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...
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...