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...
Data modeling is where AI readiness becomes concrete. AI systems need trusted context, not simply more data. They need clear definitions, understood relationships, known quality constraints and traceable transformations. Without those foundations, an AI agent may...
To migrate data warehouse workloads successfully, start with discovery and dependency mapping. Then design the target, move in waves, validate parity and finally optimize continuously. Sounds simple on the surface, right? But the difficulty lies in everything...
What we’ve observed typically goes like this: higher education data challenges are not usually caused by a lack of data. In fact, most colleges and universities have plenty of data: student records, enrollment data, financial aid information, learning management...
Data modeling workflows need to be predictable. Whether teams are importing models through the command line, running workflow scripts, applying Model Conversion Rules or editing multiple entity columns at once, they need confidence that every step can be monitored,...
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...