Data systems are designed to make information consistent, searchable, and usable. So you structure fields, enforce rules, standardize formats, and clean records. But what happens when a system follows its rules perfectly and still gets something important wrong?

Names offer a revealing case study. They look simple in a database, yet the assumptions built into their collection, storage, transformation, and use can affect how people are identified, contacted, and represented across systems - causing downstream friction and undermining organizational goals.

We'll use names as a lens to examine the tensions, tradeoffs, and gaps inherent in data system design. Participants will learn to:

>Spot assumptions embedded in data collection, validation, and transformation.

>Recognize when data-cleaning practices can remove useful information.

>Predict downstream consequences of seemingly small design decisions.

>Ask better questions about what systems may preserve, alter, or omit when representing people.

Keywords: Data Quality, Data Governance, Names and Technology, Human-Centered Technology