Microsoft Tools for Making Data AI-Ready

# Overview This brief summarizes Microsoft tools that help turn enterprise data into something AI can use reliably. The focus is on solving discrete problems: cleaning and curating source data, giving that data reusable business meaning, and providing a shared context layer that agents and analytics can consume.

# Clean and curate the underlying data Begin with the practical data-engineering capabilities you already use. Fabric Dataflow Gen2 applies Power Query for low-code cleaning and shaping: standardize codes, remove confirmed duplicates, add human-readable descriptions, and write curated outputs to lakehouse or warehouse tables. That prepares a Customers table with consistent identifiers and clear rules about who counts as a customer.

When transformations require custom logic, use Fabric notebooks. Use Data Factory pipelines to orchestrate ingestion and transformation tasks and enforce ordering and dependencies. SQL views are useful when you need clearer names or filtered perspectives without changing production tables.

# Give the data reusable business meaning A Power BI semantic model converts technical storage into business-friendly artifacts: friendly names, descriptions, relationships, hierarchies, and explicit measures. Place shared calculations into measures to avoid multiple inconsistent implementations of the same business rule. The semantic model exposes concepts such as Customers and Net Sales Amount even if physical tables use different names.

# Ontology helps

Use an ontology when multiple sources or teams need a consistent understanding of the same business concepts. It provides a shared context layer agents and other tools can consume. But an ontology does not replace correct identifiers, agreed definitions, or source preparation — it documents and connects them.

# Practical patterns and distinctions

• Prepare authoritative data before relying on agents to work around quality problems. Fix the data rather than patching queries.

• Consider an ontology when concepts span multiple systems and mappings can reduce confusion without renaming production tables.

# What comes next This article is Part 2 of a three-part series. Part 1 explained the fundamentals of AI-ready data. Part 3 will explain how Prep data for AI and Fabric Data Agent instructions divide responsibilities and how agents bring structured and unstructured data together.