A traditional master plan process runs the risk of being an exercise in futility if the result is a document that sits on a shelf and becomes quickly outdated. This often happens because an institution does not know how to convert existing data into meaningful information that can guide discussions and decision-making related to facilities and campus development. Pairing the intelligent use of available data with design processes that engage individual users and stakeholders’ perspectives can be a game-changer. This combination—what we refer to as a “living” master plan—can tell a more accurate story and become a better and more refined decision-support tool than an obsolete master plan or a one-time data analysis report, and can provide value based on real-time data long after the plan is “completed.”
The challenge that many universities face is that numbers, strategies, and projections change over time. Departments evolve and disband; interdisciplinary units form; new faculty join the institution; and funding sources shift. Following a static plan based on decisions and assumptions made years earlier can be expensive, challenging, and problematic. In a competitive academic climate, data-crunching solely to generate obligatory reports for state, federal, and accreditation authorities is no longer cutting it.
There are four ways that academic institutions can leverage their own data to uncover fresh, accurate stories about the current and future states of their programs for years to come, which can ultimately lead to better decisions and support long-term campus sustainability.
1. Consider Data Related to Organizational, Institutional, and Social Networks
It is critical to understand that campus connectivity reaches beyond measuring physical adjacencies and distances. Whether an institution is looking at a specific building, department, interdisciplinary unit, or college on campus, it must consider the space impacts of connections that are organizational (publications between Principal Investigators—PIs), social (amenity-driven), and institutional (industry/university partnerships), as well as physical ones. Successful education environments will stimulate innovation not only through proximity, spatial integration, and site planning, but also through other factors, such as diversity, social networks, and organizational cultures. The tools used to accomplish this task must be capable of transforming data into information.
As the physical scope broadens, so does the depth of information that can be used to explore the space and plan for its future.
Each point in the diagram below lists a research-backed metric that can help foster innovation or connection; as the space gets bigger, more data points are added. For example, in a single classroom or workstation, the most important metric would be the proximity of the people who work there. Step back to look at the entire building, and more metrics become useful: spatial integration, social networks, and organizational culture. On a campus-wide scale, visibility, site planning, demographics, and “the third place” (external amenities, such as a coffee shop or nearby library) are added to the mix.
In many ways, master planning exercises are rooted in an effort to transform information about the current and long-term state of a campus into plans for the future. When utilized effectively, multiple layers of data can help paint a more vivid picture and tell a more detailed story to inform the decision-making process. Traditional master planning exercises are very spatially oriented, focused on tangible things such as growth rates, recruitment, and empty spaces.
If you layer additional data on top of that—social networking, organizational culture—and connect that type of data into a dashboard, you can create micro-feedback loops. The master plan becomes a workshop and not a deliverable. It taps into the aspirations of the institution, for example which departments have strong connections, and which relationships could be fostered in the future. It’s not about plugging in a bunch of numbers; it’s about layering data on top of design concepts.
With the right tools, a university can transform robust data sets, such as geospatial information and room utilization metrics, into information used to facilitate strategic planning workshops, inform master planning, and design campus strategies, and to discover networks for innovation. For example, social network analysis can be used to assess how people interact within an organization. By analyzing the network of relationships between academic departments, the impact of proximity on interaction can be modeled and aid in the design of a facility that fosters interdepartmental collaboration, properly influencing what to build and where to place it.
2. Connect Internal and External Data Sets
Connecting the data available within the institution with publicly available data related to its community or region is another effective way to ensure that a university is considering the full story when making plans for the future. Data related to local and regional businesses, demographics, and land use patterns can provide great insight as to the future of town-gown relationships, partnerships, and student populations. For example, many academic institutions partner with, or receive funding from, private industry for research. By connecting labor data, demographic information, and statistics about the growth of specific academic majors, and creating a visual understanding of those relationships, both academic institutions and their industry partners can define which areas of research will be most beneficial to all parties involved.
If the expansion is in biology, what type of biology? Is the same type of growth occurring in industry and on an academic level? What types of people will the university be recruiting? If there is a growth in student population, what is that population like? Millennials have different expectations of what an amenity is. That is a comprehensive master planning exercise.
Sometimes the university aspires to respond to and support external trends; in other cases, the goal is to guide these trends and attract certain industry to the area. A living master plan provides a framework to be able to look at it from either direction, to build visualizations and dashboards around those datasets.
The data includes a combination of public external information—demographic and census data, for example—and internal data, such as the trends in degrees awarded, research concentrations of PIs (and with whom they are publishing), anticipated growth, research grants, and patterns in collaboration. The data can be very site-specific: One study included a predictive model looking at the distance of walking paths across a campus. As soon as the walk reached 15 minutes, there was a drop-off in collaboration. That might not be true on another campus, but it is important when discussing collaboration at that campus.
3. Apply Research to Prioritize Data
Research should not only inform design solutions, but also can impact the prioritization of data sets. That is the crux of a living master plan: It can be tailored to address certain aims, for example transitioning from a tier-two to a tier-one university, and recognizing that tier-one universities have certain types of spaces, collaborations, and amenities. We need to explore what is important to that university—is there anything missing on their campus?—then use that information to prioritize the data sets. This is where the rubber meets the road, where we start to iterate what the master plan will look like. We need to make sure we’re asking the right questions.
4. Update Data in Real Time
Relying on historic data alone can paint an incomplete picture. An interactive dashboard that can be quickly updated can ensure that individual facilities or master plans are continually evaluated from the perspective of the current state, without having to invest time and resources in repeating a master planning process every few years. Living master plans leverage the data collection processes that are typically already in place and give academic institutions more precise control over their own campuses, allowing leadership to be nimble and decisive while lowering the risk of making less-informed decisions.
These dashboards can be expanded far beyond the standard facilities management tool. For example, scheduling data shows how space is being utilized. Knowing who is booking conference rooms where can yield important information about who is collaborating with whom. Tracking publications by topic, key words, and author reveals the focus of PI research and collaborations. Dashboards allow an institution to access the information it needs when it needs it. The ability to refresh this data regularly is important: If trends or priorities shift, the master plan can accommodate those changes with a course correction, without having to repeat the entire process in five years.
With this kind of feedback loop, a master plan can be revised 10 to 20 times within a five-year period.
Leveraging data to its highest and best potential in pursuit of more adaptable master plans is no small task, but it is an important goal that many academic institutions are now prioritizing. It is important to remember that data analysis without context rarely results in an effective solution. The human factor must always be considered, balancing the science of analysis with the art of design thinking. A valuable master planning process will leverage both art and science as part of a broader strategy to generate effective solutions that would not be developed otherwise.
By Joel Yow, Assoc. AIA EDAC
Joel Yow is a computational design principal at HDR, specializing in the connection between data and design.