Making Data Simple

Making Data Simple is a brisk, interview-led show about enterprise data, AI, software, and the people trying to make those systems useful at scale. Its center of gravity is practical business technology. The conversations can move from software asset management and FinOps into graph neural networks, AI databases, RAG, vector embeddings, Python packaging, and real-time data access without losing the thread of why any of it matters. Guests usually get space to tell the story behind the work. That might mean a founder's path, a move between cities, a career shift, a previous company, or a personal detail like rock climbing before the technical discussion tightens. The show is casual in its surface texture but serious about implementation. Big AI claims are met with follow-up questions about hallucinations, enterprise search, licensing risk, customer support workflows, data lakes, structured business data, and where systems fail in practice. Episodes with Anglepoint focus on software and cloud spend, managed services, long-term customer outcomes, and large complex environments. Conversations with Kumo, Maven AGI, MongoDB, Anaconda, and Snow Leopard stay close to current enterprise AI problems: recommendation systems, agentic support, retrieval, embeddings, packaging, open source, and connecting models to live data. The show also broadens beyond pure technology. Leadership episodes examine employee buy-in, performance, measurement, and the human side of organizations. Other conversations bring in mindfulness, skills, corporate responsibility, and responsible leadership. That range gives the show a useful tension. It is not only for engineers, and it is not only for executives chasing AI headlines. Its best moments sit between those groups, where technical choices become budget choices, leadership choices, and adoption choices. The pacing is conversational, with interruptions, clarifications, jokes, repeated framing, and the occasional detour. That looseness is part of the appeal. Making Data Simple works for listeners who want enterprise AI explained by technical guests, but who also want someone in the room asking what the product does, why it matters, and whether it will survive contact with a real organization.