volume én een hoger rendement. Voorbeeld labels zijn: - High profit - Neutral - Low profit 𝟮. 𝗟𝗮𝗯𝗲𝗹 𝗼𝗽 𝗽𝗿𝗶𝗷𝘀𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁𝗶𝗲 Winstinzicht is niet voldoende. Producten waar je niet competitief bent krijg je niet kwijt. Voorbeeld labels zijn: - Lowest price - Neutral price - Highest price Na deze 2e stap heb je een selectie producten die je WIL pushen en KAN pushen. 𝟯. 𝗠𝗮𝗮𝗸 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗰𝗹𝘂𝘀𝘁𝗲𝗿𝘀 Bijvoorbeeld: Lowest price + highest profit = jackpot Per cluster kan je een aparte ROAS bepalen. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗴𝗲𝗯𝗮𝘀𝗲𝗲𝗿𝗱𝗲 𝗿𝗮𝗽𝗽𝗼𝗿𝘁𝗮𝗴𝗲𝘀. Je wilt je campagnes beoordelen op werkelijk resultaat. Dat betekent omzet, brutowinst en nettowinstcijfers uit je backend. 𝟱. 𝗕𝗿𝘂𝘁𝗼𝘄𝗶𝗻𝘀𝘁 𝗲𝗻 𝗻𝗲𝘁𝘁𝗼𝘄𝗶𝗻𝘀𝘁 𝗮𝗹𝘀 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗲𝘄𝗮𝗮𝗿𝗱𝗲 De laatste stap is het algoritme voorzien van de juiste feedback. Dat is geen omzet, maar brutowinst en nettowinst. Hoe hoger het omzetaandeel Google Ads, hoe meer je richting nettowinst gaat. Doe je dat niet? Dan loop je leeg. Iwan, Tjalling en Rikus enorm bedankt voor jullie openheid. Jullie laten zien hoe je resultaat boekt door marketing, finance en inkoop slim te combineren.

Only 6% of enterprises say AI is delivering a significant benefit to their bottom line (McKinsey, Aug 2026) and nearly two-thirds of technical leaders say their companies still struggle to turn data into business outcomes (Salesforce). The AI tooling isn't the bottleneck. The data underneath it usually is. Come talk through it with me and the GRAX team at Mariposas, Monday, September 14, 5–9 PM. Reserve your spot: https://lnkd.in/gbA5BH6y

1 million queries routed to DuckDB. 82% cost savings. 10x faster queries on average. One of our earliest Greybeam customers just crossed this milestone 🎉 At the beginning of this year when Arsham and I started buildig Greybeam, we hypothesized that executing Snowflake queries on DuckDB would save customers up to 70% on their workloads. Now that we've been in production for some months with a small handful of customers, we will need to revise this number. For some workloads, we've seen this number reach a staggering 95% while making queries faster on average. It's amazing to see how far we've come, and even more amazing to deliver on our original promise. See you at 10 million!

The cleaner your SQL looks, the worse it might perform. Take this perfectly readable join: FROM orders JOIN customers ON orders.customer_id = customers.id OR orders.legacy_customer_id = customers.id Looks elegant. Runs terribly. Why? Snowflake can't use hash joins with OR conditions! There is no longer one clear key to partition on and the query optimizer has to account for matches on either column. The ugly fix that actually works: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id UNION SELECT * FROM orders JOIN customers ON orders.legacy_customer_id = customers.id Less readable but nearly 40x faster. This OR join pattern shows up everywhere and it's just one of many innocent looking query patterns that destroy performance. I just wrote a blog that deep dives into 7 ways to make your queries run faster. The others include: - Join optimization - Query pruning - Proper use of warehouse cache - Choosing the right query engine If your queries are slow and expensive, these patterns might be why. 👇 https://lnkd.in/g_v7PjPM edit: the SF10 on the left is a screenshot error. If we run the same query on SF10 it would've taken 2h 57m!

We get 10,000+ sign-ups every month. A lot of them use personal emails. For months, we had zero visibility. We didn't know who was behind those sign-ups. Sales was guessing. Marketing was blasting generic nurture sequences. CS was missing expansion opportunities sitting right in front of them. So we built a reverse email lookup for ourselves. Now the funnel updates in real time. A VP from Salesforce. A Sales Rep from Stripe. A RevOps from HubSpot. They appear the second they sign up. Here's what surprised us: 20% of our biggest customers had signed up with a personal email first. They were testing the product under the radar. We almost missed them. Now we catch every single one. If you're a PLG company, run paid campaigns, or have incomplete CRM data, this is for you. Available now for all FullEnrich users. Credits used only on a successful match.

We get hundreds of sign-ups each week. Most of them are personal emails. We had zero visibility for months. We built a reverse identity to fix our own blind spot. Now the funnel updates in real time. A director from HubSpot. A sales lead from Stripe. A product manager from Notion. They appear the second they sign up. We reach out faster. We run cleaner discovery. We move deals sooner. Our sales cycle dropped by thirty percent. We built the tool for ourselves before anyone else. We still use it every single day. If you're a PLG company; run marketing campaigns; or have incomplete CRM data; this is for you. Available now for all FullEnrich users. Credits used only on a successful match.