Data Wrangler: Designing Better, Smarter, Faster

Architecture is in the midst of radical change due to the exploration and rapid explosion of technology. New tools, new processes — especially the infusion of data — enhance the possibilities and outcomes that can be achieved in the built environment.  We can now translate new and ever-growing datasets into actionable information that allows us to design for impact.

By pairing data and evidence with subject matter expertise and design thinking, we can develop solutions that result in favorable, predictable outcomes. To that end, at HDR we’ve developed a smart design software called Data Wrangler that allows us to access robust structured data for iterative, real-time comparative design analysis.

By integrating Data Wrangler into our design processes, we can develop more robust early-stage conceptual models with custom metadata.

What is Data Wrangler?

In a traditional design process, when a series of architectural design concepts are generated, the design team and the client have a conversation about the unique set of qualitative and quantitative characteristics, both positive and negative, that each option embodies. In this scenario, decisions are traditionally informed by a high-level discussion of these characteristics. For example, option A may have more glass than option B, which will increase cost but will allow for better views. 

But when you start including more details such as skylights, glass type, exterior shading, energy load, and life cycle, what were two or three scenarios in a traditional process grow exponentially and design decisions become challenging. While the number of options to explore can quickly become overwhelming, it can be immensely valuable to evaluate the concepts in more nuanced detail as this can result in significant cost-savings and better performance in the final design solution. This is where Data Wrangler comes in.

When we receive planning requirements from a client, we work with the client to determine which variables to compare and when. Tradeoffs are then quantified and used to create a data-driven dashboard that allows for continuous analysis as new options are generated and/or modified in real-time. We use Data Wrangler to rapidly generate data-rich conceptual elements in real-time with our clients to support test-fit studies.

If we need to include properties about materials and manufacturing constraints, Data Wrangler allows these properties to be imported and assigned to any geometry in the model. Data Wrangler is designed as a set of plugins for the popular design tools Rhino and Grasshopper. The software is also designed with a growing set of custom integrations so that we can easily move data among other tools including Revit, dRofus, Excel, and custom relational database servers.

How is Data Wrangler Integrated Into the Design Process?

At HDR, we use Data Wrangler in many different ways across a variety of different project types. Below we’ve highlighted a few of the ways it can be applied.

Planning Scenarios

One of the most popular ways we apply the Data Wrangler is to coordinate comparative planning scenarios in the early stages of a project. Many project requirements are undefined in conceptual design and schematic design stages and design teams need to rapidly study options to help the client determine the best path forward. Early-stage models often lack the data capabilities that building information modeling offers in later stages of design. With the Data Wrangler, we can easily integrate an unlimited set of data properties to their conceptual geometry in Rhino. As we adjust the model, Data Wrangler provides instant feedback on the effect of planning decisions on areas, cost, and occupancy.

Energy Use & Environmental Impact

We also use Data Wrangler in conjunction with analysis tools to organize and store performance data that supports discussions about environmental impact and energy use. When our team combines the tool with research-driven metrics, we can use the many data integrations to inform decisions regarding budget and operating costs, productivity, space utilization, employee engagement and satisfaction, recruitment, and retention. We’ve used this process in the conceptual design and schematic design phases of many diverse building types ranging from medical centers to scientific laboratories and commercial developments.

Workplace Optimization

In several instances, we’ve applied the Data Wrangler process in workplace design. Human capital is the central core of all organizations, and any design strategy that improves human outcomes brings significant value to organizations. By understanding the impact of workplace design on human outcomes, organizations can create desirable environments for users. Such environments enable organizations to increase their profits and reduce costs by retaining their best employees, minimizing turnovers, lowering absenteeism rates, decreasing health-related costs, increasing productivity and improving product/output quality. 

Research Space Utilization & Productivity

Research laboratory facilities have many end uses and occupancy variables which have historically made it difficult to accurately measure space utilization or anticipate productivity. Using Data Wrangler to accurately measure the utilization of lab space allows the optimization of research facilities and provides a foundational baseline for projections related to new space needs.

To build an accurate model, space assignments can be exported from a facilities management system and grant data can be pulled directly from .gov databases or institutional research funding data. The model can then be manipulated to examine how staffing projections inform different solutions based on organizational decisions such as shared group and lab support space. These solutions can then be analyzed for how lab density is anticipated to change over time, how much funding a space is projected to generate over time, and a whole host of other variables that can be considered such as research publications, patents, and space commitments.

The above provides just a small snapshot of how Data Wrangler can be a key tool in the design process. In part 2 of this article, we dig into specific project case studies that further demonstrate the value of utilizing Data Wrangler in the design process.