Practical guides showing how to solve real data infrastructure problems. No theory—just how companies freed their teams from pipeline work, put data where it needs to be, and shipped what actually matters.
The Challenge Your team is testing OpenAI embeddings, Anthropic’s Claude, and a custom fine-tuned model. Each needs customer data in a slightly different format. The traditional approach: build three separate pipelines, each with its own failure modes and maintenance overhead. Every AI workload expects data its own way. Your
The Challenge Your AI team has transformative ideas. Leadership approved the budget. Then reality: preparing data for AI means months of cleaning and formatting. Data scientists become data wranglers. Engineers build pipelines instead of AI features. By the time data is ready, your competitor already shipped. The problem isn’t
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
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
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
