A Tale of Two Models: Understanding Data Workers' Internal and External Representations of Complex Data

TL;DR

This study explores how data workers' mental models of complex data often differ from the formal models, affecting their ability to analyze and interact with data effectively.

Contribution

It provides insights into the divergence between internal and external data models and suggests design interventions to improve data analysis tools.

Findings

Participants had diverse mental models of data.

Divergence between mental and formal models limits reasoning.

Design interventions can bridge the gap in understanding.

Abstract

Data workers may have a a different mental model of their data that the one reified in code. Understanding the organization of their data is necessary for analyzing data, be it through scripting, visualization or abstract thought. More complicated organizations, such as tables with attached hierarchies, may tax people's ability to think about and interact with data. To better understand and ultimately design for these situations, we conduct a study across a team of ten people working with the same reified data model. Through interviews and sketching, we probed their conception of the data model and developed themes through reflexive data analysis. Participants had diverse data models that differed from the reified data model, even among team members who had designed the model, resulting in parallel hazards limiting their ability to reason about the data. From these observations, we suggest potential design interventions for data analysis processes and tools.