AI readiness in financial services means having data, architecture and governance that can support AI reliably, repeatedly and with enough context to explain how information was produced. For those of you wanting the cutting edge, that definition may sound less...
AI can generate SQL remarkably well. Give a modern AI model a schema, explain the desired output and within a few seconds it can produce joins, transformations, window functions, stored procedures and increasingly sophisticated data pipelines. For data engineers who...
Data validation for AI verifies that data meets defined quality requirements before AI systems rely on it. That sounds straightforward but the problem becomes more complicated once AI moves beyond a contained experiment. Enterprise models and agents may consume...
Before we begin, it’s important to set things straight: by ‘data governance’, we mean the set of policies, processes, responsibilities and technical controls that determine how an organization understands, manages and trusts its data. For a data team, you will find...
Our take: medallion architecture is a shared vocabulary, not a specification. It helps a data team, a governance team, and an executive board hold the same picture in their heads; which is genuinely valuable. But the things that decide whether an AI agent gives...
We believe AI readiness in financial services must begin with governed data. Banks and insurers may experiment with new models quickly but the data beneath them must remain accurate, traceable and understandable for years. When an AI system produces an answer about...
Our take: AI readiness for K-12 data teams needs to start with trusted data. It doesn’t start with selecting an LLM, it doesn’t start with deploying a chatbot and it certainly doesn’t start by giving an AI agent unrestricted access to every table in a student...
Enterprise data modeling is no longer just a design exercise. For years, data models helped architects define entities, relationships, keys, attributes and structures before implementation. That work still matters. Conceptual, logical and physical models remain...
When we flew northbound to attend the Data Innovation Summit, DIS 2026, in Stockholm, we expected AI to dominate the conversation. And it did. But the most intriguing conversations were not about AI in isolation. Rather, they were about what needs to sit underneath...