Accepted Paper:

Talking to Non-Experts about Data: Translating and Synthesizing Modeling Data in Design Teams  

Author:

Gina Neff (University of Oxford)

Paper short abstract:

How do engineers translate data to teams? How do these teams synthesize these data into design & construction decisions? We studied 14 hospital projects to find the mechanisms and strategies of communicating data. We learn more about the "last mile" of data science: integration into team decisions.

Paper long abstract:

Teams often struggle to incorporate data into their decisions. We studied energy modeling in the design and construction of health care projects. How do engineers translate complex data to teams of architect, engineering, & construction professionals? How do these teams synthesize these data into design & construction decisions? We studied 14 hospital projects to find the mechanisms and strategies of communicating data for informing better collaboration in the design process. Successful energy engineers made data meaningful to people who may not have expertise with data. We find

1) Communication Shapes Data : The goals of the project and the concerns of the design shape what kinds of energy analyses engineers do how they share the data. The team's understanding of the modeling software's technical capacities and constraints influences the analysis and how they use it for design decisions.

2) Data Are Facts, Probabilities & Negotiations: Engineers must convince other people who have different data skills. Non-engineers can see a model's data as a hard fact or truth or as a range of probabilities. The assumptions and construction of the energy model is negotiated and debated during team meetings and altered to represent the needs of design team members.

3) Engineers Bundle Data to Navigate Data's Competing Needs: Successful engineers use data communication strategies that anticipate the needs of their audience and reflect their own goals. For presentation to non-experts, they bundle data with analyses and stories based on what the team wants and what they judge experience as optimal.

Panel T113
Critical data studies