Everyone wants Copilot. Everyone wants AI agents. Everyone wants employees to ask natural-language questions and immediately receive trustworthy answers from enterprise data. But what happens when the underlying data is fragmented, business definitions conflict, governance is weak, and semantic models were never designed for AI? In this episode of M365 FM, Mirko Peters sits down with Walter E. Calcagno, Microsoft MVP in Data Platform, Microsoft Certified Trainer, data architect, author, educator, and co-founder of Data Consultants, to explore why successful enterprise AI begins long before the first prompt is written.Walter brings a perspective shaped by more than a decade working with data. His journey started in accounting, where managing increasingly complex financial information in spreadsheets pushed him toward SQL, databases, Power BI, big data, Spark, and eventually artificial intelligence. He also explains why creating original Spanish-language technical content has become an important part of his work and discusses the growing Spanish-speaking Microsoft data community.
THE BUSINESS QUESTION COMES BEFORE THE TECHNOLOGY
One of the central themes of the conversation is deceptively simple: organizations do not really need more dashboards—they need better answers to business questions. Walter describes a progression that begins with understanding what happened, continues with understanding why it happened, moves toward predicting what will happen next, and ultimately asks what actions can influence the desired outcome. Business intelligence, statistical analysis, machine learning, and recommendation systems can all contribute to those questions, but the technology remains secondary to the business problem.This changes the way organizations should think about analytics. Instead of beginning with Power BI, Fabric, Python, or another technology and asking what can be built with it, organizations should begin with the decisions they need to make. Technologies will evolve and individual products may disappear, but the fundamental business questions remain.
WHY MICROSOFT FABRIC CHANGES THE DATA PLATFORM
Walter explains Microsoft Fabric as Microsoft's attempt to bring technologies that organizations previously assembled individually into a unified Software-as-a-Service data platform. Storage, movement, analytics, big-data processing, Power BI, Spark, machine learning, and other capabilities can operate within a more integrated environment rather than forcing organizations to assemble every component separately.A major part of that architecture is OneLake. Walter discusses how OneLake provides a common data foundation and how technologies such as Delta Lake help organizations work with large volumes of data while retaining structures and capabilities traditionally associated with databases. The objective is not simply to centralize technology. It is to make enterprise data easier to organize, process, analyze, and eventually expose to AI systems.
The discussion then moves to one of the biggest misconceptions surrounding enterprise AI: if the data already exists somewhere, why not simply connect an LLM or Copilot directly to it?Walter argues that this skips essential architectural layers. AI needs context about what enterprise data actually means. Within Fabric, semantic models provide structured representations of business data. But large organizations frequently have many semantic models across departments and domains. Trying to solve that problem by creating one enormous semantic model is not necessarily the answer.This is where Walter highlights ontology as another important layer. Rather than forcing everything into a single semantic model, an ontology can describe relationships across models and provide a structure through which AI systems can navigate enterprise information. In Walter's view, semantic models combined with ontology models represent an increasingly important foundation for connecting enterprise data with LLMs while preserving meaning, relationships, and access controls.
WHAT DOES “AI-READY DATA” ACTUALLY MEAN?
Having data does not mean having AI-ready data. Walter uses the familiar medallion architecture to explain why data must progress through different levels of preparation. The first layer can preserve historical source data without attempting to solve every quality problem. A subsequent layer cleans and standardizes information, handles duplicates, establishes consistency, and prepares the data for broader analytical use. Additional layers can then prepare specific subsets of data for specific purposes such as business intelligence, machine learning, deep learning, or AI.Walter also challenges the idea that medallion architecture must always mean exactly three layers. The number of layers should follow the requirements of the architecture rather than the terminology used to describe it. What matters is that organizations understand what each stage is designed to accomplish.AI re...
