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The Challenge Your product team wants to test RAG with three different vector databases, compare embedding models, and experiment with chunking strategies. Each variation means filing engineering tickets. Engineering backlogs measure in quarters. By the time you test approach No. 2, your competitor already shipped the winning solution. AI innovation
Culture doesn't just happen. You build it. You have to be intentional. Patrick Lencioni's "The Advantage" shaped how I think about culture. Values have to be real, not aspirational. They have to mean something. They can't be corporate nonsense that no one
A ritual playing out in boardrooms everywhere: the new data strategy presentation. This time it’s different. This time we’ll capture the value. The slides are beautiful. The architecture diagrams are comprehensive. Everyone nods. Nobody believes it. We’ve been here before. At least three times, in fact. The
December 4 at 11 a.m. PT / 2 p.m. ET Six-month data migration timelines. Projects on hold. Costs climbing. What if you could test your migration in parallel with production? Roll back mistakes without losing work? Compress months into weeks? Join us December 4 to see how Matterbeam makes
The Challenge Your data engineering team wants to empower every team with reliable data access, giving them the data they need, when they need it, without friction. Instead, they’re writing custom pipelines for every data request. Sales needs customer data in a new CRM. Marketing wants campaign attribution. Product
Matterbeam goes against the data industry's complexity addiction. We built it to let small companies access sophisticated data integration without enterprise budgets. You're not locked into decisions. Time is on your side. Transform and emit data fearlessly as new use cases arise.
Welcome to Data Made Easy. This series explores how forward-thinking teams are solving data challenges and turning complexity into agility. These stories show data doesn’t have to be this hard. The Challenge Your reps are in front of customers ready to close, but the real-time data they need is
Around 2015, I was leading an extraordinary architecture team in an effort to decompose Pluralsight’s monolithic application into distributed services. The system exhibited classic tight coupling symptoms: changes cascaded unpredictably into other components, deployment required coordinated release windows across multiple teams, and specific engineers became single points of failure
A conversation with Galen Schreck, fractional CTO and enterprise architect, about the real challenges of working with Salesforce data and what needs to change. There’s a conversation that happens in almost every company using Salesforce: Sales team: “We need all our customer engagement data in Salesforce so we can
